Dilepton Enhancement by Thermal Pion Annihilation in the CERES Experiment

We compare the recent CERES data on dielectron production in 200 GeV/u S+Au collisions with the theoretical predictions due to pion annihilation in a thermal hadronization and a string fragmentation scenario. Both models yield similar results for the dilepton mass spectrum. A satisfactory description of the experimental spectrum requires an in-medium reduction of the rho-mass and a freeze-out temperature of about 150 MeV in the thermal model. We emphasize and discuss the influence of experimental acceptance and resolution corrections.Comment: 9 pages, RevTex, 4 eps figures, To appear in Phys. Lett.

Arguments for the cognitive social sciences

This article analyses the arguments for the integration between the cognitive and social sciences. We understand interdisciplinary integration as an umbrella term that includes different ways of bringing scientific disciplines together. Our focus is on four arguments based on different ideas about how the cognitive sciences should be integrated with the social sciences: explanatory grounding, theoretical unification, constraint and complementarity. These arguments not only provide different reasons why the cognitive social sciences—i.e. disciplines and research programs that aim to integrate the social sciences with the cognitive sciences—are needed but also subscribe to different visions as to how these sciences might look like. We discuss each argument in three stages: First, we provide a concrete example of the argument. Second, we reconstruct the argument by specifying its premises, inferential structure and conclusion. Third, we evaluate the argument by analyzing its presuppositions, the plausibility of its premises, the soundness of its inferences and potential conceptual ambiguities. In the final discussion, we compare these arguments and identify the most compelling reasons why the cognitive social sciences are needed.Peer reviewe

Culture–gene coevolution of individualism–collectivism and the serotonin transporter gene

Culture–gene coevolutionary theory posits that cultural values have evolved, are adaptive and influence the social and physical environments under which genetic selection operates. Here, we examined the association between cultural values of individualism–collectivism and allelic frequency of the serotonin transporter functional polymorphism (5-HTTLPR) as well as the role this culture–gene association may play in explaining global variability in prevalence of pathogens and affective disorders. We found evidence that collectivistic cultures were significantly more likely to comprise individuals carrying the short (S) allele of the 5-HTTLPR across 29 nations. Results further show that historical pathogen prevalence predicts cultural variability in individualism–collectivism owing to genetic selection of the S allele. Additionally, cultural values and frequency of S allele carriers negatively predict global prevalence of anxiety and mood disorder. Finally, mediation analyses further indicate that increased frequency of S allele carriers predicted decreased anxiety and mood disorder prevalence owing to increased collectivistic cultural values. Taken together, our findings suggest culture–gene coevolution between allelic frequency of 5-HTTLPR and cultural values of individualism–collectivism and support the notion that cultural values buffer genetically susceptible populations from increased prevalence of affective disorders. Implications of the current findings for understanding culture–gene coevolution of human brain and behaviour as well as how this coevolutionary process may contribute to global variation in pathogen prevalence and epidemiology of affective disorders, such as anxiety and depression, are discussed

The Origin of Behavior

We propose a single evolutionary explanation for the origin of several behaviors that have been observed in organisms ranging from ants to human subjects, including risk-sensitive foraging, risk aversion, loss aversion, probability matching, randomization, and diversification. Given an initial population of individuals, each assigned a purely arbitrary behavior with respect to a binary choice problem, and assuming that offspring behave identically to their parents, only those behaviors linked to reproductive success will survive, and less reproductively successful behaviors will disappear at exponential rates. When the uncertainty in reproductive success is systematic, natural selection yields behaviors that may be individually sub-optimal but are optimal from the population perspective; when reproductive uncertainty is idiosyncratic, the individual and population perspectives coincide. This framework generates a surprisingly rich set of behaviors, and the simplicity and generality of our model suggest that these derived behaviors are primitive and nearly universal within and across species

Prestige Affects Cultural Learning in Chimpanzees

Humans follow the example of prestigious, high-status individuals much more readily than that of others, such as when we copy the behavior of village elders, community leaders, or celebrities. This tendency has been declared uniquely human, yet remains untested in other species. Experimental studies of animal learning have typically focused on the learning mechanism rather than on social issues, such as who learns from whom. The latter, however, is essential to understanding how habits spread. Here we report that when given opportunities to watch alternative solutions to a foraging problem performed by two different models of their own species, chimpanzees preferentially copy the method shown by the older, higher-ranking individual with a prior track-record of success. Since both solutions were equally difficult, shown an equal number of times by each model and resulted in equal rewards, we interpret this outcome as evidence that the preferred model in each of the two groups tested enjoyed a significant degree of prestige in terms of whose example other chimpanzees chose to follow. Such prestige-based cultural transmission is a phenomenon shared with our own species. If similar biases operate in wild animal populations, the adoption of culturally transmitted innovations may be significantly shaped by the characteristics of performers

A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data

<p>Abstract</p> <p>Background</p> <p>In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns. This kind of clustering is called <it>biclustering</it>. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed.</p> <p>Methods</p> <p>We introduce <it>BiMine</it>, a new enumeration algorithm for biclustering of DNA microarray data. The proposed algorithm is based on three original features. First, <it>BiMine </it>relies on a new evaluation function called <it>Average Spearman's rho </it>(ASR). Second, <it>BiMine </it>uses a new tree structure, called <it>Bicluster Enumeration Tree </it>(BET), to represent the different biclusters discovered during the enumeration process. Third, to avoid the combinatorial explosion of the search tree, <it>BiMine </it>introduces a parametric rule that allows the enumeration process to cut tree branches that cannot lead to good biclusters.</p> <p>Results</p> <p>The performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data. The experimental results show that <it>BiMine </it>competes well with several other biclustering methods. Moreover, we test the biological significance using a gene annotation web-tool to show that our proposed method is able to produce biologically relevant biclusters. The software is available upon request from the authors to academic users.</p

Natural parenting : back to basics in infant care

Peer reviewe

Construction of gene regulatory networks using biclustering and bayesian networks

<p>Abstract</p> <p>Background</p> <p>Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling.</p> <p>Results</p> <p>In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method.</p> <p>Conclusions</p> <p>Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.</p