Mistreatment and self-neglect in community dwelling older adults: Studies of construct validity, measurement invariance and latent classes

Mistreatment and self-neglect significantly increase the risk of dying in older adults. It is estimated that 1 to 2 million older adults experience elder mistreatment and self-neglect every year in the United States. Currently, there are no elder mistreatment and self-neglect assessment tools with construct validity and measurement invariance testing and no studies have sought to identify underlying latent classes of elder self-neglect that may have differential mortality rates. Using data from 11,280 adults with Texas APS substantiated elder mistreatment and self-neglect 3 studies were conducted to: (1) test the construct validity and (2) the measurement invariance across gender and ethnicity of the Texas Adult Protective Services (APS) Client Assessment and Risk Evaluation (CARE) tool and (3) identify latent classes associated with elder self-neglect. Study 1 confirmed the construct validity of the CARE tool following adjustments to the initial hypothesized CARE tool. This resulted in the deletion of 14 assessment items and a final assessment with 5 original factors and 43 items. Cross-validation for this model was achieved. Study 2 provided empirical evidence for factor loading and item-threshold invariance of the CARE tool across gender and between African-Americans and Caucasians. The financial status domain of the CARE tool did not function properly for Hispanics and thus, had to be deleted. Subsequent analyses showed factor loading and item-threshold invariance across all 3 ethnic groups with the exception of some residual errors. Study 3 identified 4-latent classes associated with elder self-neglect behaviors which included individuals with evidence of problems in the areas of (1) their environment, (2) physical and medical status, (3) multiple domains and (4) finances. Overall, these studies provide evidence supporting the use of APS CARE tool for providing unbiased and valid investigations of mistreatment and neglect in older adults with different demographic characteristics. Furthermore, the findings support the underlying notion that elder self-neglect may not only occur along a continuum, but that differential types may exist. All of which, have very important potential implications for social and health services distributed to vulnerable mistreated and neglected older adults.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

The application of latent variable models to the assessment of determinants of HIV risk behavior

Studies on the relationship between psychosocial determinants and HIV risk behaviors have produced little evidence to support hypotheses based on theoretical relationships. One limitation inherent in many articles in the literature is the method of measurement of the determinants and the analytic approach selected. ^ To reduce the misclassification associated with unit scaling of measures specific to internalized homonegativity, I evaluated the psychometric properties of the Reactions to Homosexuality scale in a confirmatory factor analytic framework. In addition, I assessed the measurement invariance of the scale across racial/ethnic classifications in a sample of men who have sex with men. The resulting measure contained eight items loading on three first-order factors. Invariance assessment identified metric and partial strong invariance between racial/ethnic groups in the sample. ^ Application of the updated measure to a structural model allowed for the exploration of direct and indirect effects of internalized homonegativity on unprotected anal intercourse. Pathways identified in the model show that drug and alcohol use at last sexual encounter, the number of sexual partners in the previous three months and sexual compulsivity all contribute directly to risk behavior. Internalized homonegativity reduced the likelihood of exposure to drugs, alcohol or higher numbers of partners. For men who developed compulsive sexual behavior as a coping strategy for internalized homonegativity, there was an increase in the prevalence odds of risk behavior. ^ In the final stage of the analysis, I conducted a latent profile analysis of the items in the updated Reactions to Homosexuality scale. This analysis identified five distinct profiles, which suggested that the construct was not homogeneous in samples of men who have sex with men. Lack of prior consideration of these distinct manifestations of internalized homonegativity may have contributed to the analytic difficulty in identifying a relationship between the trait and high-risk sexual practices. ^

HIV prevalence and contextual risk-factors among injection drug users in Harris County, Texas

