Disc1 Variation Leads to Specific Alterations in Adult Neurogenesis

Disrupted in schizophrenia 1 (DISC1) is a risk factor for a spectrum of neuropsychiatric illnesses including schizophrenia, bipolar disorder, and major depressive disorder. Here we use two missense Disc1 mouse mutants, described previously with distinct behavioural phenotypes, to demonstrate that Disc1 variation exerts differing effects on the formation of newly generated neurons in the adult hippocampus. Disc1 mice carrying a homozygous Q31L mutation, and displaying depressive-like phenotypes, have fewer proliferating cells while Disc1 mice with a homozygous L100P mutation that induces schizophrenia-like phenotypes, show changes in the generation, placement and maturation of newly generated neurons in the hippocampal dentate gyrus. Our results demonstrate Disc1 allele specific effects in the adult hippocampus, and suggest that the divergence in behavioural phenotypes may in part stem from changes in specific cell populations in the brain.     

A theory of associating food types with their postingestive consequences

Animals often face complex and changing food environments. While such environments are challenging, an animal should make an association between a food type and its properties (such as the presence of a nutrient or toxin). We use information theory concepts, such as mutual information, to establish a theory for the development of these associations. In this theory, associations are assumed to maximize the mutual information between foods and their consequences. We show that associations are invariably imperfect. An association's accuracy increases with the length of a feeding session and the relative frequency of a food type but decreases as time delay between consumption and postingestive consequence increases. Surprisingly, the accuracy of an association is independent of the number of additional food types in the environment. The rate of information transfer between novel foods and a forager depends on the forager's diet. In light of this theory, an animal's diet may have two competing goals: first, the provision of an appropriate balance of nutrients, and second, the ability to quickly and accurately learn the properties of novel foods. We discuss the ecological and behavioral implications of making associational errors and contrast the timescale and mechanisms of our theory with those of existing theory.     

Unravelling the Relationship between Animal Growth and Immune Response during Micro-Parasitic Infections

Background

Both host genetic potentials for growth and disease resistance, as well as nutrition are known to affect responses of individuals challenged with micro-parasites, but their interactive effects are difficult to predict from experimental studies alone.

Methodology/Principal Findings

Here, a mathematical model is proposed to explore the hypothesis that a host''s response to pathogen challenge largely depends on the interaction between a host''s genetic capacities for growth or disease resistance and the nutritional environment. As might be expected, the model predicts that if nutritional availability is high, hosts with higher growth capacities will also grow faster under micro-parasitic challenge, and more resistant animals will exhibit a more effective immune response. Growth capacity has little effect on immune response and resistance capacity has little effect on achieved growth. However, the influence of host genetics on phenotypic performance changes drastically if nutrient availability is scarce. In this case achieved growth and immune response depend simultaneously on both capacities for growth and disease resistance. A higher growth capacity (achieved e.g. through genetic selection) would be detrimental for the animal''s ability to cope with pathogens and greater resistance may reduce growth in the short-term.

Significance

Our model can thus explain contradicting outcomes of genetic selection observed in experimental studies and provides the necessary biological background for understanding the influence of selection and/or changes in the nutritional environment on phenotypic growth and immune response.     

The structure of the EU mediasphere

Background

A trend towards automation of scientific research has recently resulted in what has been termed “data-driven inquiry” in various disciplines, including physics and biology. The automation of many tasks has been identified as a possible future also for the humanities and the social sciences, particularly in those disciplines concerned with the analysis of text, due to the recent availability of millions of books and news articles in digital format. In the social sciences, the analysis of news media is done largely by hand and in a hypothesis-driven fashion: the scholar needs to formulate a very specific assumption about the patterns that might be in the data, and then set out to verify if they are present or not.

Methodology/Principal Findings

In this study, we report what we think is the first large scale content-analysis of cross-linguistic text in the social sciences, by using various artificial intelligence techniques. We analyse 1.3 M news articles in 22 languages detecting a clear structure in the choice of stories covered by the various outlets. This is significantly affected by objective national, geographic, economic and cultural relations among outlets and countries, e.g., outlets from countries sharing strong economic ties are more likely to cover the same stories. We also show that the deviation from average content is significantly correlated with membership to the eurozone, as well as with the year of accession to the EU.

Conclusions/Significance

While independently making a multitude of small editorial decisions, the leading media of the 27 EU countries, over a period of six months, shaped the contents of the EU mediasphere in a way that reflects its deep geographic, economic and cultural relations. Detecting these subtle signals in a statistically rigorous way would be out of the reach of traditional methods. This analysis demonstrates the power of the available methods for significant automation of media content analysis.     

KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

Background  

The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.     

Study of small air ions concentrations versus asthma attacks in the region of Athens-Greece after the Chernobyl disaster

A consequence of the Chernobyl disaster was the isolated increase of small air ions for a limited time period over Athens-Greece. Taking advantage of this unique condition we studied the relation of log daily mean concentrations of small positive air ions, small negative air ions, NO2 and SO2 with the total daily number of asthma attacks attendances at the region's largest chest diseases hospital for 35 days following the disaster and during a same time period in the preceding four years. No correlation was found between air ionization and asthma attacks, though the study was of a rather short duration and several possible influencing factors such as aeroallergen concentrations were not taken into consideration.