Associating GWAS Information with the Notch Signaling Pathway Using Transcription Profiling

Genome-wide association studies (GWAS) have identified SNPs associated with breast cancer. However, they offer limited insights about the biological mechanisms by which SNPs confer risk. We investigated the association of GWAS information with a major oncogenic pathway in breast cancer, the Notch signaling pathway. We first identified 385 SNPs and 150 genes associated with risk for breast cancer by mining data from 41 GWAS. We then investigated their expression, along with 32 genes involved in the Notch signaling pathway using two publicly available gene expression data sets from the Caucasian (42 cases and 143 controls) and Asian (43 cases and 43 controls) populations. Pathway prediction and network modeling confirmed that Notch receptors and genes involved in the Notch signaling pathway interact with genes containing SNPs associated with risk for breast cancer. Additionally, we identified other SNP-associated biological pathways relevant to breast cancer, including the P53, apoptosis and MAP kinase pathways

Searching for an anchor in an unpredictable world: A computational model of obsessive compulsive disorder

In this article, we develop a computational model of obsessive–compulsive disorder (OCD). We propose that OCD is characterized by a difficulty in relying on past events to predict the consequences of patients’ own actions and the unfolding of possible events. Clinically, this corresponds both to patients’ difficulty in trusting their own actions (and therefore repeating them), and to their common preoccupation with unlikely chains of events. Critically, we develop this idea on the basis of the well-developed framework of the Bayesian brain, where this impairment is formalized as excessive uncertainty regarding state transitions. We illustrate the validity of this idea using quantitative simulations and use these to form specific empirical predictions. These predictions are evaluated in relation to existing evidence, and are used to delineate directions for future research. We show how seemingly unrelated findings and phenomena in OCD can be explained by the model, including a persistent experience that actions were not adequately performed and a tendency to repeat actions; excessive information gathering (i.e., checking); indecisiveness and pathological doubt; overreliance on habits at the expense of goal-directed behavior; and overresponsiveness to sensory stimuli, thoughts, and feedback. We discuss the relationship and interaction between our model and other prominent models of OCD, including models focusing on harm-avoidance, not-just-right experiences, or impairments in goal-directed behavior. Finally, we outline potential clinical implications and suggest lines for future research

Suprathreshold heat pain response predicts activity-related pain, but not rest-related pain, in an exercise-induced injury model

© 2014 Coronado et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Exercise-induced injury models are advantageous for studying pain since the onset of pain is controlled and both pre-injury and post-injury factors can be utilized as explanatory variables or predictors. In these studies, rest-related pain is often considered the primary dependent variable or outcome, as opposed to a measure of activity-related pain. Additionally, few studies include pain sensitivity measures as predictors. In this study, we examined the influence of pre-injury and post-injury factors, including pain sensitivity, for induced rest and activity-related pain following exercise induced muscle injury. The overall goal of this investigation was to determine if there were convergent or divergent predictors of rest and activityrelated pain. One hundred forty-three participants provided demographic, psychological, and pain sensitivity information and underwent a standard fatigue trial of resistance exercise to induce injury of the dominant shoulder. Pain at rest and during active and resisted shoulder motion were measured at 48- and 96-hours post-injury. Separate hierarchical models were generated for assessing the influence of pre-injury and post-injury factors on 48- and 96-hour rest-related and activityrelated pain. Overall, we did not find a universal predictor of pain across all models. However, pre-injury and post-injury suprathreshold heat pain response (SHPR), a pain sensitivity measure, was a consistent predictor of activity-related pain, even after controlling for known psychological factors. These results suggest there is differential prediction of pain. A measure of pain sensitivity such as SHPR appears more influential for activity-related pain, but not rest-related pain, and may reflect different underlying processes involved during pain appraisal

Active Inference and Auditory Hallucinations

Auditory verbal hallucinations (AVH) are often distressing symptoms of several neuropsychiatric conditions, including schizophrenia. Using a Markov decision process formulation of active inference, we develop a novel model of AVH as false (positive) inference. Active inference treats perception as a process of hypothesis testing, in which sensory data are used to disambiguate between alternative hypotheses about the world. Crucially, this depends upon a delicate balance between prior beliefs about unobserved (hidden) variables and the sensations they cause. A false inference that a voice is present, even in the absence of auditory sensations, suggests that prior beliefs dominate perceptual inference. Here we consider the computational mechanisms that could cause this imbalance in perception. Through simulation, we show that the content of (and confidence in) prior beliefs depends on beliefs about policies (here sequences of listening and talking) and on beliefs about the reliability of sensory data. We demonstrate several ways in which hallucinatory percepts could occur when an agent expects to hear a voice in the presence of imprecise sensory data. This model expresses, in formal terms, alternative computational mechanisms that underwrite AVH and, speculatively, can be mapped onto neurobiological changes associated with schizophrenia. The interaction of action and perception is important in modeling AVH, given that speech is a fundamentally enactive and interactive process-and that hallucinators often actively engage with their voices

Deep Active Inference for Partially Observable MDPs

Deep active inference has been proposed as a scalable approach to perception and action that deals with large policy and state spaces. However, current models are limited to fully observable domains. In this paper, we describe a deep active inference model that can learn successful policies directly from high-dimensional sensory inputs. The deep learning architecture optimizes a variant of the expected free energy and encodes the continuous state representation by means of a variational autoencoder. We show, in the OpenAI benchmark, that our approach has comparable or better performance than deep Q-learning, a state-of-the-art deep reinforcement learning algorithm.Comment: 1st International Workshop on Active inference, European Conference on Machine Learning (ECML/PCKDD 2020

Inflammation in sputum relates to progression of disease in subjects with COPD: a prospective descriptive study

BACKGROUND: Inflammation is considered to be of primary pathogenic importance in COPD but the evidence on which current understanding is based does not distinguish between cause and effect, and no single mechanism can account for the complex pathology. We performed a prospective longitudinal study of subjects with COPD that related markers of sputum inflammation at baseline to subsequent disease progression. METHODS: A cohort of 56 patients with chronic bronchitis was characterized in the stable state at baseline and after an interval of four years, using physiological measures and CT densitometry. Sputum markers of airway inflammation were quantified at baseline from spontaneously produced sputum in a sub-group (n = 38), and inflammation severity was related to subsequent disease progression. RESULTS: Physiological and CT measures indicated disease progression in the whole group. In the sub-group, sputum myeloperoxidase correlated with decline in FEV(1 )(rs = -0.344, p = 0.019, n = 37). LTB4 and albumin leakage correlated with TLCO decline (rs = -0.310, p = 0.033, rs = -0.401, p = 0.008, respectively, n = 35) and IL-8 correlated with progression of lung densitometric indices (rs = -0.464, p = 0.005, n = 38). CONCLUSION: The data support a principal causative role for neutrophilic inflammation in the pathogenesis of COPD and suggest that the measurement of sputum inflammatory markers may have a predictive role in clinical practice

EquiFACS: the Equine Facial Action Coding System

Although previous studies of horses have investigated their facial expressions in specific contexts, e.g. pain, until now there has been no methodology available that documents all the possible facial movements of the horse and provides a way to record all potential facial configurations. This is essential for an objective description of horse facial expressions across a range of contexts that reflect different emotional states. Facial Action Coding Systems (FACS) provide a systematic methodology of identifying and coding facial expressions on the basis of underlying facial musculature and muscle movement. FACS are anatomically based and document all possible facial movements rather than a configuration of movements associated with a particular situation. Consequently, FACS can be applied as a tool for a wide range of research questions. We developed FACS for the domestic horse (Equus caballus) through anatomical investigation of the underlying musculature and subsequent analysis of naturally occurring behaviour captured on high quality video. Discrete facial movements were identified and described in terms of the underlying muscle contractions, in correspondence with previous FACS systems. The reliability of others to be able to learn this system (EquiFACS) and consistently code behavioural sequences was high—and this included people with no previous experience of horses. A wide range of facial movements were identified, including many that are also seen in primates and other domestic animals (dogs and cats). EquiFACS provides a method that can now be used to document the facial movements associated with different social contexts and thus to address questions relevant to understanding social cognition and comparative psychology, as well as informing current veterinary and animal welfare practices