Injection drug use is the third most frequent risk factor for new HIV infections in the United States. A dual mode of exposure: unsafe drug using practices and risky sexual behaviors underlies injection drug users' (IDUs) risk for HIV infection. This research study aims to characterize patterns of drug use and sexual behaviors and to examine the social contexts associated with risk behaviors among a sample of injection drug users. ^ This cross-sectional study includes 523 eligible injection drug users from Houston, Texas, recruited into the 2009 National HIV Behavioral Surveillance project. Three separate set of analyses were carried out. First, using latent class analysis (LCA) and maximum likelihood we identified classes of behavior describing levels of HIV risk, from nine drug and sexual behaviors. Second, eight separate multivariable regression models were built to examine the odds of reporting a given risk behavior. We constructed the most parsimonious multivariable model using a manual backward stepwise process. Third, we examined whether HIV serostatus knowledge (self-reported positive, negative, or unknown serostatus) is associated with drug use and sexual HIV risk behaviors. ^ Participants were mostly male, older, and non-Hispanic Black. Forty-two percent of our sample had behaviors putting them at high risk, 25% at moderate risk, and 33% at low risk for HIV infection. Individuals in the High-risk group had the highest probability of risky behaviors, categorized as almost always sharing needles (0.93), seldom using condoms (0.10), reporting recent exchange sex partners (0.90), and practicing anal sex (0.34). We observed that unsafe injecting practices were associated with high risk sexual behaviors. IDUs who shared needles had higher odds of having anal sex (OR=2.89, 95%CI: 1.69-4.92) and unprotected sex (OR=2.66, 95%CI: 1.38-5.10) at last sex. Additionally, homelessness was associated with needle sharing (OR=2.24, 95% CI: 1.34-3.76) and cocaine use was associated with multiple sex partners (OR=1.82, 95% CI: 1.07-3.11). Furthermore, twenty-one percent of the sample was unaware of their HIV serostatus. The three groups were not different from each other in terms of drug-use behaviors: always using a new sterile needle, or in sharing needles or drug preparation equipment. However, IDUs unaware of their HIV serostatus were 33% more likely to report having more than three sexual partners in the past 12 months; 45% more likely to report to have unprotected sex and 85% more likely to use drug and or alcohol during or before at last sex compared to HIV-positive IDUs. ^ This analysis underscores the merit of LCA approach to empirically categorize injection drug users into distinct classes and identify their risk pattern using multiple indicators and our results show considerable overlap of high risk sexual and drug use behaviors among the high-risk class members. The observed clustering pattern of drug and sexual risk behavior among this population confirms that injection drug users do not represent a homogeneous population in terms of HIV risk. These findings will help develop tailored prevention programs.^

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.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Analysis of the Soehner-Dmochowski strain of Moloney murine sarcoma virus

The Soehner-Dmochowski strain of murine sarcoma virus (MuSV-SD) was derived from a bone tumor of a New Zealand Black (NZB) rat infected with the Moloney strain of MuSV, which carries the gene encoding the v-mos protein. Serial passage of cell-free tumor extracts both decreased the latent period and resulted in osteosarcomas. Cells from a late passage tumor were established in culture, cell-free extracts frozen, and later inoculated into newborn NZB rats. One of the resulting bone tumors was established in culture and clonal cell lines derived, of which S4 was selected for the present study. The objectives of the study were two-fold: an examination of the genetic organization of MuSV-SD, and an examination of the biochemical characteristics of the viral proteins, since this is an acutely transforming virus which may yield insights into the mechanism of transformation caused by the v-mos protein. Blot hybridization of digested S4 genomic DNA reveals three candidate MuSV-SD integrated viral DNAs. The largest of these, MuSV-SD-6.5, was cloned from an S4 cosmid library, and the complete MuSV-SD-mos sequence was determined. The predicted amino acid sequence of the v-mos protein was compared to that of MuSV-124 and Ht-1, which show a 96.5% and 97.1% similarity, respectively. To characterize the MuSV-SD-mos protein further, immunochemical assays were performed using anti-mos antisera. The immunoblot analysis and immunoprecipitation assays demonstrated that similar levels of the v-mos protein were present in cells chronically infected with either MuSV-SD or MuSV-124; however, the immune complex kinase assay revealed greatly reduced in vitro serine kinase activity of the MuSV-SD-mos protein compared to that of MuSV-124. Sequence analysis demonstrated that the serine at amino acid residue 358 of the MuSV-SD-mos protein, like that of MuSV-Ht-1, had been mutated to a glycine. Mutations of this serine residue have been shown to affect the detectable in vitro kinase activity, however, v-mos proteins containing this mutation still retain transforming properties. Therefore, although the characteristic in vitro kinase activity of the MuSV-SD-mos protein has not been demonstrated, it is clear that this virus is a potent transforming agent. ^

Phenotypic and genotypic analysis of mouse hepatitis virus (JHM) complementation group A temperature-sensitive mutants

