Browsing by Subject "Models, Statistical"

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    ItemOpen Access
    29 mammalian genomes reveal novel exaptations of mobile elements for likely regulatory functions in the human genome.
    (PloS one, 2012-01) Lowe, Craig B; Haussler, David
    Recent research supports the view that changes in gene regulation, as opposed to changes in the genes themselves, play a significant role in morphological evolution. Gene regulation is largely dependent on transcription factor binding sites. Researchers are now able to use the available 29 mammalian genomes to measure selective constraint at the level of binding sites. This detailed map of constraint suggests that mammalian genomes co-opt fragments of mobile elements to act as gene regulatory sequence on a large scale. In the human genome we detect over 280,000 putative regulatory elements, totaling approximately 7 Mb of sequence, that originated as mobile element insertions. These putative regulatory regions are conserved non-exonic elements (CNEEs), which show considerable cross-species constraint and signatures of continued negative selection in humans, yet do not appear in a known mature transcript. These putative regulatory elements were co-opted from SINE, LINE, LTR and DNA transposon insertions. We demonstrate that at least 11%, and an estimated 20%, of gene regulatory sequence in the human genome showing cross-species conservation was co-opted from mobile elements. The location in the genome of CNEEs co-opted from mobile elements closely resembles that of CNEEs in general, except in the centers of the largest gene deserts where recognizable co-option events are relatively rare. We find that regions of certain mobile element insertions are more likely to be held under purifying selection than others. In particular, we show 6 examples where paralogous instances of an often co-opted mobile element region define a sequence motif that closely matches a transcription factor's binding profile.
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    A functional analysis of the spacer of V(D)J recombination signal sequences.
    (PLoS Biol, 2003-10) Lee, Alfred Ian; Fugmann, Sebastian D; Cowell, Lindsay G; Ptaszek, Leon M; Kelsoe, Garnett; Schatz, David G
    During lymphocyte development, V(D)J recombination assembles antigen receptor genes from component V, D, and J gene segments. These gene segments are flanked by a recombination signal sequence (RSS), which serves as the binding site for the recombination machinery. The murine Jbeta2.6 gene segment is a recombinationally inactive pseudogene, but examination of its RSS reveals no obvious reason for its failure to recombine. Mutagenesis of the Jbeta2.6 RSS demonstrates that the sequences of the heptamer, nonamer, and spacer are all important. Strikingly, changes solely in the spacer sequence can result in dramatic differences in the level of recombination. The subsequent analysis of a library of more than 4,000 spacer variants revealed that spacer residues of particular functional importance are correlated with their degree of conservation. Biochemical assays indicate distinct cooperation between the spacer and heptamer/nonamer along each step of the reaction pathway. The results suggest that the spacer serves not only to ensure the appropriate distance between the heptamer and nonamer but also regulates RSS activity by providing additional RAG:RSS interaction surfaces. We conclude that while RSSs are defined by a "digital" requirement for absolutely conserved nucleotides, the quality of RSS function is determined in an "analog" manner by numerous complex interactions between the RAG proteins and the less-well conserved nucleotides in the heptamer, the nonamer, and, importantly, the spacer. Those modulatory effects are accurately predicted by a new computational algorithm for "RSS information content." The interplay between such binary and multiplicative modes of interactions provides a general model for analyzing protein-DNA interactions in various biological systems.
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    A new predictive model for an improved respiratory isolation strategy in HIV-infected patients with PTB.
    (The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease, 2014-07) Carugati, M; Schiroli, C; Zanini, F; Vanoni, N; Galli, M; Adorni, F; Franzetti, F

    Setting

    Luigi Sacco Hospital, Milan, Italy, 1 January 2000-31 December 2010.

    Objectives

    To develop a predictive score for identifying human immunodeficiency virus (HIV) infected patients with pulmonary tuberculosis (PTB).

    Design

    Retrospective study based on the medical charts of HIV-infected patients admitted consecutively on presumption of PTB. Patients with culture-positive TB were included in the TB group. Culture-negative subjects formed the non-TB group. Risk factors for PTB were identified and a predictive model was developed. The diagnostic test accuracy of the derived score and that of previously developed scores were analysed.

