Protein quaternary structure determination using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and chemical crosslinking

A means of analyzing protein quaternary structure using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI MS) and chemical crosslinking was evaluated. Proteins of known oligomeric structure, as well as monomeric proteins, were analyzed to evaluate the method. The quaternary structure of proteins of unknown or uncertain structure was investigated using this technique. The stoichiometry of recombinant E. coli carbamoyl phosphate synthetase and recombinant human farnesyl protein transferase were determined to be heterodimers using glutaraldehyde crosslinking, agreeing with the stoichiometry found for the wild type proteins. The stoichiometry of the gamma subunit of E. coli DNA polymerase III holoenzyme was determined in solution without the presence of other subunits to be a homotetramer using glutaraldehyde crosslinking and MALDI MS analysis. Chi and psi subunits of E. coli DNA polymerase III subunits appeared to form a heterodimer when crosslinked with heterobifunctional photoreactive crosslinkers.^ Comparison of relative % peak areas obtained from MALDI MS analysis of crosslinked proteins and densitometric scanning of silver stained sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) gels showed excellent qualitative agreement for the two techniques, but the quantitative analyses differed, sometimes significantly. This difference in quantitation could be due to SDS-PAGE conditions (differential staining, loss of sample) or to MALDI MS conditions (differences in ionization and/or detection). Investigation of pre-purified crosslinked monomers and dimers recombined in a specific ratio revealed the presence of mass discrimination in the MALDI MS process. The calculation of mass discrimination for two different MALDI time-of-flight instruments showed the loss of a factor of approximately 2.6 in relative peak area as the m/z value doubles over the m/z range from 30,000 to 145,000 daltons.^ Indirect symmetry was determined for tetramers using glutaraldehyde crosslinking with MALDI MS analysis. Mathematical modelling and simple graphing allowed the determination of the symmetry for several tetramers known to possess isologous D2 symmetry. These methods also distinguished tetramers that did not fit D2 symmetry such as apo-avidin. The gamma tetramer of E. coli DNA polymerase III appears to have isologous D2 symmetry. ^

If we call them, will they change? Feasibility of a telephone adaptation of motivational interviewing aimed at parents to reduce television time among 2nd and 4th grade children

Television viewing is a sedentary behavior that is modifiable. Reducing media-related behaviors via parent-focused interventions may hold promise for decreasing childhood obesity. This feasibility study examines a novel parent-centered brief telephone-delivered Motivational Enhancement Interview (MEI) to encourage parents of 2nd and 4th graders to set a rules limiting TV time or remove the TV from the child's bedroom. This quasi-experimental sub-study was part of the larger CATCH: En Vivo pilot study conducted in a Hispanic population in the Texas Rio Grande Valley. Parents in the MEI condition were contacted via telephone and encouraged to meet the American Academy of Pediatrics (AAP) recommended 2 hours per day or less of TV. Unconditional logistic regression was used to analyze the data. At post-test, 85% of parents of 4th graders in the MEI group (compared to 71% at pre-test) reported having a rule limiting TV time. The adjusted odds ratio for the MEI group compared to the control group was 3.88, 95% CI (0.72-20.99). At pre-test, 63.16% of 2nd graders had a television in their bedrooms. The 2nd grade MEI intervention reduced that number to 41.03% (OR=0.25, 95%CI (0.08-0.82)). This first look at using MEI to target parents of children to modify TV behavior presents evidence on a promising strategy for modifying children's home media environment and warrants further investigation. ^

MEASURING THE EFFECT OF ILLNESS ON THE EXPECTED REMAINING SERVICE TIME FOR THE ACTIVE DUTY ARMY: A LIFE TABLE APPROACH

A life table methodology was developed which estimates the expected remaining Army service time and the expected remaining Army sick time by years of service for the United States Army population. A measure of illness impact was defined as the ratio of expected remaining Army sick time to the expected remaining Army service time. The variances of the resulting estimators were developed on the basis of current data. The theory of partial and complete competing risks was considered for each type of decrement (death, administrative separation, and medical separation) and for the causes of sick time.^ The methodology was applied to world-wide U.S. Army data for calendar year 1978. A total of 669,493 enlisted personnel and 97,704 officers were reported on active duty as of 30 September 1978. During calendar year 1978, the Army Medical Department reported 114,647 inpatient discharges and 1,767,146 sick days. Although the methodology is completely general with respect to the definition of sick time, only sick time associated with an inpatient episode was considered in this study.^ Since the temporal measure was years of Army service, an age-adjusting process was applied to the life tables for comparative purposes. Analyses were conducted by rank (enlisted and officer), race and sex, and were based on the ratio of expected remaining Army sick time to expected remaining Army service time. Seventeen major diagnostic groups, classified by the Eighth Revision, International Classification of Diseases, Adapted for Use In The United States, were ranked according to their cumulative (across years of service) contribution to expected remaining sick time.^ The study results indicated that enlisted personnel tend to have more expected hospital-associated sick time relative to their expected Army service time than officers. Non-white officers generally have more expected sick time relative to their expected Army service time than white officers. This racial differential was not supported within the enlisted population. Females tend to have more expected sick time relative to their expected Army service time than males. This tendency remained after diagnostic groups 580-629 (Genitourinary System) and 630-678 (Pregnancy and Childbirth) were removed. Problems associated with the circulatory system, digestive system and musculoskeletal system were among the three leading causes of cumulative sick time across years of service. ^

Effect of patient accrual patterns on power and size of log-rank test for time-to-event outcome in clinical trials

The determination of size as well as power of a test is a vital part of a Clinical Trial Design. This research focuses on the simulation of clinical trial data with time-to-event as the primary outcome. It investigates the impact of different recruitment patterns, and time dependent hazard structures on size and power of the log-rank test. A non-homogeneous Poisson process is used to simulate entry times according to the different accrual patterns. A Weibull distribution is employed to simulate survival times according to the different hazard structures. The current study utilizes simulation methods to evaluate the effect of different recruitment patterns on size and power estimates of the log-rank test. The size of the log-rank test is estimated by simulating survival times with identical hazard rates between the treatment and the control arm of the study resulting in a hazard ratio of one. Powers of the log-rank test at specific values of hazard ratio (≠1) are estimated by simulating survival times with different, but proportional hazard rates for the two arms of the study. Different shapes (constant, decreasing, or increasing) of the hazard function of the Weibull distribution are also considered to assess the effect of hazard structure on the size and power of the log-rank test. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Left, right and interval bivariate censored data: Evaluating screening mammography in the presence of lead -time bias, length bias and over-detection

Of the large clinical trials evaluating screening mammography efficacy, none included women ages 75 and older. Recommendations on an upper age limit at which to discontinue screening are based on indirect evidence and are not consistent. Screening mammography is evaluated using observational data from the SEER-Medicare linked database. Measuring the benefit of screening mammography is difficult due to the impact of lead-time bias, length bias and over-detection. The underlying conceptual model divides the disease into two stages: pre-clinical (T0) and symptomatic (T1) breast cancer. Treating the time in these phases as a pair of dependent bivariate observations, (t0,t1), estimates are derived to describe the distribution of this random vector. To quantify the effect of screening mammography, statistical inference is made about the mammography parameters that correspond to the marginal distribution of the symptomatic phase duration (T1). This shows the hazard ratio of death from breast cancer comparing women with screen-detected tumors to those detected at their symptom onset is 0.36 (0.30, 0.42), indicating a benefit among the screen-detected cases. ^