New and Exciting in PLoS this week

Is Our Self Nothing but Reward? Neuronal Overlap and Distinction between Reward and Personal Relevance and Its Relation to Human Personality:

The attribution of personal relevance, i.e. relating internal and external stimuli to establish a sense of belonging, is a common phenomenon in daily life. Although previous research demonstrated a relationship between reward and personal relevance, their exact neuronal relationship including the impact of personality traits remains unclear. Using functional magnetic resonance imaging, we applied an experimental paradigm that allowed us to explore the neural response evoked by reward and the attribution of personal relevance separately. We observed different brain regions previously reported to be active during reward and personal relevance, including the bilateral caudate nucleus and the pregenual anterior cingulate cortex (PACC). Additional analysis revealed activations in the right and left insula specific for the attribution of personal relevance. Furthermore, our results demonstrate a negative correlation between signal changes in both the PACC and the left anterior insula during the attribution of low personal relevance and the personality dimension novelty seeking. While a set of subcortical and cortical regions including the PACC is commonly involved in reward and personal relevance, other regions like the bilateral anterior insula were recruited specifically during personal relevance. Based on our correlation between novelty seeking and signal changes in both regions during personal relevance, we assume that the neuronal response to personally relevant stimuli is dependent on the personality trait novelty seeking.

A Constant Light-Genetic Screen Identifies KISMET as a Regulator of Circadian Photoresponses:

In most organisms, intracellular molecular pacemakers called circadian clocks coordinate metabolic, physiological, and behavioral processes during the course of the day. For example, they determine when animals are active or resting. Circadian clocks are self-sustained oscillators, but their free-running period does not exactly match day length. Thus, they have to be reset by environmental inputs to stay properly phased with the day:night cycle. The fruit fly Drosophila melanogaster relies primarily on CRYPTOCHROME (CRY)--a cell-autonomous blue-light photoreceptor--to synchronize its circadian clocks with the light:dark cycle. With a genetic screen, we identified over 20 candidate genes that might regulate CRY function. kismet (kis) is among them: it encodes a chromatin remodeling factor essential for the development of Drosophila. We show that, in adult flies, KIS is expressed and functions in brain neurons that control daily behavioral rhythms. KIS determines how Drosophila circadian behavior responds to light, but not its free-running period. Moreover, manipulating simultaneously kis and cry activity demonstrates that these two genes interact to control molecular and behavioral circadian photoresponses. Our work therefore reveals that KIS regulates CRY signaling and thus determines how circadian clocks respond to light input.

Emergent Patterns of Social Affiliation in Primates, a Model:

Individual primates distribute their affiliative behaviour (such as grooming) in complex patterns among their group members. For instance, they reciprocate grooming, direct it more to partners the higher the partner's rank, use it to reconcile fights and do so in particular with partners that are more valuable. For several types of patterns (such as reconciliation and exchange), a separate theory based on specific cognitive processes has been developed (such as individual recordkeeping, a tendency to exchange, selective attraction to the former opponent, and estimation of the value of a relationship). It is difficult to imagine how these separate theories can all be integrated scientifically and how these processes can be combined in the animal's mind. To solve this problem, we first surveyed the empirical patterns and then we developed an individual-based model (called GrooFiWorld) in which individuals group, compete and groom. The grooming rule is based on grooming out of fear of defeat and on the anxiety reducing effects of grooming. We show that in this context this rule alone can explain many of the patterns of affiliation as well as the differences between egalitarian and despotic species. Our model can be used as a null model to increase our understanding of affiliative patterns of primates.

Expanding the Paradigms of Plant Pathogen Life History and Evolution of Parasitic Fitness beyond Agricultural Boundaries:

How do pathogens, whether they parasitize plants or animals, acquire virulence to new hosts and resistance to the arms we deploy to control disease? The significance of these questions for microbiology and for society at large can be illustrated by the recent worldwide efforts to track and limit the emergence of human transmissible strains of swine and avian influenza virus and of multidrug-resistant lines of human pathogenic bacteria, and to restrain the spread of Ug99, a strain of stem rust of wheat. Recent research in medical epidemiology has elucidated the impact of pathogen ecology in environmental reservoirs on the evolution of novel or enhanced pathogen virulence. In contrast, the evolution of virulence in plant pathogens has been investigated from a predominantly agro-centric perspective, and has focused overwhelmingly on evolutionary forces related to interactions with the primary plant host. Here, we argue that current concepts from the field of medical epidemiology regarding mechanisms that lead to acquisition of novel virulence, biocide resistance, and enhanced pathogenic fitness can serve as an important foundation for novel hypotheses about the evolution of plant pathogens. We present numerous examples of virulence traits in plant pathogenic microorganisms that also have a function in their survival and growth in nonagricultural and nonplant habitats. Based on this evidence, we make an appeal to expand concepts of the life history of plant pathogens and the drivers of pathogen evolution beyond the current agro-centric perspective.

Age at Puberty and the Emerging Obesity Epidemic:

Recent studies have shown that puberty starts at younger ages than previously. It has been hypothesized that the increasing prevalence of childhood obesity is contributing to this trend. The purpose of this study was to analyze the association between prepubertal body mass index (BMI) and pubertal timing, as assessed by age at onset of pubertal growth spurt (OGS) and at peak height velocity (PHV), and the secular trend of pubertal timing given the prepubertal BMI. Annual measurements of height and weight were available in all children born from 1930 to 1969 who attended primary school in the Copenhagen municipality; 156,835 children fulfilled the criteria for determining age at OGS and PHV. The effect of prepubertal BMI at age seven on these markers of pubertal development within and between birth cohorts was analyzed. BMI at seven years was significantly inversely associated with age at OGS and PHV. Dividing the children into five levels of prepubertal BMI, we found a similar secular trend toward earlier maturation in all BMI groups. The heavier both boys and girls were at age seven, the earlier they entered puberty. Irrespective of level of BMI at age seven, there was a downward trend in the age at attaining puberty in both boys and girls, which suggests that the obesity epidemic is not solely responsible for the trend.

Biomedical Text Mining and Its Applications:

This tutorial is intended for biologists and computational biologists interested in adding text mining tools to their bioinformatics toolbox. As an illustrative example, the tutorial examines the relationship between progressive multifocal leukoencephalopathy (PML) and antibodies. Recent cases of PML have been associated to the administration of some monoclonal antibodies such as efalizumab [1]. Those interested in a further introduction to text mining may also want to read other reviews [2]-[4].

Understanding large amounts of text with the aid of a computer is harder than simply equipping a computer with a grammar and a dictionary. A computer, like a human, needs certain specialized knowledge in order to understand text. The scientific field that is dedicated to train computers with the right knowledge for this task (among other tasks) is called natural language processing (NLP). Biomedical text mining (henceforth, text mining) is the subfield that deals with text that comes from biology, medicine, and chemistry (henceforth, biomedical text). Another popular name is BioNLP, which some practitioners use as synonymous with text mining.

Biomedical text is not a homogeneous realm [5]. Medical records are written differently from scientific articles, sequence annotations, or public health guidelines. Moreover, local dialects are not uncommon [6]. For example, medical centers develop their own jargons and laboratories create their idiosyncratic protein nomenclatures. This variability means, in practice, that text mining applications are tailored to specific types of text. In particular, for reasons of availability and cost, many are designed for scientific abstracts in English from Medline.