    Results

    A total of 65 patients were included in the TB group and 505 subjects in the non-TB group. An eight-variable model (age, origin, alcohol use, respiratory rate, weight loss, haemoglobin, white blood cell count, typical chest X-ray) was derived. When compared with the different scores, this model showed the greatest area under the receiver operating characteristic curve (0.880). This score was the only one to present a negative likelihood ratio of <0.2, which is the threshold for giving strong diagnostic evidence against TB.

    Conclusions

    This model may be useful in predicting PTB in HIV patients in low-endemic countries. A validation study is necessary.
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    ItemOpen Access
    Advances to Bayesian network inference for generating causal networks from observational biological data.
    (Bioinformatics, 2004-12-12) Yu, Jing; Smith, V Anne; Wang, Paul P; Hartemink, Alexander J; Jarvis, Erich D
    MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY: Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/
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    An age-structured extension to the vectorial capacity model.
    (PLoS One, 2012) Novoseltsev, Vasiliy N; Michalski, Anatoli I; Novoseltseva, Janna A; Yashin, Anatoliy I; Carey, James R; Ellis, Alicia M
    BACKGROUND: Vectorial capacity and the basic reproductive number (R(0)) have been instrumental in structuring thinking about vector-borne pathogen transmission and how best to prevent the diseases they cause. One of the more important simplifying assumptions of these models is age-independent vector mortality. A growing body of evidence indicates that insect vectors exhibit age-dependent mortality, which can have strong and varied affects on pathogen transmission dynamics and strategies for disease prevention. METHODOLOGY/PRINCIPAL FINDINGS: Based on survival analysis we derived new equations for vectorial capacity and R(0) that are valid for any pattern of age-dependent (or age-independent) vector mortality and explore the behavior of the models across various mortality patterns. The framework we present (1) lays the groundwork for an extension and refinement of the vectorial capacity paradigm by introducing an age-structured extension to the model, (2) encourages further research on the actuarial dynamics of vectors in particular and the relationship of vector mortality to pathogen transmission in general, and (3) provides a detailed quantitative basis for understanding the relative impact of reductions in vector longevity compared to other vector-borne disease prevention strategies. CONCLUSIONS/SIGNIFICANCE: Accounting for age-dependent vector mortality in estimates of vectorial capacity and R(0) was most important when (1) vector densities are relatively low and the pattern of mortality can determine whether pathogen transmission will persist; i.e., determines whether R(0) is above or below 1, (2) vector population growth rate is relatively low and there are complex interactions between birth and death that differ fundamentally from birth-death relationships with age-independent mortality, and (3) the vector exhibits complex patterns of age-dependent mortality and R(0) ∼ 1. A limiting factor in the construction and evaluation of new age-dependent mortality models is the paucity of data characterizing vector mortality patterns, particularly for free ranging vectors in the field.
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    An approach to the development of quantitative models to assess the effects of exposure to environmentally relevant levels of endocrine disruptors on homeostasis in adults.
    (Environmental health perspectives, 1999-08) Ben-Jonathan, N; Cooper, RL; Foster, P; Hughes, CL; Hoyer, PB; Klotz, D; Kohn, M; Lamb, DJ; Stancel, GM
    The workshop "Characterizing the Effects of Endocrine Disruptors on Human Health at Environmental Exposure Levels" was held to provide a forum for discussions and recommendations of methods and data needed to improve risk assessments of endocrine disruptors. This article was produced by a working group charged with determining the basic mechanistic information that should be considered when designing models to quantitatively assess potential risks of environmental endocrine disruptors in adults. To reach this goal, we initially identified a set of potential organ system toxicities in males and females on the basis of known and/or suspected effects of endocrine disruptors on estrogen, androgen, and thyroid hormone systems. We used this integrated, systems-level approach because endocrine disruptors have the potential to exert toxicities at many levels and by many molecular mechanisms. Because a detailed analysis of all these untoward effects was beyond the scope of this workshop, we selected the specific end point of testicular function for a more detailed analysis. The goal was to identify the information required to develop a quantitative model(s) of the effects of endocrine disruptors on this system while focusing on spermatogenesis, sperm characteristics, and testicular steroidogenesis as specific markers. Testicular function was selected because it is a prototypical integrated end point that can be affected adversely by individual endocrine disruptors or chemical mixtures acting at one specific site or at multiple sites. Our specific objective was to gather the information needed to develop models in the adult organism containing functional homeostatic mechanisms, and for this reason we did not consider possible developmental toxicities. Homeostatic mechanisms have the potential to ameliorate or lessen the effects of endocrine disruptors, but these pathways are also potential target sites for the actions of these chemicals.
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    Are prediction models for vaginal birth after cesarean accurate?
