A Local Correlation Score to Monitor Sensor Drift of Real-Time Environmental Data

2012 S.C. Water Resources Conference - Exploring Opportunities for Collaborative Water Research, Policy and Managemen

Real-Time Quality Control (QC) Processing, Notification, and Visualization Services, Supporting Data Management of the Intelligent River

2010 S.C. Water Resources Conferences - Science and Policy Challenges for a Sustainable Futur

Self-expansion is positively associated with Fitbit-measured daily steps across 4-weeks.

The growth of the self-concept through increasing perspectives, identities, resources, and efficacy is known as self-expansion and typically involves novelty, challenge, interest, and/or excitement. Self-expansion is positively associated with health factors including self-reported physical activity (PA). This study is the first to investigate self-expansion and daily PA, and with a PA monitor. Fifty community participants completed baseline questionnaires, wore a Fitbit One and completed daily self-report questionnaires for 28 days, and completed follow-up questionnaires. Daily surveys included questions about both general and PA-specific self-expansion. Across the 4 weeks, steps taken was positively correlated with both general (all maximum likelihood r = 0.17) and PA-specific self-expansion (maximum likelihood rs of 0.15 and 0.16), and PA-specific self-expansion was positively correlated (maximum likelihood rs of 0.38 and 0.50) with aerobic activity. Future research should investigate this relationship in a larger more diverse sample and test whether PA-specific self-expansion can be utilized as an acceptable, feasible, and effective intervention to increase daily steps and other forms of PA

Data Quality Assessment Using a Sliding Window Cumulative Sum Control Chart

2012 S.C. Water Resources Conference - Exploring Opportunities for Collaborative Water Research, Policy and Managemen

R Workshops for Researchers: A Successful Partnership Between a Library and a Statistical Consulting Laboratory

Colorado State University Libraries has partnered with the university’s Franklin A. Graybill Statistics and Data Science Laboratory to jointly offer workshops teaching R coding for academic research, while employing Statistics graduate students as instructors. This partnership has capitalized on the strengths of both units and has provided an opportunity to build campus relationships. The development, assessment, challenges, benefits, and evolution of this fruitful and ongoing collaboration are described with the intent that this information may assist in the creation or enhancement of services at other institutions

Immune, Autonomic, and Endocrine Dysregulation in Autism and Ehlers-Danlos Syndrome/Hypermobility Spectrum Disorders Versus Unaffected Controls

Background: A growing body of literature suggests etiological overlap between Ehlers-Danlos syndrome (EDS)/hypermobility spectrum disorders (HSD) and some cases of autism, although this relationship is poorly delineated. In addition, immune, autonomic, and endocrine dysregulation are reported in both conditions and may be relevant to their respective etiologies. Aims: To study symptom overlap in these two comorbid spectrum conditions. Methods and Procedures: We surveyed 702 adults aged 25+ years on a variety of EDS/HSD-related health topics, comparing individuals with EDS/HSD, autism, and unaffected controls. Outcomes and Results: The autism group reported similar though less severe symptomology as the EDS/HSD group, especially in areas of immune/autonomic/endocrine dysregulation, connective tissue abnormalities (i.e., skin, bruising/bleeding), and chronic pain. EDS/HSD mothers with autistic children reported more immune symptoms than EDS/HSD mothers without, suggesting the maternal immune system could play a heritable role in these conditions (p = 0.0119). Conclusions and Implications: These data suggest that EDS/HSD and autism share aspects of immune/autonomic/endocrine dysregulation, pain, and some tissue fragility, which is typically more severe in the former. This overlap, as well as documented comorbidity, suggests some forms of autism may be hereditary connective tissue disorders (HCTD)

The impact of participant mental health on attendance and engagement in a trial of behavioural weight management programmes: secondary analysis of the WRAP randomised controlled trial.

