Reentry and the Community: Assessing Community-Based Responses to the Challenge of Prisoner Reentry

Released prison inmates face significant challenges in their return to society, which is compounded by the fact most settle in neighborhoods with high rates of poverty and joblessness, and few opportunities. This has led to a cycle of recidivism for many, and can have a detrimental impact on already troubled communities. But can the social capital and other assets in these communities be organized to help ease this transition for ex-inmates? Can community engagement be stimulated and structured to create a positive feedback loop where ex-offenders are welcomed and receive assistance while the community also benefits, with released inmates pursuing positive outlets rather than turning to crime and other anti-social behavior? This thesis attempts to answer these questions by evaluating several community-based responses to reentry in the United States. Research indicates that strategies such as mentorship, restorative justice and civic engagement can have a positive impact but it is doubtful they could have a widespread affect considering the scale of the challenge (more than 600,000 inmates released in the U.S. annually). This thesis recommends a focus on two other strategies, family reunification and community corrections centers, and the development of a programmatic model called Community Transition Panels. By adopting such measures, this thesis concludes that even the most troubled communities can help ex-inmates with this transition and also, therefore, help themselves

Selected high-response formulations.

<p>Selected high-response formulations.</p

Aqueous phase library.

<p>Aqueous phase library.</p

Lipid library.

<p>Lipid library.</p

Change over time in lipid components in recipes tested.

<p>The representation of each lipid, computed as a percentage of recipes that have each particular component, for each generation of Evo-DoE is shown for seven successive generations. For generation 2–7, only model-based recipes are considered. A) The PG lipid group; B) the negatively charged lipid group. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0008546#s4" target="_blank">Materials and Methods</a> for abbreviations and groupings.</p

Illustration of the iterated high throughput experimental process.

<p>Note the predictive modeling procedure in the loop.</p

Representation of a 3-dimensional section of experimental space.

<p>For each PG-type lipid, shown in bold, the horizontal section lists the lipids from the group with a net negative charge and the vertical section lists the reagents in the aqueous phase and their corresponding pH values. Response levels (the UV/Vis absorbance of Amphotericin B associated with the formulation): dark grey, >0.20; medium grey, 0.15–0.20; light grey, 0.10–0.15; white, <0.10; Blank cells, not determined. For abbreviations, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0008546#s4" target="_blank">Materials and Methods</a>.</p

Rank order of all tested formulations found with Evo-DoE vs. the standard recipe.

<p>Error bars on the best new recipe were taken from three repeats performed on the same day (space repeats, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0008546#s4" target="_blank">Materials and Methods</a>). The error bars from the standard were taken from 75 total repeats performed over the course of the experiment.</p

Benchmarking the predictive ability of the Liability Antibody Profiler (LAP) flags to filter innocuous liabilities.

The y axis shows the distribution of the percentage of each liability undergoing modification. In most cases, liabilities are associated with one or more LAP flags. We only show the germline and therapeutic flag individual distributions as no liabilities in the Lu et al. Isomerization/Deamidation dataset and Liability dataset were detected to be buried. “CST” stands for Clinical Stage Therapeutics.</p

Per-dataset prevalence of sequence liabilities for five open-source databases: Genbank, literature, NGS, patents, and therapeutics.

Please note that the NGS dataset and therapeutics were paired, so the number of liabilities can not be directly compared to the single-chain datasets. Genbank, patents, and literature datasets contained unpaired heavy and light sequences. In the top portion (sequences) counts are given as a percentage of the total number of sequences in a dataset. In the lower portion (liabilities), the total count of liabilities in the dataset is given. In each case, we show the number of remaining sequences of liabilities or total liabilities after applying individual flags or their combinations.</p