    (American journal of obstetrics and gynecology, 2019-05) Harris, Benjamin S; Heine, R Phillips; Park, Jinyoung; Faurot, Keturah R; Hopkins, Maeve K; Rivara, Andrew J; Kemeny, Hanna R; Grotegut, Chad A; Jelovsek, J Eric
    BACKGROUND:The use of trial of labor after cesarean delivery calculators in the prediction of successful vaginal birth after cesarean delivery gives physicians an evidence-based tool to assist with patient counseling and risk stratification. Before deployment of prediction models for routine care at an institutional level, it is recommended to test their performance initially in the institution's target population. This allows the institution to understand not only the overall accuracy of the model for the intended population but also to comprehend where the accuracy of the model is most limited when predicting across the range of predictions (calibration). OBJECTIVE:The purpose of this study was to compare 3 models that predict successful vaginal birth after cesarean delivery with the use of a single tertiary referral cohort before continuous model deployment in the electronic medical record. STUDY DESIGN:All cesarean births for failed trial of labor after cesarean delivery and successful vaginal birth after cesarean delivery at an academic health system between May 2013 and March 2016 were reviewed. Women with a history of 1 previous cesarean birth who underwent a trial of labor with a term (≥37 weeks gestation), cephalic, and singleton gestation were included. Women with antepartum intrauterine fetal death or fetal anomalies were excluded. The probability of successful vaginal birth after cesarean delivery was calculated with the use of 3 prediction models: Grobman 2007, Grobman 2009, and Metz 2013 and compared with actual vaginal birth after cesarean delivery success. Each model's performance was measured with the use of concordance indices, Brier scores, and calibration plots. Decision curve analysis identified the range of threshold probabilities for which the best prediction model would be of clinical value. RESULTS:Four hundred four women met the eligibility criteria. The observed rate of successful vaginal birth after cesarean delivery was 75% (305/404). Concordance indices were 0.717 (95% confidence interval, 0.659-0.778), 0.703 (95% confidence interval, 0.647-0.758), and 0.727 (95% confidence interval, 0.669-0.779), respectively. Brier scores were 0.172, 0.205, and 0.179, respectively. Calibration demonstrated that Grobman 2007 and Metz vaginal birth after cesarean delivery models were most accurate when predicted probabilities were >60% and were beneficial for counseling women who did not desire to have vaginal birth after cesarean delivery but had a predicted success rates of 60-90%. The models underpredicted actual probabilities when predicting success at <60%. The Grobman 2007 and Metz vaginal birth after cesarean delivery models provided greatest net benefit between threshold probabilities of 60-90% but did not provide a net benefit with lower predicted probabilities of success compared with a strategy of recommending vaginal birth after cesarean delivery for all women . CONCLUSION:When 3 commonly used vaginal birth after cesarean delivery prediction models are compared in the same population, there are differences in performance that may affect an institution's choice of which model to use.
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    ItemRestricted
    Association between DNA damage response and repair genes and risk of invasive serous ovarian cancer.
    (PLoS One, 2010-04-08) Schildkraut, Joellen M; Iversen, Edwin S; Wilson, Melanie A; Clyde, Merlise A; Moorman, Patricia G; Palmieri, Rachel T; Whitaker, Regina; Bentley, Rex C; Marks, Jeffrey R; Berchuck, Andrew
    BACKGROUND: We analyzed the association between 53 genes related to DNA repair and p53-mediated damage response and serous ovarian cancer risk using case-control data from the North Carolina Ovarian Cancer Study (NCOCS), a population-based, case-control study. METHODS/PRINCIPAL FINDINGS: The analysis was restricted to 364 invasive serous ovarian cancer cases and 761 controls of white, non-Hispanic race. Statistical analysis was two staged: a screen using marginal Bayes factors (BFs) for 484 SNPs and a modeling stage in which we calculated multivariate adjusted posterior probabilities of association for 77 SNPs that passed the screen. These probabilities were conditional on subject age at diagnosis/interview, batch, a DNA quality metric and genotypes of other SNPs and allowed for uncertainty in the genetic parameterizations of the SNPs and number of associated SNPs. Six SNPs had Bayes factors greater than 10 in favor of an association with invasive serous ovarian cancer. These included rs5762746 (median OR(odds ratio)(per allele) = 0.66; 95% credible interval (CI) = 0.44-1.00) and rs6005835 (median OR(per allele) = 0.69; 95% CI = 0.53-0.91) in CHEK2, rs2078486 (median OR(per allele) = 1.65; 95% CI = 1.21-2.25) and rs12951053 (median OR(per allele) = 1.65; 95% CI = 1.20-2.26) in TP53, rs411697 (median OR (rare homozygote) = 0.53; 95% CI = 0.35 - 0.79) in BACH1 and rs10131 (median OR( rare homozygote) = not estimable) in LIG4. The six most highly associated SNPs are either predicted to be functionally significant or are in LD with such a variant. The variants in TP53 were confirmed to be associated in a large follow-up study. CONCLUSIONS/SIGNIFICANCE: Based on our findings, further follow-up of the DNA repair and response pathways in a larger dataset is warranted to confirm these results.
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    ItemOpen Access
    Automated quality control in nuclear medicine using the structured noise index.
    (Journal of applied clinical medical physics, 2020-04) Nelson, Jeffrey S; Samei, Ehsan