BACKGROUND: Low attendance and engagement in behavioural weight management trials are common. Mental health may play an important role, however previous research exploring this association is limited with inconsistent findings. We aimed to investigate whether mental health was associated with attendance and engagement in a trial of behavioural weight management programmes. METHODS: This is a secondary data analysis of the Weight loss referrals for adults in primary care (WRAP) trial, which randomised 1267 adults with overweight or obesity to brief intervention, WW (formerly Weight Watchers) for 12-weeks, or WW for 52-weeks. We used regression analyses to assess the association of baseline mental health (depression and anxiety (by Hospital Anxiety and Depression Scale), quality of life (by EQ5D), satisfaction with life (by Satisfaction with Life Questionnaire)) with programme attendance and engagement in WW groups, and trial attendance in all randomised groups. RESULTS: Every one unit of baseline depression score was associated with a 1% relative reduction in rate of WW session attendance in the first 12 weeks (Incidence rate ratio [IRR] 0.99; 95% CI 0.98, 0.999). Higher baseline anxiety was associated with 4% lower odds to report high engagement with WW digital tools (Odds ratio [OR] 0.96; 95% CI 0.94, 0.99). Every one unit of global quality of life was associated with 69% lower odds of reporting high engagement with the WW mobile app (OR 0.31; 95% CI 0.15, 0.64). Greater symptoms of depression and anxiety and lower satisfaction with life at baseline were consistently associated with lower odds of attending study visits at 3-, 12-, 24-, and 60-months. CONCLUSIONS: Participants were less likely to attend programme sessions, engage with resources, and attend study assessments when reporting poorer baseline mental health. Differences in attendance and engagement were small, however changes may still have a meaningful effect on programme effectiveness and trial completion. Future research should investigate strategies to maximise attendance and engagement in those reporting poorer mental health. TRIAL REGISTRATION: The original trial ( ISRCTN82857232 ) and five year follow up ( ISRCTN64986150 ) were prospectively registered with Current Controlled Trials on 15/10/2012 and 01/02/2018.The WRAP trial was funded by the National Prevention Research Initiative through research grant MR/J000493. The intervention was provided by WW (formerly Weight Watchers) at no cost via an MRC Industrial Collaboration Award. Five year follow up of the WRAP trial was funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (RP-PG-0216-20010). RAJ, ALA, SJG, and SJS are supported by the Medical Research Council (MRC) (Grant MC_UU_00006/6). The University of Cambridge has received salary support in respect of SJG from the National Health Service in the East of England through the Clinical Academic Reserve. All funding bodies had no role in the design of the study and collection, analysis and interpretation of the data, and in the writing of the manuscript

Development of three new multidimensional measures to assess household food insecurity resilience in the United States

IntroductionThis study aimed to develop and test novel self-administered measures (Absorptive capacity, Adaptive capacity, and Transformative capacity) of three aspects of a household's resilience to financial shocks (e.g., job loss) that can increase food insecurity risk.MethodsMeasures were piloted in a convenience sample of households at risk for food insecurity in the United States. The survey included the new measures, validation variables (financial shock, household food security, general health, personal resilience to challenges, and financial wellbeing), and demographic questions. Exploratory factor analysis was used to assess dimensionality, internal consistency was assessed [Cronbach's alpha (CA)], and construct validity was assessed (Spearman's correlation). Also, brief screener versions of the full measures were created.ResultsParticipants in the analytic samples (n = 220-394) averaged 44 years old, 67% experienced food insecurity, 47% had a high school diploma or less, 72% were women, and the sample was racially/ethnically diverse. Scores for Absorptive capacity [one factor; CA = 0.70; Mean = 1.32 (SD = 0.54)], Adaptive capacity [three factors; CAs 0.83-0.90; Mean = 2.63 (SD = 0.85)], and Transformative capacity [three factors; CAs 0.87-0.95; Mean = 2.70 (SD = 1.10)] were negatively associated with financial shocks (−0.221 to −0.307) and positively associated with food insecurity (0.310-0.550) general health (0.255-0.320), personal resilience (0.231-0.384), and financial wellbeing (0.401-0.474).DiscussionThese findings are encouraging and support reliability and validity of these new measures within this sample. Following further testing, such as Confirmatory Factor Analysis in future samples, these measures may prove useful for needs assessments, program evaluation, intake screening, and research/surveillance. Widespread adoption in the future may promote a more comprehensive understanding of the food insecurity experience and facilitate development of tailored interventions on upstream causes of food insecurity

The SAMI Galaxy Survey: Shocks and Outflows in a normal star-forming galaxy

We demonstrate the feasibility and potential of using large integral field spectroscopic surveys to investigate the prevalence of galactic-scale outflows in the local Universe. Using integral field data from SAMI and the Wide Field Spectrograph, we study the nature of an isolated disk galaxy, SDSS J090005.05+000446.7 (z = 0.05386). In the integral field datasets, the galaxy presents skewed line profiles changing with position in the galaxy. The skewed line profiles are caused by different kinematic components overlapping in the line-of-sight direction. We perform spectral decomposition to separate the line profiles in each spatial pixel as combinations of (1) a narrow kinematic component consistent with HII regions, (2) a broad kinematic component consistent with shock excitation, and (3) an intermediate component consistent with shock excitation and photoionisation mixing. The three kinematic components have distinctly different velocity fields, velocity dispersions, line ratios, and electron densities. We model the line ratios, velocity dispersions, and electron densities with our MAPPINGS IV shock and photoionisation models, and we reach remarkable agreement between the data and the models. The models demonstrate that the different emission line properties are caused by major galactic outflows that introduce shock excitation in addition to photoionisation by star-forming activities. Interstellar shocks embedded in the outflows shock-excite and compress the gas, causing the elevated line ratios, velocity dispersions, and electron densities observed in the broad kinematic component. We argue from energy considerations that, with the lack of a powerful active galactic nucleus, the outflows are likely to be driven by starburst activities. Our results set a benchmark of the type of analysis that can be achieved by the SAMI Galaxy Survey on large numbers of galaxies.Comment: 17 pages, 15 figures. Accepted to MNRAS. References update

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef