Learning about pain from others: an observational learning account

Although direct experience and verbal instruction are important sources in the development of pain-related beliefs and behaviors, accumulating evidence indicates that observation of others in pain may be equally as important. Taking a contemporary view on learning as a starting point, we discuss available evidence on observational learning in the context of pain, highlight its importance for both development and management of chronic pain problems, and discuss potential moderators of observational learning effects. We argue that the capacity to understand and appreciate the experience of another person is fundamental to observational learning, including use of this information to establish the association between pain and antecedent or consequent stimuli. A main objective of this paper is to stimulate research on the role of learning about pain from others. Several lines for further research, including clinical applications, are delineated. Perspective: Based upon a contemporary view on learning, this focus article delineates how pain-related beliefs and behaviors may be learnt by observing others. It is discussed how further research on the acquisition of pain-related beliefs/behaviors might further our understanding of pain and disability. (PsycINFO Database Record (c) 2011 APA, all rights reserved) (journal abstract

The First Cellular Models Based on Frataxin Missense Mutations That Reproduce Spontaneously the Defects Associated with Friedreich Ataxia

BACKGROUND:Friedreich ataxia (FRDA), the most common form of recessive ataxia, is due to reduced levels of frataxin, a highly conserved mitochondrial iron-chaperone involved in iron-sulfur cluster (ISC) biogenesis. Most patients are homozygous for a (GAA)(n) expansion within the first intron of the frataxin gene. A few patients, either with typical or atypical clinical presentation, are compound heterozygous for the GAA expansion and a micromutation. METHODOLOGY:We have developed a new strategy to generate murine cellular models for FRDA: cell lines carrying a frataxin conditional allele were used in combination with an EGFP-Cre recombinase to create murine cellular models depleted for endogenous frataxin and expressing missense-mutated human frataxin. We showed that complete absence of murine frataxin in fibroblasts inhibits cell division and leads to cell death. This lethal phenotype was rescued through transgenic expression of human wild type as well as mutant (hFXN(G130V) and hFXN(I154F)) frataxin. Interestingly, cells expressing the mutated frataxin presented a FRDA-like biochemical phenotype. Though both mutations affected mitochondrial ISC enzymes activities and mitochondria ultrastructure, the hFXN(I154F) mutant presented a more severe phenotype with affected cytosolic and nuclear ISC enzyme activities, mitochondrial iron accumulation and an increased sensitivity to oxidative stress. The differential phenotype correlates with disease severity observed in FRDA patients. CONCLUSIONS:These new cellular models, which are the first to spontaneously reproduce all the biochemical phenotypes associated with FRDA, are important tools to gain new insights into the in vivo consequences of pathological missense mutations as well as for large-scale pharmacological screening aimed at compensating frataxin deficiency

Quantifying Individual Variation in the Propensity to Attribute Incentive Salience to Reward Cues

If reward-associated cues acquire the properties of incentive stimuli they can come to powerfully control behavior, and potentially promote maladaptive behavior. Pavlovian incentive stimuli are defined as stimuli that have three fundamental properties: they are attractive, they are themselves desired, and they can spur instrumental actions. We have found, however, that there is considerable individual variation in the extent to which animals attribute Pavlovian incentive motivational properties (“incentive salience”) to reward cues. The purpose of this paper was to develop criteria for identifying and classifying individuals based on their propensity to attribute incentive salience to reward cues. To do this, we conducted a meta-analysis of a large sample of rats (N = 1,878) subjected to a classic Pavlovian conditioning procedure. We then used the propensity of animals to approach a cue predictive of reward (one index of the extent to which the cue was attributed with incentive salience), to characterize two behavioral phenotypes in this population: animals that approached the cue (“sign-trackers”) vs. others that approached the location of reward delivery (“goal-trackers”). This variation in Pavlovian approach behavior predicted other behavioral indices of the propensity to attribute incentive salience to reward cues. Thus, the procedures reported here should be useful for making comparisons across studies and for assessing individual variation in incentive salience attribution in small samples of the population, or even for classifying single animals

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)

Clinically applicable deep learning for diagnosis and referral in retinal disease

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting

Protocole de simulation du mouvement dentaire analysé par la méthode des éléments finis

International audienc

Protocole de simulation du mouvement dentaire analysé par la méthode des éléments finis

International audienc

Un nouveau protocole par éléments finis pour une simulation clinique

International audienc

The Kindness of Time

The Kindness of Time is a one hour radio drama that maps the journey of Ben Ellison as he tracks across a snow-covered Europe in search of happiness