    Purpose

    Daily flood-field uniformity evaluation serves as the central element of nuclear medicine (NM) quality control (QC) programs. Uniformity images are traditionally analyzed using pixel value-based metrics, that is, integral uniformity (IU), which often fail to capture subtle structure and patterns caused by changes in gamma camera performance, requiring visual inspections which are subjective and time demanding. The goal of this project was to implement an advanced QC metrology for NM to effectively identify nonuniformity issues, and report issues in a timely manner for efficient correction prior to clinical use. The project involved the implementation of the program over a 2-year period at a multisite major medical institution.

    Methods

    Using a previously developed quantitative uniformity analysis metric, the structured noise index (SNI) [Nelson et al. (2014), \textit{J Nucl Med.}, \textbf{55}:169-174], an automated QC process was developed to analyze, archive, and report on daily NM QC uniformity images. Clinical implementation of the newly developed program ran in parallel with the manufacturer's reported IU-based QC program. The effectiveness of the SNI program was evaluated over a 21-month period using sensitivity and coefficient of variation statistics.

    Results

    A total of 7365 uniformity QC images were analyzed. Lower level SNI alerts were generated in 12.5% of images and upper level alerts in 1.7%. Intervention due to image quality issues occurred on 26 instances; the SNI metric identified 24, while the IU metric identified eight. The SNI metric reported five upper level alerts where no clinical engineering intervention was deemed necessary.