Temperature sensitive (ts) mutant viruses have helped elucidate replication processes in many viral systems. Several panels of replication-defective ts mutants in which viral RNA synthesis is abolished at the nonpermissive temperature (RNA$\sp{-})$ have been isolated for Mouse Hepatitis Virus, MHV (Robb et al., 1979; Koolen et al., 1983; Martin et al., 1988; Schaad et al., 1990). However, no one had investigated genetic or phenotypic relationships between these different mutant panels. In order to determine how the panel of MHV-JHM RNA$\sp{-}$ ts mutants (Robb et al., 1979) were genetically related to other described MHV RNA$\sp{-}$ ts mutants, the MHV-JHM mutants were tested for complementation with representatives from two different sets of MHV-A59 ts mutants (Koolen et al., 1983; Schaad et al., 1990). The three ts mutant panels together were found to comprise eight genetically distinct complementation groups. Of these eight complementation groups, three complementation classes are unique to their particular mutant panel; genetically equivalent mutants were not observed within the other two mutant panels. Two complementation groups were common to all three mutant panels. The three remaining complementation groups overlapped two of the three mutant sets. Mutants MHV-JHM tsA204 and MHV-A59 ts261 were shown to be within one of these overlapping complementation groups. The phenotype of the MHV-JHM mutants within this complementation class has been previously characterized (Leibowitz et al., 1982; Leibowitz et al, 1990). When these mutants were grown at the permissive temperature, then shifted up to the nonpermissive temperature at the start of RNA synthesis, genome-length RNA and leader RNA fragments accumulated, but no subgenomic mRNA was synthesized. MHV-A59 ts261 produced leader RNA fragments identical to those observed with MHV-JHM tsA204. Thus, these two MHV RNA$\sp{-}$ ts mutants that were genetically equivalent by complementation testing were phenotypically similar as well. Recombination frequencies obtained from crosses of MHV-A59 ts261 with several of the gene 1 MHV-A59 mutants indicated that the causal mutation(s) of MHV-A59 ts261 was located near the overlapping junction of ORF1a and ORF1b, in the 3$\sp\prime$ end of ORF1a, or the 5$\sp\prime$ end of ORF1b. Sequence analysis of this junction and 1400 nucleotides into the 5$\sp\prime$ end of ORF1b of MHV-A59 ts261 revealed one nucleotide change from the wildtype MHV-A59. This substitution at nucleotide 13,598 (A to G) was a silent mutation in the ORF1a reading frame, but resulted in an amino acid change in ORF1b gene product (I to V). This amino acid change would be expressed only in the readthrough translation product produced upon successful ribosome frameshifting. A revertant of MHV-A59 ts261 (R2) also retained this guanidine residue, but had a second substitution at nucleotide 14,475 in ORF1b. This mutation results in the substitution of valine for an isoleucine.^ The data presented here suggest that the mutation in MHV-A59 ts261 (nucleotide 13,598) would be responsible for the MHV-JHM complementation group A phenotype. A second-site reversion at nucleotide 14,475 may correct this defect in the revertant. Sequencing of gene 1 immediately upstream of nucleotide 13,296 and downstream of nucleotide 15,010 must be conducted to test this hypothesis. ^

ANALYSIS OF SEROTONERGIC MODULATION OF NEURONS INVOLVED IN TWO DEFENSIVE BEHAVIORS IN APLYSIA CALIFORNICA (CYCLIC-AMP, AROUSAL, CALCIUM, POTASSIUM)

Sensitization is a simple form of learning which refers to an enhancement of a behavioral response resulting from an exposure to a novel stimulus. While sensitization is found throughout the animal world, little is known regarding the underlying neural mechanisms. By taking advantage of the simple nervous system of the marine mollusc Aplysia, I have begun to examine the cellular and molecular mechanisms underlying this simple form of learning. In an attempt to determine the generality of the mechanisms of neuromodulation underlying sensitization, I have investigated and compared the modulation of neurons involved in two defensive behaviors in Aplysia, the defensive inking response and defensive tail withdrawal.^ The motor neurons that produce the defensive release of ink receive a slow decreased conductance excitatory postsynaptic potential (EPSP) in response to sensitizing stimuli. Using electrophysiological techniques, it was found that serotonin (5-HT) mimicked the physiologically produced slow EPSP. 5-HT produced its response through a reduction in a voltage-independent conductance to K('+). The 5-HT sensitive K('+) conductance of the ink motor neurons was separate from the fast K('+), delayed K('+), and Ca('2+)-activated K('+) conductances found in these and other molluscan neurons. 5-HT was shown to produce a decrease in K('+) conductance in the ink motor neurons through an elevation of cellular cAMP.^ The mechanosensory neurons that participate in the defensive tail withdrawal response are also modulated by sensitizing stimuli through the action of 5-HT. Using electrophysiological techniques, it was found that 5-HT modulated the tail sensory neurons through a reduction in a voltage-dependent conductance to K('+). The serotonin-sensitive K('+) conductance was found to be largely a Ca('2+)-activated K('+) conductance. Much like the ink motor neurons, 5-HT produced its modulation through an elevation of cellular cAMP. While the actual K('+) conductance modulated by 5-HT in these two classes of neurons differs, the following generalizations can be made: (1) the effects of sensitizing stimuli are mimicked by 5-HT, (2) 5-HT produces its effect through an elevation of cellular cAMP, and (3) the conductance to K('+) is modulated by 5-HT. ^

