Speech Communication

Contains table of contents for Part IV, table of contents for Section 1 and reports on five research projects.Apple Computer, Inc.C.J. Lebel FellowshipNational Institutes of Health (Grant T32-NS07040)National Institutes of Health (Grant R01-NS04332)National Institutes of Health (Grant R01-NS21183)National Institutes of Health (Grant P01-NS23734)U.S. Navy / Naval Electronic Systems Command (Contract N00039-85-C-0254)U.S. Navy - Office of Naval Research (Contract N00014-82-K-0727

Speech Communication

Contains reports on five research projects.C.J. Lebel FellowshipNational Institutes of Health (Grant 5 T32 NSO7040)National Institutes of Health (Grant 5 R01 NS04332)National Institutes of Health (Grant 5 R01 NS21183)National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 1 PO1-NS23734)National Science Foundation (Grant BNS 8418733)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0254)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0341)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0290)National Institutes of Health (Grant RO1-NS21183), subcontract with Boston UniversityNational Institutes of Health (Grant 1 PO1-NS23734), subcontract with the Massachusetts Eye and Ear Infirmar

Tobacco smoke exposure as a risk factor for human papillomavirus infections in women 18-26 years old in the United States.

BackgroundAlthough tobacco smoke has been associated with many infections, little is known of its association with human papillomavirus (HPV) infections among young adult women. The aim of the study was to explore the association of tobacco smoke exposure on HPV infections in young adult women in the United States. It was hypothesized that tobacco smoke exposure (both active and passive) as objectively measured by cotinine levels was associated with increased HPV infection in a national sample of 18 and 26 year-old women in the United States.Study methods and findingsThe NHANES 2007-2012 dataset was used in the analyses. A national representative sample of women 18 to 26 year old (N = 1,414) was included in the study. Infection with any HPV was determined by PCR while tobacco smoke exposure was determined by measuring serum cotinine levels. Women with cotinine levels = 0.05 were considered as exposed. Exposed women were further categorized as passive smokers (cotinine levels 0.05- 10ng/mL). Data were analyzed by descriptive statistics and multiple logistic regression analysis. Exposed women (passive smokers with cotinine levels > = 0.05ng/mL-10ng/mL) were almost 2 times (64% vs 35%) more likely to be infected with any HPV type than their unexposed counterparts (cotinine levels = 10 ng/mL) were more than twice more likely (75%) to be infected with the virus than the unexposed counterparts. The relationship held true even after controlling for various socio-demographics. Indeed in the multiple regression analyses controlling for the various confounders, compared to their unexposed counterparts, women exposed to second hand smoke were more than twice more likely to have HPV infections (OR: 2.45; 95% C.I = 1.34-4.48). When compared to their unexposed counterparts, actively smoking women were more than 3.5 times more likely to be infected with HPV (OR = 3.56; 95% CI 1.23-10.30). Finally, a strong dose-response relationship was further demonstrated with increasing risk of HPV with each quartile of cotinine levels even after controlling for various confounders. The risk of HPV was lowest in the lowest quartile (Referent OR = 1) and increased steadily with each quartile of cotinine levels until the risk was highest among women with cotinine levels in the 4th quartile (OR = 4.16; 95% C.I. = 1.36-12.67).ConclusionBoth passive and active tobacco smoking were strongly associated with any HPV infection in 18 to 26 year old young women with a significant dose-response relationship. Future studies should explore the effect of tobacco smoke exposure among younger women less than 18 years of age

A Brief Analysis of Suicide Methods and Trends in Virginia from 2003 to 2012

Background. The objective is to analyze and compare Virginia suicide data from 2003 to 2012 to US suicide data. Methods. Suicide trends by method, age, gender, and race were obtained from Virginia’s Office of the Chief Medical Examiner’s annual reports. Results. Similar to US suicide rates, suicide rates in Virginia increased between 2003 and 2012 from 10.9/100,000 people to 12.9/100,000 people. The most common methods were firearm, asphyxia, and intentional drug overdose, respectively. The increase in asphyxia (r=0.77, P≤0.01) and decrease in CO poisoning (r=-0.89, P≤0.01) were significant. Unlike national trends, intentional drug overdoses decreased (r=-0.55, P=0.10). Handgun suicides increased (r=0.61, P=0.06) and are the most common method of firearm suicide. Hanging was the most common method of asphyxia. Helium suicides also increased (r=0.75, P=0.05). Middle age females and males comprise the largest percentage of suicide. Unlike national data, the increase in middle age male suicides occurred only in the 55–64-year-old age group (r=0.79, P≤0.01) and decreased in the 35–44-year-old age group (r=-0.60, P=0.07) and 10–14-year-old age group (r=-0.73, P=0.02). Suicide in all female age ranges remained stable. Caucasians represent the highest percentage of suicide. Conclusion. There has been a rise in suicide in Virginia and suicide rates and trends have closely resembled the national average albeit some differences. Suicide prevention needs to be enhanced

