Hemoperfusive Removal of Specific Intoxicants: The Role of the Rabbit in Preclinical Trials

Fjemelse af specifikke giftsloffer ved hemoperfusion Haemoperfusion er den foretrukne metode til direkte detoksifikation af patienter med akutte forgiftninger. Som adsorbant anvendes saedvanligvis kul. Den nyeste forskning indenfor dette omrade beskmftiger Sig med udvikling af specifikke adsorbanter til fjernelse at specitikke antistoffer, immunkomplekser 0g giftstoffer. Der gives en beskrivelse af en dyreeksperimentel model til udvikling af specifik detoksifikation ved anvendelse af haemoperfusion. Som forsagsdyr anvendes kaniner med permanente katetre i v. jugularis og a. carotis. Haemoperfusionssystemet bestfir af en peristaltisk pumpe og en stajle med agaroseperler (0.5—1.0 mm i diameter) indeholdende tusinder af mikrosphaerer (0.2 p. i diameter) koblet til specifikke antigener. Det arterielle blod pumpes fra a. carotis gennem sajlen til V. jugularis. Systemet perfunderes med hepariniseret saltvand (1 enh/ml) far brug, og kaninen hepariniseres med 300 enh heparin pr. kg legemsvaegt. Perfusionshastigheden er 8—15 ml/min, svarende til en perfusionshastighed p5. 20—30 min. I fig. 5 og 6 Vises resultaterne af forsog pa fjernelse af kviksolv og anti bovint serum albumin

A comparison of 'pruning' during multi-step planning in depressed and healthy individuals

BACKGROUND: Real-life decisions are often complex because they involve making sequential choices that constrain future options. We have previously shown that to render such multi-step decisions manageable, people 'prune' (i.e. selectively disregard) branches of decision trees that contain negative outcomes. We have theorized that sub-optimal pruning contributes to depression by promoting an oversampling of branches that result in unsavoury outcomes, which results in a negatively-biased valuation of the world. However, no study has tested this theory in depressed individuals. METHODS: Thirty unmedicated depressed and 31 healthy participants were administered a sequential reinforcement-based decision-making task to determine pruning behaviours, and completed measures of depression and anxiety. Computational, Bayesian and frequentist analyses examined group differences in task performance and relationships between pruning and depressive symptoms. RESULTS: Consistent with prior findings, participants robustly pruned branches of decision trees that began with large losses, regardless of the potential utility of those branches. However, there was no group difference in pruning behaviours. Further, there was no relationship between pruning and levels of depression/anxiety. CONCLUSIONS: We found no evidence that sub-optimal pruning is evident in depression. Future research could determine whether maladaptive pruning behaviours are observable in specific sub-groups of depressed patients (e.g. in treatment-resistant individuals), or whether misuse of other heuristics may contribute to depression

Group selection models in prebiotic evolution

The evolution of enzyme production is studied analytically using ideas of the group selection theory for the evolution of altruistic behavior. In particular, we argue that the mathematical formulation of Wilson's structured deme model ({\it The Evolution of Populations and Communities}, Benjamin/Cumings, Menlo Park, 1980) is a mean-field approach in which the actual environment that a particular individual experiences is replaced by an {\it average} environment. That formalism is further developed so as to avoid the mean-field approximation and then applied to the problem of enzyme production in the prebiotic context, where the enzyme producer molecules play the altruists role while the molecules that benefit from the catalyst without paying its production cost play the non-altruists role. The effects of synergism (i.e., division of labor) as well as of mutations are also considered and the results of the equilibrium analysis are summarized in phase diagrams showing the regions of the space of parameters where the altruistic, non-altruistic and the coexistence regimes are stable. In general, those regions are delimitated by discontinuous transition lines which end at critical points.Comment: 22 pages, 10 figure

Adaptations to Climate in Candidate Genes for Common Metabolic Disorders

Evolutionary pressures due to variation in climate play an important role in shaping phenotypic variation among and within species and have been shown to influence variation in phenotypes such as body shape and size among humans. Genes involved in energy metabolism are likely to be central to heat and cold tolerance. To test the hypothesis that climate shaped variation in metabolism genes in humans, we used a bioinformatics approach based on network theory to select 82 candidate genes for common metabolic disorders. We genotyped 873 tag SNPs in these genes in 54 worldwide populations (including the 52 in the Human Genome Diversity Project panel) and found correlations with climate variables using rank correlation analysis and a newly developed method termed Bayesian geographic analysis. In addition, we genotyped 210 carefully matched control SNPs to provide an empirical null distribution for spatial patterns of allele frequency due to population history alone. For nearly all climate variables, we found an excess of genic SNPs in the tail of the distributions of the test statistics compared to the control SNPs, implying that metabolic genes as a group show signals of spatially varying selection. Among our strongest signals were several SNPs (e.g., LEPR R109K, FABP2 A54T) that had previously been associated with phenotypes directly related to cold tolerance. Since variation in climate may be correlated with other aspects of environmental variation, it is possible that some of the signals that we detected reflect selective pressures other than climate. Nevertheless, our results are consistent with the idea that climate has been an important selective pressure acting on candidate genes for common metabolic disorders.</p

Deciphering Network Community Structure by Surprise

The analysis of complex networks permeates all sciences, from biology to sociology. A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise (S), leads to a very efficient characterization of the community structure of complex synthetic networks. Particularly, S qualitatively outperforms the most commonly used criterion to define communities, Newman and Girvan's modularity (Q). Applying S maximization to real networks often provides natural, well-supported partitions, but also sometimes counterintuitive solutions that expose the limitations of our previous knowledge. These results indicate that it is possible to define an effective global criterion for community structure and open new routes for the understanding of complex networks.Comment: 7 pages, 5 figure

Novel type of phase transition in a system of self-driven particles

A simple model with a novel type of dynamics is introduced in order to investigate the emergence of self-ordered motion in systems of particles with biologically motivated interaction. In our model particles are driven with a constant absolute velocity and at each time step assume the average direction of motion of the particles in their neighborhood with some random perturbation ($\eta$) added. We present numerical evidence that this model results in a kinetic phase transition from no transport (zero average velocity, $| {\bf v}_a | =0$) to finite net transport through spontaneous symmetry breaking of the rotational symmetry. The transition is continuous since $| {\bf v}_a |$ is found to scale as $(\eta_c-\eta)^\beta$ with $\beta\simeq 0.45$

20 questions on Adaptive Dynamics

Abstract Adaptive Dynamics is an approach to studying evolutionary change when fitness is density or frequency dependent. Modern papers identifying themselves as using this approach first appeared in the 1990s, and have greatly increased up to the present. However, because of the rather technical nature of many of the papers, the approach is not widely known or understood by evolutionary biologists. In this review we aim to remedy this situation by outlining the methodology and then examining its strengths and weaknesses. We carry this out by posing and answering 20 key questions on Adaptive Dynamics. We conclude that Adaptive Dynamics provides a set of useful approximations for studying various evolutionary questions. However, as with any approximate method, conclusions based on Adaptive Dynamics are valid only under some restrictions that we discuss