Identification and characterization of distinct classes of cAMP-pulse-induced genes in Dictyostelium discoideum development

The unicellular amoeba Dictyostelium discoideum embarks on a developmental program upon starvation. During development, extracellular oscillatory cAMP signaling orchestrates the chemotaxis-mediated aggregation of ∼105 amoebae and is required for optimal induction of so-called pulse-induced genes. This requirement for pulsatile CAMP reflects adaptation of the cAMP-receptor-mediated pathways that regulate these genes. Through examination of a collection of pulse-induced genes, we defined two distinct gene classes based on their induction kinetics and the impact of mutations that impair PKA signaling. The first class (represented by D2 and prtA) is highly dependent on PKA signaling, whereas the second class (represented by carA, gpaB, and acaA) is not. Analysis of expression kinetics revealed that these classes are sequentially expressed with the PKA-independent genes peaking in expression before the PKA-dependent class. Experiments with cycloheximide, an inhibitor of translation, demonstrated that the pulse induction of both classes depends on new protein synthesis early in development. carA and gpaB also exhibit pulse-independent, starvation-induced expression which, unlike their pulse induction, was found to be insensitive to cycloheximide added at the outset of starvation. This result indicates that the mechanism of starvation induction pre-exists in growing cells and is distinct from the pulse induction mechanism for these genes. In order to identify cis-acting elements that are critical for induction of carA, we constructed a GFP reporter controlled by a 914-base-pair portion of its promoter and verified that its expression was PKA-independent, pulse-inducible, and developmentally regulated like the endogenous carA gene. By a combination of truncation, internal deletion, and site-directed mutation, we defined several distinct functional elements within the carA promoter, including a 39-bp region required for pulse induction between base pairs -321 and -282 (relative to the transcription start site), a 131-bp region proximal to the start site that is sufficient for starvation induction, and two separate enhancer domains. Identification of factors that interact with these promoter elements and genetic approaches exploiting the GFP reporter described here should help complete our understanding of the mechanisms regulating these genes, including adaptation mechanisms that likely also govern chemotaxis of Dictyostelium and mammalian cells. ^

Identification and analysis of novel mitotic inhibitors of the Caenorhabditis elegans Aurora B kinase

Proper execution of mitosis requires the accurate segregation of replicated DNA into each daughter cell. The highly conserved mitotic kinase AIR-2/Aurora B is a dynamic protein that interacts with subsets of cofactors and substrates to coordinate chromosome segregation and cytokinesis in Caenorhabdiris elegans. To identify components of the AIR-2 regulatory pathway, a genome-wide RNAi-based screen for suppressors of air-2 temperature-sensitive mutant lethality was conducted. Here, I present evidence that two classes of suppressors identified in this screen are bona fide regulators of the AIR-2 kinase. The strongest suppressor cdc-48.3, encodes an Afg2/Spaf-related Cdc48-like AAA+ ATPase that regulates AIR-2 kinase activity and stability during C. elegans embryogenesis. Loss of CDC-48.3 suppresses the lethality of air-2 mutant embryos, marked by the restoration of the dynamic behavior of AIR-2 and rescue of chromosome segregation and cytokinesis defects. Loss of CDC-48.3 leads to mitotic delays and abnormal accumulation of AIR-2 during late telophase/mitotic exit. In addition, AIR-2 kinase activity is significantly upregulated from metaphase through mitotic exit in CDC-48.3 depleted embryos. Inhibition of the AIR-2 kinase is dependent on (1) a direct physical interaction between CDC-48.3 and AIR-2, and (2) CDC-48.3 ATPase activity. Importantly, the increase in AIR-2 kinase activity does not correlate with the stabilization of AIR-2 in late mitosis. Hence, CDC-48.3 is a bi-functional inhibitor of AIR-2 that is likely to act via distinct mechanisms. The second class of suppressors consists of psy-2/smk-1 and pph-4.1, which encode two components of the conserved PP4 phosphatase complex that is essential for spindle assembly, chromosome segregation, and overall mitotic progression. AIR-2 and its substrates are likely to be targets of this complex since mitotic AIR-2 kinase activity is significantly increased during mitosis when either PSY-2/SMK-1 or PPH-4.l is depleted. Altogether, this study demonstrates that during the C. elegans embryonic cell cycle, regulators including the CDC-48.3 ATPase and PP4 phosphatase complex interact with and control the kinase activity, targeting behavior and protein stability of the Aurora B kinase to ensure accurate and timely progression of mitosis. ^