    Conclusion

    An SNI-based QC program provides a robust quantification of the performance of gamma camera uniformity. It operates seamlessly across a fleet of multiple camera models and, additionally, provides effective workflow among the clinical staff. The reliability of this process could eliminate the need for visual inspection of each image, saving valuable time, while enabling quantitative analysis of inter- and intrasystem performance.
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    Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location.
    (Proceedings of the National Academy of Sciences of the United States of America, 2012-03) Graupner, Michael; Brunel, Nicolas
    Multiple stimulation protocols have been found to be effective in changing synaptic efficacy by inducing long-term potentiation or depression. In many of those protocols, increases in postsynaptic calcium concentration have been shown to play a crucial role. However, it is still unclear whether and how the dynamics of the postsynaptic calcium alone determine the outcome of synaptic plasticity. Here, we propose a calcium-based model of a synapse in which potentiation and depression are activated above calcium thresholds. We show that this model gives rise to a large diversity of spike timing-dependent plasticity curves, most of which have been observed experimentally in different systems. It accounts quantitatively for plasticity outcomes evoked by protocols involving patterns with variable spike timing and firing rate in hippocampus and neocortex. Furthermore, it allows us to predict that differences in plasticity outcomes in different studies are due to differences in parameters defining the calcium dynamics. The model provides a mechanistic understanding of how various stimulation protocols provoke specific synaptic changes through the dynamics of calcium concentration and thresholds implementing in simplified fashion protein signaling cascades, leading to long-term potentiation and long-term depression. The combination of biophysical realism and analytical tractability makes it the ideal candidate to study plasticity at the synapse, neuron, and network levels.
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    Cervical cancer precursors and hormonal contraceptive use in HIV-positive women: application of a causal model and semi-parametric estimation methods.
    (PLoS One, 2014) Leslie, Hannah H; Karasek, Deborah A; Harris, Laura F; Chang, Emily; Abdulrahim, Naila; Maloba, May; Huchko, Megan J
    OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.
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    Challenges of COVID-19 Case Forecasting in the US, 2020-2021.
    (PLoS computational biology, 2024-05) Lopez, Velma K; Cramer, Estee Y; Pagano, Robert; Drake, John M; O'Dea, Eamon B; Adee, Madeline; Ayer, Turgay; Chhatwal, Jagpreet; Dalgic, Ozden O; Ladd, Mary A; Linas, Benjamin P; Mueller, Peter P; Xiao, Jade; Bracher, Johannes; Castro Rivadeneira, Alvaro J; Gerding, Aaron; Gneiting, Tilmann; Huang, Yuxin; Jayawardena, Dasuni; Kanji, Abdul H; Le, Khoa; Mühlemann, Anja; Niemi, Jarad; Ray, Evan L; Stark, Ariane; Wang, Yijin; Wattanachit, Nutcha; Zorn, Martha W; Pei, Sen; Shaman, Jeffrey; Yamana, Teresa K; Tarasewicz, Samuel R; Wilson, Daniel J; Baccam, Sid; Gurung, Heidi; Stage, Steve; Suchoski, Brad; Gao, Lei; Gu, Zhiling; Kim, Myungjin; Li, Xinyi; Wang, Guannan; Wang, Lily; Wang, Yueying; Yu, Shan; Gardner, Lauren; Jindal, Sonia; Marshall, Maximilian; Nixon, Kristen; Dent, Juan; Hill, Alison L; Kaminsky, Joshua; Lee, Elizabeth C; Lemaitre, Joseph C; Lessler, Justin; Smith, Claire P; Truelove, Shaun; Kinsey, Matt; Mullany, Luke C; Rainwater-Lovett, Kaitlin; Shin, Lauren; Tallaksen, Katharine; Wilson, Shelby; Karlen, Dean; Castro, Lauren; Fairchild, Geoffrey; Michaud, Isaac; Osthus, Dave; Bian, Jiang; Cao, Wei; Gao, Zhifeng; Lavista Ferres, Juan; Li, Chaozhuo; Liu, Tie-Yan; Xie, Xing; Zhang, Shun; Zheng, Shun; Chinazzi, Matteo; Davis, Jessica T; Mu, Kunpeng; Pastore Y Piontti, Ana; Vespignani, Alessandro; Xiong, Xinyue; Walraven, Robert; Chen, Jinghui; Gu, Quanquan; Wang, Lingxiao; Xu, Pan; Zhang, Weitong; Zou, Difan; Gibson, Graham Casey; Sheldon, Daniel; Srivastava, Ajitesh; Adiga, Aniruddha; Hurt, Benjamin; Kaur, Gursharn; Lewis, Bryan; Marathe, Madhav; Peddireddy, Akhil Sai; Porebski, Przemyslaw; Venkatramanan, Srinivasan; Wang, Lijing; Prasad, Pragati V; Walker, Jo W; Webber, Alexander E; Slayton, Rachel B; Biggerstaff, Matthew; Reich, Nicholas G; Johansson, Michael A
    During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.
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    Childhood bullying involvement predicts low-grade systemic inflammation into adulthood.
    (Proc Natl Acad Sci U S A, 2014-05-27) Copeland, WE; Wolke, D; Lereya, ST; Shanahan, L; Worthman, C; Costello, EJ
