A Particle Filter Approach to Multiprocess Dynamic Models with Application to Hormone Data

We extend the multiprocess dynamic models to the general non-Gaussian and nonlinear setting. Under this framework, we propose specific models to simultaneously model hormone smooth basal trend and pulsatile activities. The pulse input is modeled by two processes: one as a point mass at zero and one as a gamma distributed random variable. This gamma-driven approach ensures the pulse estimates to be nonnegative, which is an intrinsic characteristic of hormone dynamics. The smooth trend is modeled by smoothing splines. Both additive and multiplicative observational errors are investigated. Parameters are estimated by maximizing the marginal likelihood. Baseline and pulses are estimated by posterior means. For implementation, particle filter is adopted. Unlike the traditional condensation method where a single distribution is used to approximate a mixture of distributions, this particle filter approach allows the model components to be accurately evaluated at the expense of computational resources. The specific models are applied to a cortisol series. The finite sample performance is evaluated by a simulation. The data application and the simulation show that the biological characteristics can be incorporated and be accurately estimated under the proposed framework

Government Responses Matter: Predicting Covid-19 cases in US under an empirical Bayesian time series framework

Since the Covid-19 outbreak, researchers have been predicting how the epidemic will evolve, especially the number in each country, through using parametric extrapolations based on the history. In reality, the epidemic progressing in a particular country depends largely on its policy responses and interventions. Since the outbreaks in some countries are earlier than United States, the prediction of US cases can benefit from incorporating the similarity in their trajectories. We propose an empirical Bayesian time series framework to predict US cases using different countries as prior reference. The resultant forecast is based on observed US data and prior information from the reference country while accounting for different population sizes. When Italy is used as prior in the prediction, which the US data resemble the most, the cases in the US will exceed 300,000 by the beginning of April unless strong measures are adopted

Modeling diurnal hormone profiles by hierarchical state space models.

Adrenocorticotropic hormone (ACTH) diurnal patterns contain both smooth circadian rhythms and pulsatile activities. How to evaluate and compare them between different groups is a challenging statistical task. In particular, we are interested in testing 1) whether the smooth ACTH circadian rhythms in chronic fatigue syndrome and fibromyalgia patients differ from those in healthy controls, and 2) whether the patterns of pulsatile activities are different. In this paper, a hierarchical state space model is proposed to extract these signals from noisy observations. The smooth circadian rhythms shared by a group of subjects are modeled by periodic smoothing splines. The subject level pulsatile activities are modeled by autoregressive processes. A functional random effect is adopted at the pair level to account for the matched pair design. Parameters are estimated by maximizing the marginal likelihood. Signals are extracted as posterior means. Computationally efficient Kalman filter algorithms are adopted for implementation. Application of the proposed model reveals that the smooth circadian rhythms are similar in the two groups but the pulsatile activities in patients are weaker than those in the healthy controls

fmixed: A SAS Macro for Smoothing-Spline-Based Functional Mixed Effects Models

In this article we implement the smoothing-spline-based functional mixed effects models (Guo 2002) by a SAS macro by exploiting the connection between mixed effects models and smoothing splines. The macro can handle flexible design matrices and is easy to use. Input parameters and output results are described and explained. A numeric example and a real data example are used for illustration

State policy environment and the dental safety net: a case study of professional practice environments’ effect on dental service availability in Federally Qualified Health Centers

Objectives To determine whether and to what extent the state policy environment for the dental hygiene workforce affects the availability of dental services at Federally Qualified Health Centers (FQHCs). Methods We examined data drawn from the Uniform Data System on 1,135 unique FQHC grantees receiving community health center funding from the U.S. Health Center program between 2004 and 2012. The Dental Hygiene Professional Practice Index was used to quantify variations in state policy environment. We then examined the influence of state policy environment on the availability of dental care through generalized linear mixed-effects models. Results Approximately 80% of FQHCs reported delivering dental services. We consistently observed that FQHCs with favorable levels of state support had the highest proportion of FQHCs that delivered dental services, even more so than FQHCs with extremely high support. FQHCs located in the most restrictive states had 0.28 the odds of delivering dental services as did those located in the most supportive states. Conclusions The state policy environment for the dental hygiene workforce is likely associated with the availability of dental services at FQHCs. The greatest proportion of FQHCs delivering dental services was found in states with policy provisions supporting professional independence in public health settings. Nevertheless, additional research is needed to understand the specific mechanism by which these policies affect FQHCs

Duration-dependent effects of clinically relevant oral alendronate doses on cortical bone toughness in beagle dogs

Bisphosphonates (BPs) have been shown to significantly reduce bone toughness in vertebrae within one year when given at clinical doses to dogs. Although BPs also reduce toughness in the cortical bone when given at high doses, their effect on cortical bone material properties when given at clinical doses is less clear. In part, this may be due to the use of small sample sizes that were powered to demonstrate differences in bone mineral density rather than the bone's material properties. Our lab has conducted several studies in which dogs were treated with alendronate at a clinically relevant dose. The goal of this study was to examine these published and unpublished data collectively to determine whether there is a significant time-dependent effect of alendronate on toughness of the cortical bone. This analysis seemed particularly relevant given the recent occurrence of atypical femoral fractures in humans. Differences in the toughness of ribs taken from dogs derived from five separate experiments were measured. The dogs were orally administered saline (CON, 1ml/kg/day) or alendronate (ALN) at a clinical dose (0.2mg/kg/day). Treatment duration ranged from 3months to 3years. Groups were compared using ANOVA, and time trends analyzed with linear regression analysis. Linear regressions of the percent difference in toughness between CON and ALN at each time point revealed a significant reduction in toughness with longer exposure to ALN. The downward trend was primarily driven by a downward trend in post-yield toughness, whereas toughness in the pre-yield region was not changed relative to CON. These data suggest that a longer duration of treatment with clinical doses of ALN results in deterioration of cortical bone toughness in a time-dependent manner. As the duration of treatment is lengthened, the cortical bone exhibits increasingly brittle behavior. This may be important in assessing the role that long-term BP treatments play in the risk of atypical fractures of the femoral cortical bone in humans.AR047838 AR62002 National Osteoporosis Foundation Research Facilities Improvement Program Grant Number C06RR10601 NIH National Center for Research Resource

Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions

Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge not only in natural language tasks, but also in some vision-language tasks such as open-domain knowledge-based visual question answering (OK-VQA). As images are invisible to LLMs, researchers convert images to text to engage LLMs into the visual question reasoning procedure. This leads to discrepancies between images and their textual representations presented to LLMs, which consequently impedes final reasoning performance. To fill the information gap and better leverage the reasoning capability, we design a framework that enables LLMs to proactively ask relevant questions to unveil more details in the image, along with filters for refining the generated information. We validate our idea on OK-VQA and A-OKVQA. Our method continuously boosts the performance of baselines methods by an average gain of 2.15% on OK-VQA, and achieves consistent improvements across different LLMs.Comment: Accepted to EMNLP2023 Finding