The role of death qualification in venirepersons' evaluations of aggravating and mitigating circumstances in capital trials

Death qualification is a part of voir dire that is unique to capital trials. Unlike all other litigation, capital jurors must affirm their willingness to impose both legal standards (either life in prison or the death penalty). Jurors who assert they are able to do so are deemed “death-qualified” and are eligible for capital jury service: jurors who assert that they are unable to do so are deemed “excludable” or “scrupled” and are barred from hearing a death penalty case. During the penalty phase in capital trials, death-qualified jurors weigh the aggravators (i.e., arguments for death) against the mitigators (i.e., arguments for life) in order to determine the sentence. If the aggravating circumstances outweigh the mitigating circumstances, then the jury is to recommend death; if the mitigating circumstances outweigh the aggravating circumstances, then the jury is to recommend life. The jury is free to weigh each aggravating and mitigating circumstance in any matter they see fit. Previous research has found that death qualification impacts jurors' receptiveness to aggravating and mitigating circumstances (e.g., Luginbuhl & Middendorf, 1988). However, these studies utilized the now-defunct Witherspoon rule and did not include a case scenario for participants to reference. The purpose of this study was to investigate whether death qualification affects jurors' endorsements of aggravating and mitigating circumstances when Witt, rather than Witherspoon, is the legal standard for death qualification. Four hundred and fifty venirepersons from the 11 th Judicial Circuit in Miami, Florida completed a booklet of stimulus materials that contained the following: two death qualification questions; a case scenario that included a summary of the guilt and penalty phases of a capital case; a 26-item measure that required participants to endorse aggravators, nonstatutory mitigators, and statutory mitigators on a 6-point Likert scale; and standard demographic questions. Results indicated that death-qualified venirepersons, when compared to excludables, were more likely to endorse aggravating circumstances. Excludable participants, when compared to death-qualified venirepersons, were more likely to endorse nonstatutory mitigators. There was no significant difference between death-qualified and excludable venirepersons with respect to their endorsement of 6 out of 7 statutory mitigators. It would appear that the Furman v. Georgia (1972) decision to declare the death penalty unconstitutional is frustrated by the Lockhart v. McCree (1986) affirmation of death qualification. ^

Stabilization of troponin I for applications in clinical chemistry and forensic medicine. Cardiac troponin I: A time of death marker

Cardiac troponin I (cTnI) is one of the most useful serum marker test for the determination of myocardial infarction (MI). The first commercial assay of cTnI was released for medical use in the United States and Europe in 1995. It is useful in determining if the source of chest pains, whose etiology may be unknown, is cardiac related. Cardiac TnI is released into the bloodstream following myocardial necrosis (cardiac cell death) as a result of an infarct (heart attack). In this research project the utility of cardiac troponin I as a potential marker for the determination of time of death is investigated. The approach of this research is not to investigate cTnI degradation in serum/plasma, but to investigate the proteolytic breakdown of this protein in heart tissue postmortem. If our hypothesis is correct, cTnI might show a distinctive temporal degradation profile after death. This temporal profile may have potential as a time of death marker in forensic medicine. The field of time of death markers has lagged behind the great advances in technology since the late 1850's. Today medical examiners are using rudimentary time of death markers that offer limited reliability in the medico-legal arena. Cardiac TnI must be stabilized in order to avoid further degradation by proteases in the extraction process. Chemically derivatized magnetic microparticles were covalently linked to anti-cTnI monoclonal antibodies. A charge capture approach was also used to eliminate the antibody from the magnetic microparticles given the negative charge on the microparticles. The magnetic microparticles were used to extract cTnI from heart tissue homogenate for further bio-analysis. Cardiac TnI was eluted from the beads with a buffer and analyzed. This technique exploits banding pattern on sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) followed by a western blot transfer to polyvinylidene fluoride (PVDF) paper for probing with anti-cTnI monoclonal antibodies. Bovine hearts were used as a model to establish the relationship of time of death and concentration/band-pattern given its homology to human cardiac TnI. The final concept feasibility was tested with human heart samples from cadavers with known time of death. ^

An Analytical Workflow for Metagenomic Data and its Application to the Study of Chronic Obstructive Pulmonary Disease (COPD)

Metagenomics is the culture-independent study of genetic material obtained directly from environmental samples. It has become a realistic approach to understanding microbial communities thanks to advances in high-throughput DNA sequencing technologies over the past decade. Current research has shown that different sites of the human body house varied bacterial communities. There is a strong correlation between an individual’s microbial community profile at a given site and disease. Metagenomics is being applied more often as a means of comparing microbial profiles in biomedical studies. The analysis of the data collected using metagenomics can be quite challenging and there exist a plethora of tools for interpreting the results. An automatic analytical workflow for metagenomic analyses has been implemented and tested using synthetic datasets of varying quality. It is able to accurately classify bacteria by taxa and correctly estimate the richness and diversity of each set. The workflow was then applied to the study of the airways microbiome in Chronic Obstructive Pulmonary Disease (COPD). COPD is a progressive lung disease resulting in narrowing of the airways and restricted airflow. Despite being the third leading cause of death in the United States, little is known about the differences in the lung microbial community profiles of healthy individuals and COPD patients. Bronchoalveolar lavage (BAL) samples were collected from COPD patients, active or ex-smokers, and never smokers and sequenced by 454 pyrosequencing. A total of 56 individuals were recruited for the study. Substantial colonization of the lungs was found in all subjects and differentially abundant genera in each group were identified. These discoveries are promising and may further our understanding of how the structure of the lung microbiome is modified as COPD progresses. It is also anticipated that the results will eventually lead to improved treatments for COPD.

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^