Reading faces: differential lateral gaze bias in processing canine and human facial expressions in dogs and 4-year-old children

Sensitivity to the emotions of others provides clear biological advantages. However, in the case of heterospecific relationships, such as that existing between dogs and humans, there are additional challenges since some elements of the expression of emotions are species-specific. Given that faces provide important visual cues for communicating emotional state in both humans and dogs, and that processing of emotions is subject to brain lateralisation, we investigated lateral gaze bias in adult dogs when presented with pictures of expressive human and dog faces. Our analysis revealed clear differences in laterality of eye movements in dogs towards conspecific faces according to the emotional valence of the expressions. Differences were also found towards human faces, but to a lesser extent. For comparative purpose, a similar experiment was also run with 4-year-old children and it was observed that they showed differential processing of facial expressions compared to dogs, suggesting a species-dependent engagement of the right or left hemisphere in processing emotions

Classical kinetic energy, quantum fluctuation terms and kinetic-energy functionals

We employ a recently formulated dequantization procedure to obtain an exact expression for the kinetic energy which is applicable to all kinetic-energy functionals. We express the kinetic energy of an N-electron system as the sum of an N-electron classical kinetic energy and an N-electron purely quantum kinetic energy arising from the quantum fluctuations that turn the classical momentum into the quantum momentum. This leads to an interesting analogy with Nelson's stochastic approach to quantum mechanics, which we use to conceptually clarify the physical nature of part of the kinetic-energy functional in terms of statistical fluctuations and in direct correspondence with Fisher Information Theory. We show that the N-electron purely quantum kinetic energy can be written as the sum of the (one-electron) Weizsacker term and an (N-1)-electron kinetic correlation term. We further show that the Weizsacker term results from local fluctuations while the kinetic correlation term results from the nonlocal fluctuations. For one-electron orbitals (where kinetic correlation is neglected) we obtain an exact (albeit impractical) expression for the noninteracting kinetic energy as the sum of the classical kinetic energy and the Weizsacker term. The classical kinetic energy is seen to be explicitly dependent on the electron phase and this has implications for the development of accurate orbital-free kinetic-energy functionals. Also, there is a direct connection between the classical kinetic energy and the angular momentum and, across a row of the periodic table, the classical kinetic energy component of the noninteracting kinetic energy generally increases as Z increases.Comment: 10 pages, 1 figure. To appear in Theor Chem Ac

Neural Correlates of Face and Object Perception in an Awake Chimpanzee (Pan Troglodytes) Examined by Scalp-Surface Event-Related Potentials

BACKGROUND: The neural system of our closest living relative, the chimpanzee, is a topic of increasing research interest. However, electrophysiological examinations of neural activity during visual processing in awake chimpanzees are currently lacking. METHODOLOGY/PRINCIPAL FINDINGS: In the present report, skin-surface event-related brain potentials (ERPs) were measured while a fully awake chimpanzee observed photographs of faces and objects in two experiments. In Experiment 1, human faces and stimuli composed of scrambled face images were displayed. In Experiment 2, three types of pictures (faces, flowers, and cars) were presented. The waveforms evoked by face stimuli were distinguished from other stimulus types, as reflected by an enhanced early positivity appearing before 200 ms post stimulus, and an enhanced late negativity after 200 ms, around posterior and occipito-temporal sites. Face-sensitive activity was clearly observed in both experiments. However, in contrast to the robustly observed face-evoked N170 component in humans, we found that faces did not elicit a peak in the latency range of 150-200 ms in either experiment. CONCLUSIONS/SIGNIFICANCE: Although this pilot study examined a single subject and requires further examination, the observed scalp voltage patterns suggest that selective processing of faces in the chimpanzee brain can be detected by recording surface ERPs. In addition, this non-invasive method for examining an awake chimpanzee can be used to extend our knowledge of the characteristics of visual cognition in other primate species