Bayesian latent time joint mixed-effects model of progression in the Alzheimer's Disease Neuroimaging Initiative.

IntroductionWe characterize long-term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative.MethodsWe apply a latent time joint mixed-effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference.ResultsWe find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid-β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis.DiscussionThe latent time joint mixed-effects model can (1) uncover long-term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject-specific prognostic estimates of the time until onset of symptoms

Rho-GTPases in Embryonic Stem Cells

Michael S. Samuel and Michael F. Olso

Determination of high temperature strains using a PC based vision system

With the widespread availability of video digitizers and cheap personal computers, the use of computer vision as an experimental tool is becoming common place. These systems are being used to make a wide variety of measurements that range from simple surface characterization to velocity profiles. The Sub-Pixel Digital Image Correlation technique has been developed to measure full field displacement and gradients of the surface of an object subjected to a driving force. The technique has shown its utility by measuring the deformation and movement of objects that range from simple translation to fluid velocity profiles to crack tip deformation of solid rocket fuel. This technique has recently been improved and used to measure the surface displacement field of an object at high temperature. The development of a PC based Sub-Pixel Digital Image Correlation system has yielded an accurate and easy to use system for measuring surface displacements and gradients. Experiments have been performed to show the system is viable for measuring thermal strain

Marketing Tips for Small-scale, Local Honey Bee Keepers in Northwest Arkansas

The objective of this thesis was to gain market information for beekeepers regarding different honey bee products and to provide information about economic feasibility when produced on a small, local scale. Since cost-of-production information about operating an apiary is widely available, the focus of this work was on gaining marketing knowledge. One of the objectives of the surveys was to develop a better sense of what potential resellers of honey bee products considered locally produced. Another objective was to determine preferences for honey bee product packaging as well as bee pollination services. Using that feedback, a marketing plan for different niche markets can be developed for part-time beekeeping operations. The survey results pertaining to local retailers and end users in Northwest Arkansas in 2016 suggested a supply radius near 100 miles and a preference for small packaging in general. Least cost supply, and at least regional brand recognition were not deemed as important as ensuring locally sourced products that can be sold at a premium. Different niche markets revealed both similar and different priorities related to these marketing aspects

Predicting the course of Alzheimer's progression.

Alzheimer's disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer's dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer's Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only

Viterbi sparse spike detection

pre-printAccurate interpretation of seismic traveltimes and amplitudes in the exploration and global scales is complicated by the band-limited nature of seismic data. We discovered a stochastic method to reduce a seismic waveform into a most probable constituent spike train. Model waveforms were constructed from a set of candidate spike trains convolved with a source wavelet estimate. For each model waveform, a profile hidden Markov model (HMM) was constructed to represent the waveform as a stochastic generative model with a linear topology corresponding to a sequence of samples. Each match state in the HMM represented a sample in the model waveform, in which the amplitude was represented by a Gaussian distribution. Insert and delete states allowed the underlying source wavelet to dilate or contract, accounting for nonstationarity in the seismic data and errors in the source wavelet estimate. The Gaussian distribution characterizing each sample's amplitude accounted for random noise. The Viterbi algorithm was employed to simultaneously find the optimal nonlinear alignment between a model waveform and the seismic data and to assign a score to each candidate spike train. The most probable traveltimes and amplitudes were inferred from the alignments of the highest scoring models. The method required no implicit assumptions regarding the distribution of traveltimes and amplitudes; however, in practice, the solution set may be limited to mitigate the nonuniqueness of solutions and to reduce the computational effort. Our analyses found that the method can resolve closely spaced arrivals below traditional resolution limits and that traveltime estimates are robust in the presence of random noise and source wavelet errors. The method was particularly well suited to fine-scale interpretation problems such as thin bed interpretation, tying seismic images to well logs, and the analysis of anomalous waveforms in global seismology

Tissue-selective expression of a conditionally-active ROCK2-estrogen receptor fusion protein

The serine/threonine kinases ROCK1 and ROCK2 are central mediators of actomyosin contractile force generation that act downstream of the RhoA small GTP-binding protein. As a result, they have key roles in regulating cell morphology and proliferation, and have been implicated in numerous pathological conditions and diseases including hypertension and cancer. Here we describe the generation of a gene-targeted mouse line that enables CRE-inducible expression of a conditionally-active fusion between the ROCK2 kinase domain and the hormone-binding domain of a mutated estrogen receptor (ROCK2:ER). This two-stage system of regulation allows for tissue-selective expression of the ROCK2:ER fusion protein, which then requires administration of estrogen analogues such as tamoxifen or 4-hydroxytamoxifen to elicit kinase activity. This conditional gain-of-function system was validated in multiple tissues by crossing with mice expressing CRE recombinase under the transcriptional control of cytokeratin14 (K14), murine mammary tumor virus (MMTV) or cytochrome P450 Cyp1A1 (Ah) promoters, driving appropriate expression in the epidermis, mammary or intestinal epithelia respectively. Given the interest in ROCK signaling in normal physiology and disease, this mouse line will facilitate research into the consequences of ROCK activation that could be used to complement conditional knockout models