The Influence of Parenting and Acculturative Stress on Parental Feeding Style and Pediatric Obesity for Latino Families

Title from PDF of title page, viewed on June 29, 2015Dissertation advisor: Chris BrownVitaIncludes bibliographic references (pages 126-143)Thesis (Ph.D.)--School of Education. University of Missouri--Kansas City, 2015Pediatric obesity has become an epidemic in the United States. Previous research has shown that parenting factors related to stress and parental feeding style impact child BMI, and that Latino families are especially at risk for pediatric obesity and stress. The goal of the current study was to evaluate the effects of parenting and acculturative stress on the parental feeding styles of Latino parents. Parental feeding styles were then examined in relation to child BMI. Latino parents of children between the ages of 2 and 8 (N = 124) completed a survey on parenting stress, parental feeding styles, parent BMI, and demographics. Child BMI scores were collected as outcome variables. Children were predominantly male (52.4%), about 6 years old (M age in months = 59.02, SD = 23.82), and had an average BMI z-score of 0.77 (SD = 1.14). There were several important significant results found by the current study. A demanding parental feeding style was associated with lower child BMI zscores, r = -.179, p < .05. There was a trend finding that parents with an authoritative feeding style endorsed less parenting stress than parents who endorsed other feeding styles, F(3, 120) = 2.21, p = .09. Parents with uninvolved feeding style had significantly higher BMIs than parents with authoritarian feeding style, F(3, 69) = 3.38, p < .05. Parent BMI was positively associated with child BMI z-score, r = .273, p < .05. Finally, parents who did not think weight was a health concern for their children actually had children who were more overweight, F(2,111) = 3.18, p < .05. Findings from the current study can be used to inform healthcare practitioners of the need to use culturally sensitive interventions that consider parents’ stress and health experiences. Future research is warranted in the area of ethnic variations and cultural misperceptions about obesity and how it is a health epidemic.Introduction -- Review of literature -- Methodology -- Results -- Discussion -- Appendix A. Measures used in current stud

Machine learning and quantum optics : an approach to improve OCT capabilities.

In machine learning, a neural network is trained using datasets to provide a prediction of a pre-defined parameter. Due to its advanced image analysis capabilities, machine learning was quickly embraced by the optics and photonics community, providing solutions to decades-long problems or opening paths to achieving completely new quality or performance capabilities. Optical Coherence Tomography (OCT) is a light-based method able to provide highquality images of the inside of semi-transparent objects, such as eyes. Originally proposed three decades ago, it is now in all ophthalmologists’ offices, allowing eye disease diagnoses. OCT reaches beyond object visualisation, allowing functional imaging, e.g. quantifying blood flows or helping assess the stiffness of objects. Due to this incredible versatility, OCT is continuously researched to provide even better image quality and newer and broader functional capabilities. To be able to push OCT forward, scientists keep turning to other science disciplines. Using quantum optics concepts, quantum OCT was created, which provides two very soughtafter advancements: a twofold resolution increase and cancellation of resolution-degrading dispersion effects. In another method called quantum-mimic OCT, the quantum OCT signal is mimicked with a computer algorithm and a traditional OCT signal as an input, allowing to obtain the majority of quantum OCT features in a fast and robust way. Unfortunately, the advantages of these two attractive methods are buried in artefacts, unwanted elements in the image that completely obscure it. The thesis aims to extend the imaging and functional capabilities of OCT through a novel application of machine learning in quantum OCT and quantum-mimic OCT. Firstly, neural network models are built and trained to efficiently remove the artefacts in quantum OCT, therefore enabling access to quantum OCT’s beneficial features. Secondly, neural network models are created for quantum-mimic OCT to infer the distribution of chromatic dispersion within the object and, consequently, provide the first steps toward functional imaging based on dispersion values. In both cases, an approach hardly seen in machine learning is taken: the input signals are pre-processed raw signals, rather than final 2D images, and the training is carried out on entirely computer-generated, model signals rather than experimental data. Most interestingly, the core of the approach is the presence of artefacts. A pre-processing algorithm elucidates them, and then during training, the neural networks learn the unique relationship between their shape and positioning, and the imaged object characteristics: the structure in the case of quantum OCT and the dispersion values in the case of quantum-mimic OCT. The test results on both computer-generated and experimental data show very good prediction fidelity

Ab-initio investigation of phonon dispersion and anomalies in palladium

In recent years, palladium has proven to be a crucial component for devices ranging from nanotube field effect transistors to advanced hydrogen storage devices. In this work, I examine the phonon dispersion of fcc Pd using first principle calculations based on density functional perturbation theory. While several groups in the past have studied the acoustic properties of palladium, this is the first study to reproduce the phonon dispersion and associated anomaly with high accuracy and no adjustable parameters. In particular, I focus on the Kohn anomaly in the [110] direction.Comment: 19 pages, preprint format, 7 figures, added new figures and discussio

Towards retrieving dispersion profiles using quantum-mimic Optical Coherence Tomography and Machine Learnin

