Sample matching by inferred agonal stress in gene expression analyses of the brain

<p>Abstract</p> <p>Background</p> <p>Gene expression patterns in the brain are strongly influenced by the severity and duration of physiological stress at the time of death. This agonal effect, if not well controlled, can lead to spurious findings and diminished statistical power in case-control comparisons. While some recent studies match samples by tissue pH and clinically recorded agonal conditions, we found that these indicators were sometimes at odds with observed stress-related gene expression patterns, and that matching by these criteria still sometimes results in identifying case-control differences that are primarily driven by residual agonal effects. This problem is analogous to the one encountered in genetic association studies, where self-reported race and ethnicity are often imprecise proxies for an individual's actual genetic ancestry.</p> <p>Results</p> <p>We developed an Agonal Stress Rating (ASR) system that evaluates each sample's degree of stress based on gene expression data, and used ASRs in <it>post hoc </it>sample matching or covariate analysis. While gene expression patterns are generally correlated across different brain regions, we found strong region-region differences in empirical ASRs in many subjects that likely reflect inter-individual variabilities in local structure or function, resulting in region-specific vulnerability to agonal stress.</p> <p>Conclusion</p> <p>Variation of agonal stress across different brain regions differs between individuals, revealing a new level of complexity for gene expression studies of brain tissues. The Agonal Stress Ratings quantitatively assess each sample's extent of regulatory response to agonal stress, and allow a strong control of this important confounder.</p

Analyzing Patient Trajectories With Artificial Intelligence

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery

The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques

New perspectives and applications for greedy algorithms in machine learning

Approximating probability densities is a core problem in Bayesian statistics, where the inference involves the computation of a posterior distribution. Variational Inference (VI) is a technique to approximate posterior distributions through optimization. It involves specifying a set of tractable densities, out of which the final approximation is to be chosen. While VI is traditionally motivated with the goal of tractability, the focus in this dissertation is to use Bayesian approximation to obtain parsimonious distributions. With this goal in mind, we develop greedy algorithm variants and study their theoretical properties by establishing novel connections of the resulting optimization problems in parsimonious VI with traditional studies in the discrete optimization literature. Specific realizations lead to efficient solutions for many sparse probabilistic models like Sparse regression, Sparse PCA, Sparse Collective Matrix Factorization (CMF) etc. For cases where existing results are insufficient to provide acceptable approximation guarantees, we extend the optimization results for some large scale algorithms to a much larger class of functions.The developed methods are applied to both simulated and real world datasets, including high dimensional functional Magnetic Resonance Imaging (fMRI) datasets, and to the real world tasks of interpreting data exploration and model predictions.Electrical and Computer Engineerin

Ion channels and the tree of life

The field of comparative neurobiology has deep roots. I will begin by giving an overview of the parts of its history that I feel are most relevant for this dissertation. Within this history lies a wealth of zoological research and penetrating theories that are underutilized by modern evolutionary biologists. The age of whole-genome sequencing provides a perfect opportunity to revisit and perhaps update this corpus to better understand the phylogenetic history of organismal behavior. The first three chapters of my dissertation will be case studies on the evolution of sodium-selective ion channels. Sodium channels are responsible for much of the electrical signaling in animal nervous systems and muscles, but their evolutionary relationships have not yet been explored with the modern tools of phylogenetics and comparative genomics. Chapter 1 will deal with the classic Nav channels which create action potentials in nerves and muscles. There I will show that this gene family pre-dates the nervous system and even animal multicellularity. Chapter two will investigate sodium leak channels, which likley create the leak conductance measured by Hodgkin and Huxley. These channels turn out to be close relatives of fungal calcium channels, a relationship which illuminates the evolution of both groups. Chapter three is on bacterial sodium channels and their use as models for other sodium channel types. The final chapter will turn away from sodium channels in particular and discuss the evolution of animal nervous systems by means of ion channel genomics. In that chapter I will show that the genomic complements of ion channels that animals with nervous systems possess evolved independently to large degree, and that the early evolution of nervous systems also involved periods of gene loss. I will end with a more general discussion of convergent evolution, a key theme of this dissertation, and its effect on comparative analyses in the age of genomics.Ecology, Evolution and Behavio

Facial expression of pain: an evolutionary account.

This paper proposes that human expression of pain in the presence or absence of caregivers, and the detection of pain by observers, arises from evolved propensities. The function of pain is to demand attention and prioritise escape, recovery, and healing; where others can help achieve these goals, effective communication of pain is required. Evidence is reviewed of a distinct and specific facial expression of pain from infancy to old age, consistent across stimuli, and recognizable as pain by observers. Voluntary control over amplitude is incomplete, and observers can better detect pain that the individual attempts to suppress rather than amplify or simulate. In many clinical and experimental settings, the facial expression of pain is incorporated with verbal and nonverbal vocal activity, posture, and movement in an overall category of pain behaviour. This is assumed by clinicians to be under operant control of social contingencies such as sympathy, caregiving, and practical help; thus, strong facial expression is presumed to constitute and attempt to manipulate these contingencies by amplification of the normal expression. Operant formulations support skepticism about the presence or extent of pain, judgments of malingering, and sometimes the withholding of caregiving and help. To the extent that pain expression is influenced by environmental contingencies, however, "amplification" could equally plausibly constitute the release of suppression according to evolved contingent propensities that guide behaviour. Pain has been largely neglected in the evolutionary literature and the literature on expression of emotion, but an evolutionary account can generate improved assessment of pain and reactions to it