Junior Recital: Bob Wright, jazz trumpet

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Music in Performance. Mr. Wright studies trumpet with Douglas Lindsey.https://digitalcommons.kennesaw.edu/musicprograms/1253/thumbnail.jp

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.publishedVersio

Emotional eating in adults:The role of socio-demographics, lifestyle behaviors, and self-regulation- Findings from a US national study

Background: Emotional eating, the tendency to overeat in response to negative emotions, has been linked to weight gain. However, scant evidence exists examining the prevalence and correlates of emotional eating among large samples of adults in the United States (U.S.). Hence, we examine the relationship among individual and socioeconomic factors, health behaviors, and self-regulation with emotional eating patterns among U.S. adults. Methods: Cross-sectional analysis of 5863 Family Health Habits Survey participants. Multivariable, ordered, logistic regression was employed to examine the relationship between the frequency of the desire to eat when emotionally upset (never, rarely, sometimes, often, and very often) and the independent variables. Results: Analysis reveals that 20.5% of the sample tended to emotionally eat often or very often. Being female, non-Hispanic White, and of younger age were all related to a higher likelihood of emotional eating. Additionally, inability to delay gratification (impatience) was related to an 18% increased likelihood (95% confidence interval (CI) 1.05–1.33) for emotional eating. Finally, emotional eating was significantly related to more frequent fast-food consumption. Conclusions: Program planners might need to develop targeted interventions aimed at enhancing emotional regulation skills while addressing these less healthful behaviors (e.g., fast-food intake) with the goal of obesity and chronic disease prevention

Quality improvement in general practice: what do GPs and practice managers think? Results from a nationally representative survey of UK GPs and practice managers.

BACKGROUND: This paper presents the results of the first UK-wide survey of National Health Service (NHS) general practitioners (GPs) and practice managers (PMs) designed to explore the service improvement activities being undertaken in practices, and the factors that facilitated or obstructed that work. The research was prompted by growing policy and professional interest in the quality of general practice and its improvement. The analysis compares GP and PM involvement in, and experience of, quality improvement activities. METHODS: This was a mixed-method study comprising 26 semistructured interviews, a focus group and two surveys. The qualitative data supported the design of the surveys, which were sent to all 46 238 GPs on the Royal College of General Practitioners (RCGP) database and the PM at every practice across the UK (n=9153) in July 2017. RESULTS: Responses from 2377 GPs and 1424 PMs were received and were broadly representative of each group. Ninety-nine per cent reported having planned or undertaken improvement activities in the previous 12 months. The most frequent related to prescribing and access. Key facilitators of improvement included 'good clinical leadership'. The two main barriers were 'too many demands from external stakeholders' and a lack of protected time. Audit and significant event audit were the most common improvement tools used, but respondents were interested in training on other quality improvement tools. CONCLUSION: GPs and PMs are interested in improving service quality. As such, the new quality improvement domain in the Quality and Outcomes Framework used in the payment of practices is likely to be relatively easily accepted by GPs in England. However, if improving quality is to become routine work for practices, it will be important for the NHS in the four UK countries to work with practices to mitigate some of the barriers that they face, in particular the lack of protected time

Improving the quality of general practice services in the UK: Surveying the activities of GPs and practice managers (abstract & commentary).

Improving the quality of general practice services has risen in priority as primary care has taken on an increasingly important role in National Health Service policy. In 2015 the Royal College of General Practitioners issued a guide to quality improvement (RCGP, 2015) and a new on-line resource in early 2017 called Quality Now (http://www.rcgp.org.uk/clinical-and-research/our-programmes/quality-improvement.aspx). However, there has been little research on the levers for improvement and a recent King’s Fund report (The King’s Fund, 2011) was unable to establish the nature and quantity of quality improvement work happening in practices. This is the first study of both General Practitioners and practice managers, across the UK, which looks at the range of improvement work going on, what motivates it, who is involved in this work and the barriers and facilitators practices face. It is important to understand these issues in order to focus training and better disseminate good practice and innovation across different countries

Comparing data quality from personal computers and mobile devices in an online survey among professionals

It is increasingly common for respondents to complete web surveys using mobile devices (smartphones and tablets) rather than personal computers/laptops (PCs). Evidence of the impact of the use of mobile devices on response and data quality shows mixed results and is only available for general population surveys. We looked at response quality for a work-related survey in the UK among general practitioners (GPs). GPs were sent email invitations to complete a web survey and half (55%) completed it on a mobile device. While GPs using a mobile device were less likely to complete the full questionnaire than those using a PC, we found no differences in data quality between mobile and PC users, except for PC users being more likely to respond to open-ended questions

Jazz Combos

Kennesaw State University of Music presents Jazz Combos featuring 2080 Combo, Brooker Combo and Morgan Combo.https://digitalcommons.kennesaw.edu/musicprograms/1352/thumbnail.jp

Lessons From SEED: A National Demonstration of Child Development Accounts

Lessons From SEED: A National Demonstration of Child Development Account

Jazz Combos

Kennesaw State University School of Music presents Jazz Combos.https://digitalcommons.kennesaw.edu/musicprograms/1420/thumbnail.jp