Artefacts in quantum-mimic Optical Coherence Tomography are considered detrimental because they scramble the images even for the simplest objects. They are a side effect of autocorrelation which is used in the quantum entanglement mimicking algorithm behind this method. Interestingly, the autocorrelation imprints certain characteristics onto an artefact - it makes its shape and characteristics depend on the amount of dispersion exhibited by the layer that artefact corresponds to. This unique relationship between the artefact and the layer's dispersion can be used to determine Group Velocity Dispersion (GVD) values of object layers and, based on them, build a dispersion-contrasted depth profile. The retrieval of GVD profiles is achieved via Machine Learning. During training, a neural network learns the relationship between GVD and the artefacts' shape and characteristics, and consequently, it is able to provide a good qualitative representation of object's dispersion profile for never-seen-before data: computer-generated single dispersive layers and experimental pieces of glass.Comment: 11 pages, 5 figure

Monocyte-mediated Tumoricidal Activity via the Tumor Necrosis Factor–related Cytokine, TRAIL

TRAIL (tumor necrosis factor [TNF]-related apoptosis-inducing ligand) is a molecule that displays potent antitumor activity against selected targets. The results presented here demonstrate that human monocytes rapidly express TRAIL, but not Fas ligand or TNF, after activation with interferon (IFN)-γ or -α and acquire the ability to kill tumor cells. Monocyte-mediated tumor cell apoptosis was TRAIL specific, as it could be inhibited with soluble TRAIL receptor. Moreover, IFN stimulation caused a concomitant loss of TRAIL receptor 2 expression, which coincides with monocyte acquisition of resistance to TRAIL-mediated apoptosis. These results define a novel mechanism of monocyte-induced cell cytotoxicity that requires TRAIL, and suggest that TRAIL is a key effector molecule in antitumor activity in vivo

The STAR Silicon Strip Detector (SSD)

The STAR Silicon Strip Detector (SSD) completes the three layers of the Silicon Vertex Tracker (SVT) to make an inner tracking system located inside the Time Projection Chamber (TPC). This additional fourth layer provides two dimensional hit position and energy loss measurements for charged particles, improving the extrapolation of TPC tracks through SVT hits. To match the high multiplicity of central Au+Au collisions at RHIC the double sided silicon strip technology was chosen which makes the SSD a half million channels detector. Dedicated electronics have been designed for both readout and control. Also a novel technique of bonding, the Tape Automated Bonding (TAB), was used to fullfill the large number of bounds to be done. All aspects of the SSD are shortly described here and test performances of produced detection modules as well as simulated results on hit reconstruction are given.Comment: 11 pages, 8 figures, 1 tabl

Urgent pericardiocentesis is more frequently needed after left circumflex coronary artery perforation

Background: Coronary artery perforation (CAP) is a rare but potentially life-threatening complication of percutaneous coronary interventions (PCIs) due to the risk of cardiac tamponade. Strikingly, in contrast to numerous analyses of CAP predictors, only few studies were focused on the predictors of tamponade after PCI, once iatrogenic CAP has occurred. Our aim was to search for clinical and periprocedural characteristics, including the coronary artery involved, associated with the development of acute cardiac tamponade among patients experiencing CAP. Methods: From the medical records of nine centers of invasive cardiology in southern Poland, we retrospectively selected 81 patients (80% with acute myocardial infarction) who had iatrogenic CAP with a visible extravasation jet during angiography (corresponding to type III CAP by the Ellis classification, CAPIII) over a 15-year period (2005&ndash;2019). Clinical, angiographic and periprocedural characteristics were compared between the patients who developed acute cardiac tamponade requiring urgent pericardiocentesis in the cathlab (n = 21) and those with CAPIII and without tamponade (n = 60). Results: CAPIII were situated in the left anterior descending artery (LAD) or its diagonal branches (51%, n = 41), right coronary artery (RCA) (24%, n = 19), left circumflex coronary artery (LCx) (16%, n = 13), its obtuse marginal branches (7%, n = 6) and left main coronary artery (2%, n = 2). Acute cardiac tamponade occurred in 24% (10 of 41), 21% (4 of 19) and 37% (7 of 19) patients who experienced CAPIII in the territory of LAD, RCA and LCx, respectively. There were no significant differences in the need for urgent pericardiocentesis (37%) in patients with CAPIII in LCx territory (i.e., the LCx or its obtuse marginal branches) compared to CAPIII in the remaining coronary arteries (23%) (p = 0.24). However, when CAPIII in the LCx were separated from CAPIII in obtuse marginal branches, urgent pericardiocentesis was more frequently performed in patients with CAPIII in the LCx (54%, 7 of 13) compared to subjects with CAPIII in an artery other than the LCx (21%, 14 of 68) (p = 0.03). The direction of this tendency remained consistent regardless of CAP management: prolonged balloon inflation only (n = 26, 67% vs. 13%, p = 0.08) or balloon inflation with subsequent stent implantation (n = 55, 50% vs. 24%, p = 0.13). Besides LCx involvement, no significant differences in other characteristics were observed between patients according to the need of urgent pericardiocentesis. Conclusions: CAPIII in the LCx appears to lead to a higher risk of acute cardiac tamponade compared to perforations involving other coronary arteries. This association may possibly be linked to distinct features of LCx anatomy and/or well-recognized delays in diagnosis and management of LCx-related acute coronary syndromes