Governor’s Office of Drug Control Policy Performance Report, FY2007

Agency Performance Report

On Ito's formula for elliptic diffusion processes

Bardina and Jolis [Stochastic process. Appl. 69 (1997) 83-109] prove an extension of Ito's formula for F(Xt, t), where F(x, t) has a locally square-integrable derivative in x that satisfies a mild continuity condition in t and X is a one-dimensional diffusion process such that the law of Xt has a density satisfying certain properties. This formula was expressed using quadratic covariation. Following the ideas of Eisenbaum [Potential Anal. 13 (2000) 303-328] concerning Brownian motion, we show that one can re-express this formula using integration over space and time with respect to local times in place of quadratic covariation. We also show that when the function F has a locally integrable derivative in t, we can avoid the mild continuity condition in t for the derivative of F in x.

Governor’s Office of Drug Control Policy Performance Report, FY2008

Agency Performance Report

Governor’s Office of Drug Control Policy Performance Report, FY2009

Agency Performance Report

Governor’s Office of Drug Control Policy Performance Report, FY2010

Agency Performance Report

Governor’s Office of Drug Control Policy Performance Report, FY2011

Agency Performance Report

Governor’s Office of Drug Control Policy Performance Report, FY2012

Agency Performance Report

Governor’s Office of Drug Control Policy Performance Plan, FY 2013

Agency Performance Plan, Governor’s Office of Drug Control Policy

The role of presidential advisory systems in US foreign policy-making : the case of the National Security Council and Vietnam, 1953-1961

Doctoral dissertation, University of Joensuu

Governor’s Office of Drug Control Policy Performance Plan, FY2014

Agency Performance Plan, Governor’s Office of Drug Control Policy

Governor's Office of Drug Control Policy Five-Year Strategic Plan July 1, 2010-June 30, 2015

The Office of the Drug Policy Coordinator is established in Chapter 80E of the Code of Iowa. The Coordinator directs the Governor’s Office of Drug Control Policy; coordinates and monitors all statewide counter-drug efforts, substance abuse treatment grants and programs, and substance abuse prevention and education programs; and engages in other related activities involving the Departments of public safety, corrections, education, public health, and human services. The coordinator assists in the development of local and community strategies to fight substance abuse, including local law enforcement, education, and treatment activities. The Drug Policy Coordinator serves as chairperson to the Drug Policy Advisory Council. The council includes the directors of the departments of corrections, education, public health, public safety, human services, division of criminal and juvenile justice planning, and human rights. The Council also consists of a prosecuting attorney, substance abuse treatment specialist, substance abuse prevention specialist, substance abuse treatment program director, judge, and one representative each from the Iowa Association of Chiefs of Police and Peace Officers, the Iowa State Police Association, and the Iowa State Sheriff’s and Deputies’ Association. Council members are appointed by the Governor and confirmed by the Senate. The council makes policy recommendations related to substance abuse education, prevention, and treatment, and drug enforcement. The Council and the Coordinator oversee the development and implementation of a comprehensive State of Iowa Drug Control Strategy. The Office of Drug Control Policy administers federal grant programs to improve the criminal justice system by supporting drug enforcement, substance abuse prevention and offender treatment programs across the state. The ODCP prepares and submits the Iowa Drug and Violent Crime Control Strategy to the U.S. Department of Justice, with recommendations from the Drug Policy Advisory Council. The ODCP also provides program and fiscal technical assistance to state and local agencies, as well as program evaluation and grants management.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.