Exploring the Correspondence Between General Correctional Programming and Inmate Misconduct Using a Time-Course Framework.
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ABSTRACT:
SUBMITTER: Linning SJ
PROVIDER: S-EPMC8721553 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Research has shown that inmate misconduct is related to a range of demographic factors and experiences with the criminal justice system. Poor mental and physical health has also been associated with inmate misconduct, although no research has examined the relationship between co-occurring conditions and misconduct in prison populations. METHODS:We rely on data from the 2004 Survey of Inmates in State Correctional Facilities (N = 14,499) and use negative binomial regression models to examine the relationship between types of co-occurring mental and physical conditions and misconduct. RESULTS:The results demonstrate that people in prison dealing with concurrent mental and physical health problems are significantly more likely to engage in prison misconduct than healthy incarcerated individuals. After accounting for physical and co-occurring health conditions, mental conditions are not associated with serious misconduct. CONCLUSIONS:Enhancements in prison healthcare may not only improve the general health of those in prison, but also contribute to a decrease in misconduct. Research that examines the relationship between mental health and deviant behavior in and out of prison should consider the multifaceted elements of a person's health, including acute and chronic physical ailments.
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Project description:Objectives: Deep brain stimulation (DBS) programming is based on clinical response testing. Our clinical pilot trial assessed the feasibility of image-guided programing using software depicting the lead location in a patient-specific anatomical model. Methods: Parkinson's disease patients with subthalamic nucleus-DBS were randomly assigned to standard clinical-based programming (CBP) or anatomical-based (imaging-guided) programming (ABP) in an 8-week crossover trial. Programming characteristics and clinical outcomes were evaluated. Results: In 10 patients, both programs led to similar motor symptom control (MDS-UPDRS III) after 4 weeks (medicationOFF/stimulationON; CPB: 18.27 ± 9.23; ABP: 18.37 ± 6.66). Stimulation settings were not significantly different, apart from higher frequency in the baseline program than CBP (p = 0.01) or ABP (p = 0.003). Time spent in a program was not significantly different (CBP: 86.1 ± 29.82%, ABP: 88.6 ± 29.0%). Programing time was significantly shorter (p = 0.039) with ABP (19.78 ± 5.86 min) than CBP (45.22 ± 18.32). Conclusion: Image-guided DBS programming in PD patients drastically reduces programming time without compromising symptom control and patient satisfaction in this small feasibility trial.
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Project description:Studying the time course of gene expression in injured skeletal muscle would help to estimate the timing of injuries. In this study, we investigated large-scale gene expression in incision-injured mouse skeletal muscle by DNA microarray using correspondence analysis (CA). Biceps femoris muscle samples were collected 6, 12, and 24 hours after injury, and RNA was extracted and prepared for microarray analysis. On a 2-dimensional plot by CA, the genes (row score coordinate) located farther from each time series (column score coordinate) had more upregulation at particular times. Each gene was situated in 6 subdivided triangular areas according to the magnitude of the relationship of the fold change (FC) value at each time point compared to the control. In each area, genes for which the ratios of two particular FC values were close to 1 were distributed along the two border lines. There was a tendency for genes whose FC values were almost equal to be distributed near the intersection of these 6 areas. Therefore, the gene marker candidates for estimation of the timing of injuries were detectable according to the location on the CA plot. Moreover, gene sets created by a specific gene and its surrounding genes were composed of genes that showed similar or identical fluctuation patterns to the specific gene. In various analyses on these sets, significant gene ontology term and pathway activity may reflect changes in specific genes. In conclusion, analyses of gene sets based on CA plots is effective for investigation of the time-dependent fluctuation in gene expression after injury.
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Project description:The time course of wound healing or the estimation of wound age is one of the most important research subjects in forensic pathology. In this study, we obtained microarray data of incision injury samples of mouse skeletal muscle at 3 different time points post-injury (6, 12, and 24 h). To visualize the time course fluctuation in gene expression on a plot, and to examine large-scale data using various analytical methods, correspondence analysis (CA) was carried out on microarray data that was converted to a matrix (data type is fold change (FC) values) consisting of data of each time point (columns) by each gene (rows) as variables. Genes were successfully clustered based on the magnitude relationship of FC values of 3 time series with a CA plot, which effectively visualizes the data of time course experiments.
