This study is the first to describe age-related changes in a large cohort of patients with Phelan-McDermid syndrome (PMS), also known as 22q13 deletion syndrome. Over a follow-up period of up to 12 years, physical examinations and structured interviews were conducted for 201 individuals diagnosed with PMS, 120 patients had a focused, high-resolution 22q12q13 array CGH, and 92 patients' deletions were assessed for parent-of-origin. 22q13 genomic anomalies include terminal deletions of 22q13 (89 %), terminal deletions and interstitial duplications (9 %), and interstitial deletions (2 %). Considering different age groups, in older patients, behavioral problems tended to subside, developmental abilities improved, and some features such as large or fleshy hands, full or puffy eyelids, hypotonia, lax ligaments, and hyperextensible joints were less frequent. However, the proportion reporting an autism spectrum disorder, seizures, and cellulitis, or presenting with lymphedema or abnormal reflexes increased with age. Some neurologic and dysmorphic features such as speech and developmental delay and macrocephaly correlated with deletion size. Deletion sizes in more recently diagnosed patients tend to be smaller than those diagnosed a decade earlier. Seventy-three percent of de novo deletions were of paternal origin. Seizures were reported three times more often among patients with a de novo deletion of the maternal rather than paternal chromosome 22. This analysis improves the understanding of the clinical presentation and natural history of PMS and can serve as a reference for the prevalence of clinical features in the syndrome.

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Human error has been identified as the primary contributing cause for up to 80% of the accidents in complex, high risk systems such as aviation, oil and gas, mining and healthcare. Many models have been proposed to analyze these incidents and identify their causes, focusing on the human factor. One such safety model is the Human Factors Analysis and Classification System (HFACS), a comprehensive accident investigation and analysis tool which focuses not only on the act of the individual preceding the accident but on other contributing factors in the system as well. Since its development, HFACS has received substantial research attention; however, the literature on its reliability is limited. This study adds to past research by investigating the overall intra-rater and inter-rater reliability of HFACS in addition to the intra-rater and inter-rater reliability for each tier and category. For this investigation, 125 coders with similar HFACS training coded 95 causal factors extracted from actual incident/accident reports from several sectors. The overall intra-rater reliability was evaluated using percent agreement, Krippendorff"s Alpha, and Cohen"s Kappa, while the inter-rater was analyzed using percent agreement, Krippendorff"s Alpha, and Fleiss" Kappa. Because of analytical limitations, only percent agreement and Krippendorff"s Alpha were used for the intra-rater evaluation at the individual tier and category level and Fleiss" Kappa and Krippendorff"s Alpha, for the corresponding inter-rater evaluation. The overall intra-rater and inter-rater results for the tier level and the individual HFACS tiers achieved acceptable reliability levels with respect to all agreement v ACKNOWLEDGMENTS First, I would like to express my sincerest thanks and gratitude to Allah, who is the source of my success for accomplishing this research. I acknowledge the insightful instruction and guidance of my advisors, Dr. Anand Gramopadhye and Dr. Scott Shappell, who have given me continuous support throughout this research. I also thank my committee members Dr. Kurz and Dr. Sharp, for their valuable suggestions for improving the quality of this work. I am especially grateful to Dr. Julia Sharp; her guidance, constructive feedback, and support significantly contributed to the accomplishment of this research. Special thanks also go to Barbara Ramirez, Director of the Class of 1941 Studio for Student Communication, for her technical help and support in editing this research. Much appreciation goes to my family: my parents, Omar and Mohra; my siblings, Sassia, Abdel-Hakim, Eman, Najmeddien, Wafa, and Housameddien; and my mother in law, Aisha. I am blessed that you are my family and am especially grateful for your prayers, thoughtfulness and emotional support. Special gratitude is also extended to my other family members, aunts, uncles, nieces, nephews, and cousins. Finally, I am very grateful to my husband, Ahmed; I could not have done any of this without you.

BackgroundThis paper examines the individual factors that influence prevalence rates of canine heartworm in the contiguous United States. A data set provided by the Companion Animal Parasite Council, which contains county-by-county results of over nine million heartworm tests conducted during 2011 and 2012, is analyzed for predictive structure. The goal is to identify the factors that are important in predicting high canine heartworm prevalence rates.MethodsThe factors considered in this study are those envisioned to impact whether a dog is likely to have heartworm. The factors include climate conditions (annual temperature, precipitation, and relative humidity), socio-economic conditions (population density, household income), local topography (surface water and forestation coverage, elevation), and vector presence (several mosquito species). A baseline heartworm prevalence map is constructed using estimated proportions of positive tests in each county of the United States. A smoothing algorithm is employed to remove localized small-scale variation and highlight large-scale structures of the prevalence rates. Logistic regression is used to identify significant factors for predicting heartworm prevalence.ResultsAll of the examined factors have power in predicting heartworm prevalence, including median household income, annual temperature, county elevation, and presence of the mosquitoes Aedes trivittatus, Aedes sierrensis and Culex quinquefasciatus. Interactions among factors also exist.ConclusionsThe factors identified are significant in predicting heartworm prevalence. The factor list is likely incomplete due to data deficiencies. For example, coyotes and feral dogs are known reservoirs of heartworm infection. Unfortunately, no complete data of their populations were available. The regression model considered is currently being explored to forecast future values of heartworm prevalence.