Preoperative pain level and patient expectation predict hospital length of stay after total hip arthroplasty.

The purpose of this study was to identify preoperative predictors of length of stay after primary total hip arthroplasty in a patient population reflecting current trends toward shorter hospitalization and using readily obtainable factors that do not require scoring systems. A retrospective review of 112 consecutive patients was performed. High preoperative pain level and patient expectation of discharge to extended care facilities (ECFs) were the only significant multivariable predictors of hospitalization extending beyond 2 days (P=0.001 and P<0.001 respectively). Patient expectation remained significant after adjusting for Medicare's 3-day requirement for discharge to ECFs (P<0.001). The study was adequately powered to analyze the variables in the multivariable logistic regression model, which had a concordance index of 0.857.

Patient expectation is the most important predictor of discharge destination after primary total joint arthroplasty.

The purpose of this study was to identify preoperative predictors of discharge destination after total joint arthroplasty. A retrospective study of three hundred and seventy-two consecutive patients who underwent primary total hip and knee arthroplasty was performed. The mean length of stay was 2.9 days and 29.0% of patients were discharged to extended care facilities. Age, caregiver support at home, and patient expectation of discharge destination were the only significant multivariable predictors regardless of the type of surgery (total knee versus total hip arthroplasty). Among those variables, patient expectation was the most important predictor (P < 0.001; OR 169.53). The study was adequately powered to analyze the variables in the multivariable logistic regression model, which had a high concordance index of 0.969.

Expectation Modulates Episodic Memory Formation via Dopaminergic Circuitry

Episodic memory formation is shaped by expectation. Events that generate expectations have the capacity to influence memory. Additionally, whether subsequent events meet or violate expectations has consequences for memory. However, clarification is still required to illuminate the circumstances and direction of memory modulation. In the brain, the mechanisms by which expectation modulates memory formation also require consideration. The dopamine system has been implicated in signaling events associated with different states of expectancy; it has also been shown to modulate episodic memory formation in the hippocampus. Thus, the studies included in this dissertation utilized both functional magnetic resonance imaging (fMRI) and behavioral testing to examine when and how the dopaminergic system supports the modulation of memory by expectation. The work aimed to characterize the activation of dopaminergic circuitry in response to cues that generate expectancy, during periods of anticipation, and in response to outcomes that resolve expectancy. The studies also examined how each of these event types influenced episodic memory formation. The present findings demonstrated that novelty and expectancy violation both drive dopaminergic circuitry capable of contributing to memory formation. Consistent with elevated dopaminergic midbrain and hippocampus activation for each, expected versus expectancy violating novelty did not differentially affect memory success. We also showed that high curiosity expectancy states drive memory formation. This was supported by activation in dopaminergic circuitry that was greater for subsequently remembered information only in the high curiosity state. Finally, we showed that cues that generate high expected reward value versus high reward uncertainty differentially modulate memory formation during reward anticipation. This behavioral result was consistent with distinct temporal profiles of dopaminergic action having differential downstream effects on episodic memory formation. Integrating the present studies with previous research suggests that dopaminergic circuitry signals events that are unpredicted, whether cuing or resolving expectations. It also suggests that contextual differences change the contribution of the dopaminergic system during anticipation, depending on the nature of the expectation. And finally, this work is consistent with a model in which dopamine elevation in response to expectancy events positively modulates episodic memory formation.

A mathematical theory of stochastic microlensing. II. Random images, shear, and the Kac-Rice formula

Continuing our development of a mathematical theory of stochastic microlensing, we study the random shear and expected number of random lensed images of different types. In particular, we characterize the first three leading terms in the asymptotic expression of the joint probability density function (pdf) of the random shear tensor due to point masses in the limit of an infinite number of stars. Up to this order, the pdf depends on the magnitude of the shear tensor, the optical depth, and the mean number of stars through a combination of radial position and the star's mass. As a consequence, the pdf's of the shear components are seen to converge, in the limit of an infinite number of stars, to shifted Cauchy distributions, which shows that the shear components have heavy tails in that limit. The asymptotic pdf of the shear magnitude in the limit of an infinite number of stars is also presented. All the results on the random microlensing shear are given for a general point in the lens plane. Extending to the general random distributions (not necessarily uniform) of the lenses, we employ the Kac-Rice formula and Morse theory to deduce general formulas for the expected total number of images and the expected number of saddle images. We further generalize these results by considering random sources defined on a countable compact covering of the light source plane. This is done to introduce the notion of global expected number of positive parity images due to a general lensing map. Applying the result to microlensing, we calculate the asymptotic global expected number of minimum images in the limit of an infinite number of stars, where the stars are uniformly distributed. This global expectation is bounded, while the global expected number of images and the global expected number of saddle images diverge as the order of the number of stars. © 2009 American Institute of Physics.