Behavioral and neurocognitive factors distinguishing post-traumatic stress comorbidity in substance use disorders

Abstract Significant trauma histories and post-traumatic stress disorder (PTSD) are common in persons with substance use disorders (SUD) and often associate with increased SUD severity and poorer response to SUD treatment. As such, this sub-population has been associated with unique risk factors and treatment needs. Understanding the distinct etiological profile of persons with co-occurring SUD and PTSD is therefore crucial for advancing our knowledge of underlying mechanisms and the development of precision treatments. To this end, we employed supervised machine learning algorithms to interrogate the responses of 160 participants with SUD on the multidimensional NIDA Phenotyping Assessment Battery. Significant PTSD symptomatology was correctly predicted in 75% of participants (sensitivity: 80%; specificity: 72.22%) using a classification-based model based on anxiety and depressive symptoms, perseverative thinking styles, and interoceptive awareness. A regression-based machine learning model also utilized similar predictors, but failed to accurately predict severity of PTSD symptoms. These data indicate that even in a population already characterized by elevated negative affect (individuals with SUD), especially severe negative affect was predictive of PTSD symptomatology. In a follow-up analysis of a subset of 102 participants who also completed neurocognitive tasks, comorbidity status was correctly predicted in 86.67% of participants (sensitivity: 91.67%; specificity: 66.67%) based on depressive symptoms and fear-related attentional bias. However, a regression-based analysis did not identify fear-related attentional bias as a splitting factor, but instead split and categorized the sample based on indices of aggression, metacognition, distress tolerance, and interoceptive awareness. These data indicate that within a population of individuals with SUD, aberrations in tolerating and regulating aversive internal experiences may also characterize those with significant trauma histories, akin to findings in persons with anxiety without SUD. The results also highlight the need for further research on PTSD-SUD comorbidity that includes additional comparison groups (i.e., persons with only PTSD), captures additional comorbid diagnoses that may influence the PTSD-SUD relationship, examines additional types of SUDs (e.g., alcohol use disorder), and differentiates between subtypes of PTSD

Resting-state directional connectivity and anxiety and depression symptoms in adult cannabis users

BACKGROUND: Anxiety and depression symptoms are common among cannabis users and could be a risk factor for cannabis use (CU) disorder. Thus, it is critical to understand the neuronal circuits underlying the associations between CU and these symptoms. Alterations in resting-state functional connectivity within and/or between the default mode network and salience network have been reported in CU, anxiety, and depressive disorders and thus could be a mechanism underlying the associations between CU disorder and anxiety/depression symptoms. METHODS: Using resting-state functional magnetic resonance imaging, effective connectivities (ECs) among 9 major nodes from the default mode network and salience network were measured using dynamic causal modeling in 2 datasets: the Human Connectome Project (28 CU participants and 28 matched non-drug-using control participants) and a local CU study (21 CU participants and 21 matched non-drug-using control participants) in separate and parallel analyses. RESULTS: Relative to the control participants, right amygdala to left amygdala, anterior cingulate cortex to left amygdala, and medial prefrontal cortex to right insula ECs were greater, and left insula to left amygdala EC was smaller in the CU group. Each of these ECs showed a reliable linear relationship with at least one of the anxiety/depression measures. Most findings on the right amygdala to left amygdala EC were common to both datasets. CONCLUSIONS: Right amygdala to left amygdala and anterior cingulate cortex to left amygdala ECs may be related to the close associations between CU and anxiety/depression symptoms. The findings on the medial prefrontal cortex to right insula and left insula to left amygdala ECs may reflect a compensatory mechanism