Model reconstruction from temporal data for coupled oscillator networks

In a complex system, the interactions between individual agents often lead to emergent collective behavior like spontaneous synchronization, swarming, and pattern formation. The topology of the network of interactions can have a dramatic influence over those dynamics. In many studies, researchers start with a specific model for both the intrinsic dynamics of each agent and the interaction network, and attempt to learn about the dynamics that can be observed in the model. Here we consider the inverse problem: given the dynamics of a system, can one learn about the underlying network? We investigate arbitrary networks of coupled phase-oscillators whose dynamics are characterized by synchronization. We demonstrate that, given sufficient observational data on the transient evolution of each oscillator, one can use machine learning methods to reconstruct the interaction network and simultaneously identify the parameters of a model for the intrinsic dynamics of the oscillators and their coupling.Comment: 27 pages, 7 figures, 16 table

Conus Medullaris Enterogenous Cyst

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147142/1/pmr2698.pd

Pulmonary Artery Acceleration Time Provides a Reliable Estimate of Invasive Pulmonary Hemodynamics in Children

Background Pulmonary artery acceleration time (PAAT) is a non-invasive method to assess pulmonary hemodynamics, but lacks validity in children. This study sought to evaluate the accuracy of Doppler echocardiography (DE) derived PAAT in predicting right heart catheterization (RHC) derived pulmonary arterial pressure (PAP), pulmonary vascular resistance (PVR) and compliance in children. Methods Prospectively acquired and retrospectively measured DE derived PAAT and RHC derived systolic PAP (sPAP), mean PAP (mPAP), index PVR (PVRi) and compliance were compared by regression analysis in a derivation cohort of 75 children (median age, 5.3 years; 1.3–12.6) with wide ranges of pulmonary hemodynamics. To account for heart rate variability, PAAT was adjusted for right ventricle ejection time (RVET) and corrected by the RR interval. Regression equations incorporating PAAT and PAAT:RVET from the derivation cohort were then evaluated for the accuracy of its predictive values for invasive pulmonary hemodynamics in a validation cohort of 50 age- and weight- matched children with elevated PAP and PVR. Results There were significant inverse correlations between PAAT and RHC derived mPAP (r = −0.82) and PVRi (r= −0.78) and direct correlation (r= 0.78) between PAAT and pulmonary compliance in the derivation cohort. For detection of pulmonary hypertension (PRVi > 3 WU x m2 and mPAP > 25 mmHg), PAAT < 90 msec and PAAT:RVET < 0.31 resulted in a sensitivity of 97% and a specificity of 95%. In the derivation cohort, the regression equations relating PAAT with mPAP and PVRi were: mPAP = 48 – 0.28 x PAAT and PVRi = 9 –0.07 x PAAT. These PAAT integrated equations predicted RHC measured pulmonary hemodynamics in the validation cohort with good correlations (r = 0.88, 0.83 respectively), small biases (<10%), and minimal coefficient of variation (<8%). Conclusions PAAT inversely correlates with RHC measured pulmonary hemodynamics and directly correlates with pulmonary arterial compliance in children. The study established PAAT based regression equations in children to accurately predict RHC derived PAP and PVR

Predicting Persistent Opioid Use, Abuse, and Toxicity Among Cancer Survivors.

BackgroundAlthough opioids play a critical role in the management of cancer pain, the ongoing opioid epidemic has raised concerns regarding their persistent use and abuse. We lack data-driven tools in oncology to understand the risk of adverse opioid-related outcomes. This project seeks to identify clinical risk factors and create a risk score to help identify patients at risk of persistent opioid use and abuse.MethodsWithin a cohort of 106 732 military veteran cancer survivors diagnosed between 2000 and 2015, we determined rates of persistent posttreatment opioid use, diagnoses of opioid abuse or dependence, and admissions for opioid toxicity. A multivariable logistic regression model was used to identify patient, cancer, and treatment risk factors associated with adverse opioid-related outcomes. Predictive risk models were developed and validated using a least absolute shrinkage and selection operator regression technique.ResultsThe rate of persistent opioid use in cancer survivors was 8.3% (95% CI = 8.1% to 8.4%); the rate of opioid abuse or dependence was 2.9% (95% CI = 2.8% to 3.0%); and the rate of opioid-related admissions was 2.1% (95% CI = 2.0% to 2.2%). On multivariable analysis, several patient, demographic, and cancer and treatment factors were associated with risk of persistent opioid use. Predictive models showed a high level of discrimination when identifying individuals at risk of adverse opioid-related outcomes including persistent opioid use (area under the curve [AUC] = 0.85), future diagnoses of opioid abuse or dependence (AUC = 0.87), and admission for opioid abuse or toxicity (AUC = 0.78).ConclusionThis study demonstrates the potential to predict adverse opioid-related outcomes among cancer survivors. With further validation, personalized risk-stratification approaches could guide management when prescribing opioids in cancer patients

High Exposure Facility Technical Description

The High Exposure Facility is a collimated high-level gamma irradiator that is located in the basement of the 318 building. It was custom developed by PNNL back in 1982 to meet the needs for high range radiological instrument calibrations and dosimeter irradiations. At the time no commercially available product existed that could create exposure rates up to 20,000 R/h. This document is intended to pass on the design criteria that was employed to create this unique facility, while maintaining compliance with ANSI N543-1974, "General Safety Standard for Installations Using Non-Medical X-Ray and Sealed Gamma-Ray Sources, Energies up to 10 MeV.

An Automated Procedure to Identify Biomedical Articles that Contain Cancer-associated Gene Variants

The proliferation of biomedical literature makes it increasingly difficult for researchers to find and manage relevant information. However, identifying research articles containing mutation data, a requisite first step in integrating large and complex mutation data sets, is currently tedious, time-consuming and imprecise. More effective mechanisms for identifying articles containing mutation information would be beneficial both for the curation of mutation databases and for individual researchers. We developed an automated method that uses information extraction, classifier, and relevance ranking techniques to determine the likelihood of MEDLINE abstracts containing information regarding genomic variation data suitable for inclusion in mutation databases. We targeted the CDKN2A (p16) gene and the procedure for document identification currently used by CDKN2A Database curators as a measure of feasibility. A set of abstracts was manually identified from a MEDLINE search as potentially containing specific CDKN2A mutation events. A subset of these abstracts was used as a training set for a maximum entropy classifier to identify text features distinguishing relevant from not relevant abstracts. Each document was represented as a set of indicative word, word pair, and entity tagger-derived genomic variation features. When applied to a test set of 200 candidate abstracts, the classifier predicted 88 articles as being relevant; of these, 29 of 32 manuscripts in which manual curation found CDKN2A sequence variants were positively predicted. Thus, the set of potentially useful articles that a manual curator would have to review was reduced by 56%, maintaining 91% recall (sensitivity) and more than doubling precision (positive predictive value). Subsequent expansion of the training set to 494 articles yielded similar precision and recall rates, and comparison of the original and expanded trials demonstrated that the average precision improved with the larger data set. Our results show that automated systems can effectively identify article subsets relevant to a given task and may prove to be powerful tools for the broader research community. This procedure can be readily adapted to any or all genes, organisms, or sets of documents