    Bullying is a common childhood experience that involves repeated mistreatment to improve or maintain one's status. Victims display long-term social, psychological, and health consequences, whereas bullies display minimal ill effects. The aim of this study is to test how this adverse social experience is biologically embedded to affect short- or long-term levels of C-reactive protein (CRP), a marker of low-grade systemic inflammation. The prospective population-based Great Smoky Mountains Study (n = 1,420), with up to nine waves of data per subject, was used, covering childhood/adolescence (ages 9-16) and young adulthood (ages 19 and 21). Structured interviews were used to assess bullying involvement and relevant covariates at all childhood/adolescent observations. Blood spots were collected at each observation and assayed for CRP levels. During childhood and adolescence, the number of waves at which the child was bullied predicted increasing levels of CRP. Although CRP levels rose for all participants from childhood into adulthood, being bullied predicted greater increases in CRP levels, whereas bullying others predicted lower increases in CRP compared with those uninvolved in bullying. This pattern was robust, controlling for body mass index, substance use, physical and mental health status, and exposures to other childhood psychosocial adversities. A child's role in bullying may serve as either a risk or a protective factor for adult low-grade inflammation, independent of other factors. Inflammation is a physiological response that mediates the effects of both social adversity and dominance on decreases in health.
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    Compressive holography.
    (2012) Lim, Se Hoon
    Compressive holography estimates images from incomplete data by using sparsity priors. Compressive holography combines digital holography and compressive sensing. Digital holography consists of computational image estimation from data captured by an electronic focal plane array. Compressive sensing enables accurate data reconstruction by prior knowledge on desired signal. Computational and optical co-design optimally supports compressive holography in the joint computational and optical domain. This dissertation explores two examples of compressive holography : estimation of 3D tomographic images from 2D data and estimation of images from under sampled apertures. Compressive holography achieves single shot holographic tomography using decompressive inference. In general, 3D image reconstruction suffers from underdetermined measurements with a 2D detector. Specifically, single shot holographic tomography shows the uniqueness problem in the axial direction because the inversion is ill-posed. Compressive sensing alleviates the ill-posed problem by enforcing some sparsity constraints. Holographic tomography is applied for video-rate microscopic imaging and diffuse object imaging. In diffuse object imaging, sparsity priors are not valid in coherent image basis due to speckle. So incoherent image estimation is designed to hold the sparsity in incoherent image basis by support of multiple speckle realizations. High pixel count holography achieves high resolution and wide field-of-view imaging. Coherent aperture synthesis can be one method to increase the aperture size of a detector. Scanning-based synthetic aperture confronts a multivariable global optimization problem due to time-space measurement errors. A hierarchical estimation strategy divides the global problem into multiple local problems with support of computational and optical co-design. Compressive sparse aperture holography can be another method. Compressive sparse sampling collects most of significant field information with a small fill factor because object scattered fields are locally redundant. Incoherent image estimation is adopted for the expanded modulation transfer function and compressive reconstruction.
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    Computed tomography dose index and dose length product for cone-beam CT: Monte Carlo simulations.
    (Journal of applied clinical medical physics, 2011-01-19) Kim, Sangroh; Song, Haijun; Samei, Ehsan; Yin, Fang-Fang; Yoshizumi, Terry T