2019-11-19 | GSE140517 | GEO
Project description:The biological processes involved in a drug's mechanisms of action are oftentimes dynamic, complex and difficult to discern. Time-course gene expression data is a rich source of information that can be used to unravel these complex processes, identify biomarkers of drug sensitivity and predict the response to a drug. However, the majority of previous work has not fully utilized this temporal dimension. In these studies, the gene expression data is either considered at one time-point (before the administration of the drug) or two time-points (before and after the administration of the drug). This is clearly inadequate in modeling dynamic gene-drug interactions, especially for applications such as long-term drug therapy. In this work, we present a novel REcursive Prediction (REP) framework for drug response prediction by taking advantage of time-course gene expression data. Our goal is to predict drug response values at every stage of a long-term treatment, given the expression levels of genes collected in the previous time-points. To this end, REP employs a built-in recursive structure that exploits the intrinsic time-course nature of the data and integrates past values of drug responses for subsequent predictions. It also incorporates tensor completion that can not only alleviate the impact of noise and missing data, but also predict unseen gene expression levels (GEXs). These advantages enable REP to estimate drug response at any stage of a given treatment from some GEXs measured in the beginning of the treatment. Extensive experiments on two datasets corresponding to multiple sclerosis patients treated with interferon are included to showcase the effectiveness of REP.
| S-EPMC7573611 | biostudies-literature
Project description:MotivationMetabolomics is the study of the complement of small molecule metabolites in cells, biofluids and tissues. Many metabolomic experiments are designed to compare changes observed over time under two experimental conditions or groups (e.g. a control and drug-treated group) with the goal of identifying discriminatory metabolites or biomarkers that characterize each condition. A common study design consists of repeated measurements taken on each experimental unit thus producing time courses of all metabolites. We describe a statistical framework for estimating time-varying metabolic profiles and their within-group variability and for detecting between-group differences. Specifically, we propose (i) a smoothing splines mixed effects (SME) model that treats each longitudinal measurement as a smooth function of time and (ii) an associated functional test statistic. Statistical significance is assessed by a non-parametric bootstrap procedure.ResultsThe methodology has been extensively evaluated using simulated data and has been applied to real nuclear magnetic resonance spectroscopy data collected in a preclinical toxicology study as part of a larger project lead by the COMET (Consortium for Metabonomic Toxicology). Our findings are compatible with the previously published studies.AvailabilityAn R script is freely available for download at http://www2.imperial.ac.uk/~gmontana/sme.htm.
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Project description:Time course 'omics' experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. 'Omics' technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course 'omics' experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course 'omics' data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course 'omics' studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.
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Project description:During July to November 2012, two botulism outbreaks (12 cases total) occurred in one all-male prison; both were associated with illicitly brewed alcohol (pruno) consumption. Inmate surveys were conducted to evaluate and develop prevention and education strategies. Qualitative surveys with open-ended questions were performed among inmates from rooms where outbreaks occurred to learn about pruno consumption. Quantitative surveys assessed knowledge gained after the outbreaks and preferred information sources. For the quantitative surveys, 250 inmates were randomly selected by bed from across the correctional facility and 164 inmates were interviewed. Only 24% of inmates reported any botulism knowledge before the outbreaks and education outreach, whereas 73% reported knowledge after the outbreaks (p < .01). Preferred information sources included handouts/fliers (52%) and the prison television channel (32%).
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Project description:Plants are continuously exposed to varying environmental conditions; some anticipated diurnal changes in light and temperature and others far less predictable. Key to adaptation to a diurnal environment is the anticipation provided by the circadian clock, which coordinates broad changes in gene expression with a period of about 24 h.Here we present the analysis of RNA sequencing to measure transcript abundance over time in Brachypodium distachyon entrained in photo- and thermocycles then transferred to photocycles or thermocycles alone, or constant light and temperature conditions.
2019-05-19 | E-MTAB-7950 | biostudies-arrayexpress
Project description:MotivationAlthough widely accepted that high-throughput biological data are typically highly noisy, the effects that this uncertainty has upon the conclusions we draw from these data are often overlooked. However, in order to assign any degree of confidence to our conclusions, we must quantify these effects. Bootstrap resampling is one method by which this may be achieved. Here, we present a parametric bootstrapping approach for time-course data, in which Gaussian process regression (GPR) is used to fit a probabilistic model from which replicates may then be drawn. This approach implicitly allows the time dependence of the data to be taken into account, and is applicable to a wide range of problems.ResultsWe apply GPR bootstrapping to two datasets from the literature. In the first example, we show how the approach may be used to investigate the effects of data uncertainty upon the estimation of parameters in an ordinary differential equations (ODE) model of a cell signalling pathway. Although we find that the parameter estimates inferred from the original dataset are relatively robust to data uncertainty, we also identify a distinct second set of estimates. In the second example, we use our method to show that the topology of networks constructed from time-course gene expression data appears to be sensitive to data uncertainty, although there may be individual edges in the network that are robust in light of present data.AvailabilityMatlab code for performing GPR bootstrapping is available from our web site: http://www3.imperial.ac.uk/theoreticalsystemsbiology/data-software/.
| S-EPMC2677737 | biostudies-literature