Defective lymphocyte chemotaxis in beta-arrestin2- and GRK6-deficient mice.

Lymphocyte chemotaxis is a complex process by which cells move within tissues and across barriers such as vascular endothelium and is usually stimulated by chemokines such as stromal cell-derived factor-1 (CXCL12) acting via G protein-coupled receptors. Because members of this receptor family are regulated ("desensitized") by G protein-coupled receptor kinase (GRK)-mediated receptor phosphorylation and beta-arrestin binding, we examined signaling and chemotactic responses in splenocytes derived from knockout mice deficient in various beta-arrestins and GRKs, with the expectation that these responses might be enhanced. Knockouts of beta-arrestin2, GRK5, and GRK6 were examined because all three proteins are expressed at high levels in purified mouse CD3+ T and B220+ B splenocytes. CXCL12 stimulation of membrane GTPase activity was unaffected in splenocytes derived from GRK5-deficient mice but was increased in splenocytes from the beta-arrestin2- and GRK6-deficient animals. Surprisingly, however, both T and B cells from beta-arrestin2-deficient animals and T cells from GRK6-deficient animals were strikingly impaired in their ability to respond to CXCL12 both in transwell migration assays and in transendothelial migration assays. Chemotactic responses of lymphocytes from GRK5-deficient mice were unaffected. Thus, these results indicate that beta-arrestin2 and GRK6 actually play positive regulatory roles in mediating the chemotactic responses of T and B lymphocytes to CXCL12.

Cross-Domain Multitask Learning with Latent Probit Models

Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain transforms, and the shared probit classifier in the common domain. To learn meaningful task relatedness and avoid over-fitting in classification, we introduce sparsity in the domain transforms matrices, as well as in the common classifier. We derive theoretical bounds for the estimation error of the classifier in terms of the sparsity of domain transforms. An expectation-maximization algorithm is derived for learning the LPM. The effectiveness of the approach is demonstrated on several real datasets.

People who expect to enter psychotherapy are prone to believing that they have forgotten memories of childhood trauma and abuse.

We asked 1004 undergraduates to estimate both the probability that they would enter therapy and the probability that they experienced but could not remember incidents of potentially life-threatening childhood traumas or physical and sexual abuse. We found a linear relation between the expectation of entering therapy and the belief that one had, but cannot now remember, childhood trauma and abuse. Thus individuals who are prone to seek psychotherapy are also prone to accept a suggested memory of childhood trauma or abuse as fitting their expectations. In multiple regressions predicting the probability of forgotten memories of childhood traumas and abuse, the expectation of entering therapy remained as a substantial predictor when self-report measures of mood, anxiety, post-traumatic stress disorder symptom severity, and trauma exposure were included.

Opportunistic Control Over Shared Wireless Channels

© 2015 IEEE.We consider a wireless control architecture with multiple control loops over a shared wireless medium. A scheduler observes the random channel conditions that each control system experiences over the shared medium and opportunistically selects systems to transmit at a set of non-overlapping frequencies. The transmit power of each system also adapts to channel conditions and determines the probability of successfully receiving and closing the loop. We formulate the optimal design of channel-aware scheduling and power allocation that minimize the total power consumption while meeting control performance requirements for all systems. In particular, it is required that for each control system a given Lyapunov function decreases at a specified rate in expectation over the random channel conditions. We develop an offline algorithm to find the optimal communication design, as well as an online protocol which selects scheduling and power variables based on a random observed channel sequence and converges almost surely to the optimal operating point. Simulations illustrate the power savings of our approach compared to other non-channel-aware schemes.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.