    Dosimetry in kilovoltage cone beam computed tomography (CBCT) is a challenge due to the limitation of physical measurements. To address this, we used a Monte Carlo (MC) method to estimate the CT dose index (CTDI) and the dose length product (DLP) for a commercial CBCT system. As Dixon and Boone showed that CTDI concept can be applicable to both CBCT and conventional CT, we evaluated weighted CT dose index (CTDI(w)) and DLP for a commercial CBCT system. Two extended CT phantoms were created in our BEAMnrc/EGSnrc MC system. Before the simulations, the beam collimation of a Varian On-Board Imager (OBI) system was measured with radiochromic films (model: XR-QA). The MC model of the OBI X-ray tube, validated in a previous study, was used to acquire the phase space files of the full-fan and half-fan cone beams. Then, DOSXYZnrc user code simulated a total of 20 CBCT scans for the nominal beam widths from 1 cm to 10 cm. After the simulations, CBCT dose profiles at center and peripheral locations were extracted and integrated (dose profile integral, DPI) to calculate the CTDI per each beam width. The weighted cone-beam CTDI (CTDI(w,l)) was calculated from DPI values and mean CTDI(w,l) (CTDI(w,l)) and DLP were derived. We also evaluated the differences of CTDI(w) values between MC simulations and point dose measurements using standard CT phantoms. In results, it was found that CTDI(w,600) was 8.74 ± 0.01 cGy for head and CTDI(w,900) was 4.26 ± 0.01 cGy for body scan. The DLP was found to be proportional to the beam collimation. We also found that the point dose measurements with standard CT phantoms can estimate the CTDI within 3% difference compared to the full integrated CTDI from the MC method. This study showed the usability of CTDI as a dose index and DLP as a total dose descriptor in CBCT scans.
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    Cytokine profiles of preterm neonates with fungal and bacterial sepsis.
    (Pediatr Res, 2012-08) Sood, Beena G; Shankaran, Seetha; Schelonka, Robert L; Saha, Shampa; Benjamin, Danny K; Sánchez, Pablo J; Adams-Chapman, Ira; Stoll, Barbara J; Thorsen, Poul; Skogstrand, Kristin; Ehrenkranz, Richard A; Hougaard, David M; Goldberg, Ronald N; Tyson, Jon E; Das, Abhik; Higgins, Rosemary D; Carlo, Waldemar A; Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network
    BACKGROUND: Information on cytokine profiles in fungal sepsis (FS), an important cause of mortality in extremely low birthweight (ELBW) infants, is lacking. We hypothesized that cytokine profiles in the first 21 d of life in ELBW infants with FS differ from those with bacterial sepsis (BS) or no sepsis (NS). METHODS: In a secondary analysis of the National Institute of Child Health and Human Development Cytokine study, three groups were defined-FS (≥1 episode of FS), BS (≥1 episode of BS without FS), and NS. Association between 11 cytokines assayed in dried blood spots obtained on days 0-1, 3 ± 1, 7 ± 2, 14 ± 3, and 21 ± 3 and sepsis group was explored. RESULTS: Of 1,066 infants, 89 had FS and 368 had BS. As compared with BS, FS was more likely to be associated with lower birthweight, vaginal delivery, patent ductus arteriosus, postnatal steroids, multiple central lines, longer respiratory support and hospital stay, and higher mortality (P < 0.05). Analyses controlling for covariates showed significant group differences over time for interferon-γ (IFN-γ), interleukin (IL)-10, IL-18, transforming growth factor-β (TGF-β), and tumor necrosis factor-α (TNF-α) (P < 0.05). CONCLUSION: Significant differences in profiles for IFN-γ, IL-10, IL-18, TGF-β, and TNF-α in FS, BS, or NS in this hypothesis-generating secondary study require validation in rigorously designed prospective studies and may have implications for diagnosis and treatment.
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    ItemOpen Access
    Designing risk prediction models for ambulatory no-shows across different specialties and clinics.
    (Journal of the American Medical Informatics Association : JAMIA, 2018-08) Ding, Xiruo; Gellad, Ziad F; Mather, Chad; Barth, Pamela; Poon, Eric G; Newman, Mark; Goldstein, Benjamin A
    Objective:As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit. Methods:Using data from 2 264 235 outpatient appointments we assessed the performance of models built across 14 different specialties and 55 clinics. We used regularized logistic regression models to fit and assess models built on the health system, specialty, and clinic levels. We evaluated fits based on their discrimination and calibration. Results:Overall, the results suggest that a relatively robust risk score for patient no-shows could be derived with an average C-statistic of 0.83 across clinic level models and strong calibration. Moreover, the clinic specific models, even with lower training set sizes, often performed better than the more general models. Examination of the individual models showed that risk factors had different degrees of predictability across the different specialties. Implementation of optimal modeling strategies would lead to capturing an additional 4819 no-shows per-year. Conclusion:Overall, this work highlights both the opportunity for and the importance of leveraging the available electronic health record data to develop more refined risk models.
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    Development and Validation of a Model for Opioid Prescribing Following Gynecological Surgery.
    (JAMA network open, 2022-07) Rodriguez, Isabel V; Cisa, Paige McKeithan; Monuszko, Karen; Salinaro, Julia; Habib, Ashraf S; Jelovsek, J Eric; Havrilesky, Laura J; Davidson, Brittany

