Role of the tubulin-microtubule system in lymphocyte activation

The role of the tubulin-microtubule system was examined in human peripheral blood leukocytes after activation with phytohemagglutinin (PHA). Soluble tubulin and microtubules were measured with a [(3)H]colchicine-binding assay. It was found that the tubulin content of PHA-activated lymphocytes was consistently increased relative to total protein content after 36 h of culture. There was no increase in the proportion of total tubulin synthesis which was present as microtubules at 36 h. Nevertheless, as a result of increased tubulin synthesis, there was a two-to three-fold increase in total microtubular mass. Colchicine, which disrupts microtubles, was used to assess the role of microtubule assembly in the sequence of events which follow lymphocyte activation, namely lymphokine release, protein synthesis, RNA synthesis, and DNA synthesis. Colchicine consistently inhibited DNA synthesis but did not inhibit release of the lymphokine, osteoclast activating factor (OAF). Protein and RNA syntheses were inhibited much less than DNA synthesis. The fact that some effects of PHA on lymphocytes appear to require intact microtubules and at least one does not suggest that the microtubule dependent step in PHA-stimulated lymphocyte activation occurs at a stage after propagation of the signal from the membrane to the cell interior.     

STEPS: A grid search methodology for optimized peptide identification filtering of MS/MS database search results

For bottom‐up proteomics, there are wide variety of database‐searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid‐search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection‐–referred to as STEPS‐–utilizes user‐defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal “parameter set” for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true‐positive identifications are demonstrated using datasets derived from immunoaffinity‐depleted blood serum and a bacterial cell lysate, two common proteomics sample types.     

Simulated brain tumor growth dynamics using a three-dimensional cellular automaton

We have developed a novel and versatile three-dimensional cellular automaton model of brain tumor growth. We show that macroscopic tumor behavior can be realistically modeled using microscopic parameters. Using only four parameters, this model simulates Gompertzian growth for a tumor growing over nearly three orders of magnitude in radius. It also predicts the composition and dynamics of the tumor at selected time points in agreement with medical literature. We also demonstrate the flexibility of the model by showing the emergence, and eventual dominance, of a second tumor clone with a different genotype. The model incorporates several important and novel features, both in the rules governing the model and in the underlying structure of the model. Among these are a new definition of how to model proliferative and non-proliferative cells, an isotropic lattice, and an adaptive grid lattice.     

A revision of Aceratherium blanfordi Lydekker, 1884 (Mammalia: Rhinocerotidae) from the Early Miocene of Pakistan: postcranials as a key

Rhinocerotids are particularly abundant and diversified in Neogene deposits of the Indian subcontinent, but their systematics is far from being well defined. Based on the revision of old collections and new findings from the Early Miocene of the Bugti Hills and Zinda Pir, Pakistan, ‘Aceratherium blanfordi Lydekker, 1884’ is a chimera, consisting of two dentally convergent but postcranially distinct rhinocerotid taxa: Pleuroceros blanfordi and Mesaceratherium welcommi sp. nov. Postcranial features appear to be much more diagnostic than craniodental morphology in this case. A phylogenetic analysis based on 282 morphological characters scored for 28 taxa (four outgroups and ingroup including both taxa of interest and a ‘branching group’) strengthens this statement and supports Pleuroceros and Mesaceratherium as monophyletic genera within Rhinocerotinae. Both genera are recognized for the first time outside Europe. In the Bugti Hills, P. blanfordi and M. welcommi are part of an exceptionally diversified rhinocerotid fauna, with up to nine species associated in the same locality (Kumbi 4f). This rhinocerotid assemblage confirms the earliest Miocene age (Agenian/Aquitanian) of the upper member of the Chitarwata Formation as a whole. Coeval homotaxic rhinocerotid faunas from Europe (France, Czech Republic) and East Africa (Uganda, Kenya) support broad and sustainable rhinocerotid interchanges amongst South Asia, Europe, and Africa under compatible environmental conditions throughout earliest Miocene times. © 2010 The Linnean Society of London, Zoological Journal of the Linnean Society, 2010, 160 , 139–194.     

