Prediction analysis for microbiome sequencing data

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension‐reduction structure in the model. An efficient Monte Carlo expectation‐maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.     

How placebo responses are formed: a learning perspective

Despite growing scientific interest in the placebo effect and increasing understanding of neurobiological mechanisms, theoretical conceptualization of the placebo effect remains poorly developed. Substantial mechanistic research on this phenomenon has proceeded with little guidance by any systematic theoretical paradigm. This review seeks to present a theoretical perspective on the formation of placebo responses. We focus on information processing, and argue that different kinds of learning along with individuals' genetic make-up evolved as the proximate cause for triggering behavioural and neural mechanisms that enable the formation of individual expectations and placebo responses. Conceptualizing the placebo effect in terms of learning offers the opportunity for facilitating scientific investigation with a significant impact on medical care.     

Is there a ‘plenhaptic’ function?

One approach to gauge the complexity of the computational problem underlying haptic perception is to determine the number of dimensions needed to describe it. In vision, the number of dimensions can be estimated to be seven. This observation raises the question of what is the number of dimensions needed to describe touch. Only with certain simplified representations of mechanical interactions can this number be estimated, because it is in general infinite. Organisms must be sensitive to considerably reduced subsets of all possible measurements. These reductions are discussed by considering the sensing apparatuses of some animals and the underlying mechanisms of two haptic illusions.     

Vision, touch and object manipulation in Senegal parrots Poicephalus senegalus

Parrots are exceptional among birds for their high levels of exploratory behaviour and manipulatory abilities. It has been argued that foraging method is the prime determinant of a bird's visual field configuration. However, here we argue that the topography of visual fields in parrots is related to their playful dexterity, unique anatomy and particularly the tactile information that is gained through their bill tip organ during object manipulation. We measured the visual fields of Senegal parrots Poicephalus senegalus using the ophthalmoscopic reflex technique and also report some preliminary observations on the bill tip organ in this species. We found that the visual fields of Senegal parrots are unlike those described hitherto in any other bird species, with both a relatively broad frontal binocular field and a near comprehensive field of view around the head. The behavioural implications are discussed and we consider how extractive foraging and object exploration, mediated in part by tactile cues from the bill, has led to the absence of visual coverage of the region below the bill in favour of more comprehensive visual coverage above the head.     

Neural cell adhesion molecule (NCAM-/-) null mice show impaired sensitization of the startle response

The neural cell adhesion molecule (NCAM) plays important roles in development of the nervous system and in synaptic plasticity and memory formation in the adult. The present study sought to further investigate the role of NCAM in learning by testing habituation and footshock sensitization learning of the startle response (SR) in NCAM null mutant (NCAM-/-) and wildtype littermate (NCAM+/+) mice. Whereas habituation is a form of non-associative learning, footshock sensitization is induced by rapid contextual fear conditioning. Habituation was tested by repetitive presentation of acoustic and tactile startle stimuli. Although NCAM-/- mice showed differences in sensitivity in both stimulus modalities, habituation learning was intact in NCAM-/- mice, suggesting that NCAM does not play a role in the mechanisms underlying synaptic plasticity in the startle pathway. Footshock sensitization was elicited by presentation of electric footshocks between two series of acoustic stimuli. In contrast to habituation, footshock sensitization learning was attenuated in NCAM-/- mice: the acoustic SR increase after the footshocks was lower in the mutant than in wildtype mice, indicating that NCAM plays an important role in the relevant brain areas, such as amygdala and/or the hippocampus.     

Back to basics for Bayesian model building in genomic selection

Numerous Bayesian methods of phenotype prediction and genomic breeding value estimation based on multilocus association models have been proposed. Computationally the methods have been based either on Markov chain Monte Carlo or on faster maximum a posteriori estimation. The demand for more accurate and more efficient estimation has led to the rapid emergence of workable methods, unfortunately at the expense of well-defined principles for Bayesian model building. In this article we go back to the basics and build a Bayesian multilocus association model for quantitative and binary traits with carefully defined hierarchical parameterization of Student's t and Laplace priors. In this treatment we consider alternative model structures, using indicator variables and polygenic terms. We make the most of the conjugate analysis, enabled by the hierarchical formulation of the prior densities, by deriving the fully conditional posterior densities of the parameters and using the acquired known distributions in building fast generalized expectation-maximization estimation algorithms.     

Creating Public Awareness of How Goats Are Reared and Milk Produced May Affect Consumer Acceptability

This study evaluated whether showing a video of the different ways of raising goats for milk affected consumer acceptability. Four combinations, 2 Videos (intensive [INT] and semiextensive [SEM] system) × 2 Milk Types (semiskimmed [S] and whole [W] milk), were evaluated by 70 habitual consumers of goat milk, who scored their liking and purchase intention during blind (B), expected (E), and informed (I) acceptability sessions. In the B session, consumers tasted both milk types without information. S samples were preferred over W samples. In the E session, SEM video created high expectations in terms of milk liking and purchase intent, whereas the opposite happened when showing INT video. In the I session, consumers showed a clear preference for combinations created using SEM video, regardless of milk type. W-SEM and S-INT were worse (negative disconfirmation) and better (positive disconfirmation) than expected, respectively. A complete assimilation toward expectations occurred only for S-INT. INT video adversely affected the acceptability of S samples. Concerning purchase intent, W-SEM and S-SEM were worse than expected, but the assimilation was complete only for S-SEM: SEM video increased purchase intent for S samples.     

On the Estimation of the Mean with Random Number Of Observations. (Comments on a Paper by M. Singh and V. K. Gupta)

In this paper we study the problem of estimating the mean when the number of observations taken on each unit is a random variable. The estimator proposed by M. Singh and V. K. Gupta (1980) is discussed and modified. It is argued, however, that the arithmetic mean is a more appropriate choice of estimator.     

The body beyond the body: expectation of a sensory event is enough to induce ownership over a fake hand

More than 100 papers have been published on the rubber hand illusion since its discovery 14 years ago. The illusion has been proposed as a demonstration that the body is distinguished from other objects by its participation in specific forms of intermodal perceptual correlation. Here, we radically challenge this view by claiming that perceptual correlation is not necessary to produce the experience of this body as mine. Each of 15 participants was seated with his/her right arm resting upon a table just below another smaller table. Thus, the real hand was hidden from the participant''s view and a life-sized rubber model of a right hand was placed on the small table in front of the participant. The participant observed the experimenter''s hand while approaching—without touching—the rubber hand. Phenomenology of the illusion was measured by means of skin conductance response and questionnaire. Both measures indicated that participants experienced the illusion that the experimenter''s hand was about to touch their hidden hand rather than the rubber hand, as if the latter replaced their own hand. This did not occur when the rubber hand was rotated by 180° or replaced by a piece of wood. This illusion indicates that our brain does not build a sense of self in a merely reactive way, via perceptual correlations; rather it generates predictions on what may or may not belong to itself.