    Importance

    Overprescription of opioid medications following surgery is well documented. Current prescribing models have been proposed in narrow patient populations, which limits their generalizability.

    Objective

    To develop and validate a model for predicting outpatient opioid use following a range of gynecological surgical procedures.

    Design, setting, and participants

    In this prognostic study, statistical models were explored using data from a training cohort of participants undergoing gynecological surgery for benign and malignant indications enrolled prospectively at a single institution's academic gynecologic oncology practice from February 2018 to March 2019 (cohort 1) and considering 39 candidate predictors of opioid use. Final models were internally validated using a separate testing cohort enrolled from May 2019 to February 2020 (cohort 2). The best final model was updated by combining cohorts, and an online calculator was created. Data analysis was performed from March to May 2020.

    Exposures

    Participants completed a preoperative survey and weekly postoperative assessments (up to 6 weeks) following gynecological surgery. Pain management was at the discretion of clinical practitioners.

    Main outcomes and measures

    The response variable used in model development was number of pills used postoperatively, and the primary outcome was model performance using ordinal concordance and Brier score.

    Results

    Data from 382 female adult participants (mean age, 56 years; range, 18-87 years) undergoing gynecological surgery (minimally invasive procedures, 158 patients [73%] in cohort 1 and 118 patients [71%] in cohort 2; open surgical procedures, 58 patients [27%] in cohort 1 and 48 patients [29%] in cohort 2) were included in model development. One hundred forty-seven patients (38%) used 0 pills after hospital discharge, and the mean (SD) number of pills used was 7 (10) (median [IQR], 3 [0-10] pills). The model used 7 predictors: age, educational attainment, smoking history, anticipated pain medication use, anxiety regarding surgery, operative time, and preoperative pregabalin administration. The ordinal concordance was 0.65 (95% CI, 0.62-0.68) for predicting 5 or more pills (Brier score, 0.22), 0.65 (95% CI, 0.62-0.68) for predicting 10 or more pills (Brier score, 0.18), and 0.65 (95% CI, 0.62-0.68) for predicting 15 or more pills (Brier score, 0.14).

    Conclusions and relevance

    This model provides individualized estimates of outpatient opioid use following a range of gynecological surgical procedures for benign and malignant indications with all model inputs available at the time of procedure closing. Implementation of this model into the clinical setting is currently ongoing, with plans for additional validation in other surgical populations.
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    Enhancing imaging systems using transformation optics.
    (Opt Express, 2010-09-27) Smith, David R; Urzhumov, Yaroslav; Kundtz, Nathan B; Landy, Nathan I
    We apply the transformation optical technique to modify or improve conventional refractive and gradient index optical imaging devices. In particular, when it is known that a detector will terminate the paths of rays over some surface, more freedom is available in the transformation approach, since the wave behavior over a large portion of the domain becomes unimportant. For the analyzed configurations, quasi-conformal and conformal coordinate transformations can be used, leading to simplified constitutive parameter distributions that, in some cases, can be realized with isotropic index; index-only media can be low-loss and have broad bandwidth. We apply a coordinate transformation to flatten a Maxwell fish-eye lens, forming a near-perfect relay lens; and also flatten the focal surface associated with a conventional refractive lens, such that the system exhibits an ultra-wide field-of-view with reduced aberration.
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    Equity and accuracy in medical malpractice insurance pricing.
    (J Health Econ, 1990-11) Sloan, FA; Hassan, M
    This study examines alternative classification approaches for setting medical malpractice insurance premiums. Insurers generally form risk classification categories on factors other than the physician's own loss experience. Our analysis of such classification approaches indicates different but no more categories than now used. An actuarially-fair premium-setting scheme based on the frequency and severity of the individual physician's losses would substantially penalize adverse experience. Alternatively, premiums could be set for groups of physicians, such as hospital medical staffs. Our simulations suggest that even staffs at rather small hospitals may be large enough to be experience-rated.