Future climate‐driven shifts in distribution of Calanus finmarchicus

Calanus finmarchicus is a key‐structural species of the North Atlantic polar biome. The species plays an important trophic role in subpolar and polar ecosystems as a grazer of phytoplankton and as a prey for higher trophic levels such as the larval stages of many fish species. Here, we used a recently developed ecological niche model to assess the ecological niche (sensu Hutchinson) of C. finmarchicus and characterize its spatial distribution. This model explained about 65% of the total variance of the observed spatial distribution inferred from an independent dataset (data of the continuous plankton recorder survey). Comparisons with other types of models (structured population and ecophysiological models) revealed a clear similarity between modeled spatial distributions at the scale of the North Atlantic. Contemporary models coupled with future projections indicated a progressive reduction of the spatial habitat of the species at the southern edge and a more pronounced one in the Georges Bank, the Scotian Shelf and the North Sea and a potential increase in abundance at the northern edge of its spatial distribution, especially in the Barents Sea. These major changes will probably lead to a major alteration of the trophodynamics of North Atlantic ecosystems affecting the trophodynamics and the biological carbon pump.     

PRISM: a data management system for high-throughput proteomics

Advanced proteomic research efforts involving areas such as systems biology or biomarker discovery are enabled by the use of high level informatics tools that allow the effective analysis of large quantities of differing types of data originating from various studies. Performing such analyses on a large scale is not feasible without a computational platform that performs data processing and management tasks. Such a platform must be able to provide high-throughput operation while having sufficient flexibility to accommodate evolving data analysis tools and methodologies. The Proteomics Research Information Storage and Management system (PRISM) provides a platform that serves the needs of the accurate mass and time tag approach developed at Pacific Northwest National Laboratory. PRISM incorporates a diverse set of analysis tools and allows a wide range of operations to be incorporated by using a state machine that is accessible to independent, distributed computational nodes. The system has scaled well as data volume has increased over several years, while allowing adaptability for incorporating new and improved data analysis tools for more effective proteomics research.     

A Hierarchy of Time-Scales and the Brain

In this paper, we suggest that cortical anatomy recapitulates the temporal hierarchy that is inherent in the dynamics of environmental states. Many aspects of brain function can be understood in terms of a hierarchy of temporal scales at which representations of the environment evolve. The lowest level of this hierarchy corresponds to fast fluctuations associated with sensory processing, whereas the highest levels encode slow contextual changes in the environment, under which faster representations unfold. First, we describe a mathematical model that exploits the temporal structure of fast sensory input to track the slower trajectories of their underlying causes. This model of sensory encoding or perceptual inference establishes a proof of concept that slowly changing neuronal states can encode the paths or trajectories of faster sensory states. We then review empirical evidence that suggests that a temporal hierarchy is recapitulated in the macroscopic organization of the cortex. This anatomic-temporal hierarchy provides a comprehensive framework for understanding cortical function: the specific time-scale that engages a cortical area can be inferred by its location along a rostro-caudal gradient, which reflects the anatomical distance from primary sensory areas. This is most evident in the prefrontal cortex, where complex functions can be explained as operations on representations of the environment that change slowly. The framework provides predictions about, and principled constraints on, cortical structure–function relationships, which can be tested by manipulating the time-scales of sensory input.     

Observing the observer (II): deciding when to decide

In a companion paper [1], we have presented a generic approach for inferring how subjects make optimal decisions under uncertainty. From a Bayesian decision theoretic perspective, uncertain representations correspond to "posterior" beliefs, which result from integrating (sensory) information with subjective "prior" beliefs. Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden or unknown) state of the world. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. In this paper, we describe a concrete implementation of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions) and demonstrate its utility by applying it to both simulated and empirical reaction time data from an associative learning task. Here, inter-trial variability in reaction times is modelled as reflecting the dynamics of the subjects' internal recognition process, i.e. the updating of representations (posterior densities) of hidden states over trials while subjects learn probabilistic audio-visual associations. We use this paradigm to demonstrate that our meta-Bayesian framework allows for (i) probabilistic inference on the dynamics of the subject's representation of environmental states, and for (ii) model selection to disambiguate between alternative preferences (loss functions) human subjects could employ when dealing with trade-offs, such as between speed and accuracy. Finally, we illustrate how our approach can be used to quantify subjective beliefs and preferences that underlie inter-individual differences in behaviour.