Arthropods: Developmental diversity within a (super) phylum

The expression patterns of developmental genes provide new markers that address the homology of body parts and provide clues as to how body plans have evolved. Such markers support the idea that insect wings evolved from limbs but refute the idea that insect and crustacean jaws are fundamentally different in structure. They also confirm that arthropod tagmosis reflects underlying patterns of Hox gene regulation but they do not yet resolve to what extent Hox expression domains may serve to define segment homologies.

Computation of Surface Electrical Potentials of Plant Cell Membranes : Correspondence to Published Zeta Potentials from Diverse Plant Sources

A Gouy-Chapman-Stern model has been developed for the computation of surface electrical potential (ψ0) of plant cell membranes in response to ionic solutes. The present model is a modification of an earlier version developed to compute the sorption of ions by wheat (Triticum aestivum L. cv Scout 66) root plasma membranes. A single set of model parameters generates values for ψ0 that correlate highly with published ζ potentials of protoplasts and plasma membrane vesicles from diverse plant sources. The model assumes ion binding to a negatively charged site (R− = 0.3074 μmol m−2) and to a neutral site (P0 = 2.4 μmol m−2) according to the reactions R− + IΖ ⇌ RIΖ−1 and P0 + IΖ ⇌ PIΖ, where IΖ represents an ion of charge Ζ. Binding constants for the negative site are 21,500 m−1 for H+, 20,000 m−1 for Al3+, 2,200 m−1 for La3+, 30 m−1 for Ca2+ and Mg2+, and 1 m−1 for Na+ and K+. Binding constants for the neutral site are 1/180 the value for binding to the negative site. Ion activities at the membrane surface, computed on the basis of ψ0, appear to determine many aspects of plant-mineral interactions, including mineral nutrition and the induction and alleviation of mineral toxicities, according to previous and ongoing studies. A computer program with instructions for the computation of ψ0, ion binding, ion concentrations, and ion activities at membrane surfaces may be requested from the authors.

Outer Dense Fiber 2 Is a Widespread Centrosome Scaffold Component Preferentially Associated with Mother Centrioles: Its Identification from Isolated Centrosomes

Because centrosomes were enriched in the bile canaliculi fraction from the chicken liver through their association with apical membranes, we developed a procedure for isolation of centrosomes from this fraction. With the use of the centrosomes, we generated centrosome-specific monoclonal antibodies. Three of the monoclonal antibodies recognized an antigen of ∼90 kDa. Cloning of its cDNA identified this antigen as a chicken homologue of outer dense fiber 2 protein (Odf2), which was initially identified as a sperm outer dense fiber-specific component. Exogenously expressed and endogenous Odf2 were shown to be concentrated at the centrosomes in a microtubule-independent manner in various types of cells at both light and electron microscopic levels. Odf2 exhibited a cell cycle-dependent pattern of localization and was preferentially associated with the mother centrioles in G0/G1-phase. Toward G1/S-phase before centrosome duplication, it became detectable in both mother and daughter centrioles. In the isolated bile canaliculi and centrosomes, Odf2, in contrast to other centrosomal components, was highly resistant to KI extraction. These findings indicate that Odf2 is a widespread KI-insoluble scaffold component of the centrosome matrix, which may be involved in the maturation event of daughter centrioles.

The evolution of emergent computation.

A simple evolutionary process can discover sophisticated methods for emergent information processing in decentralized spatially extended systems. The mechanisms underlying the resulting emergent computation are explicated by a technique for analyzing particle-based logic embedded in pattern-forming systems. Understanding how globally coordinated computation can emerge in evolution is relevant both for the scientific understanding of natural information processing and for engineering new forms of parallel computing systems.

The vesicular monoamine transporter 2 is present in small synaptic vesicles and preferentially localizes to large dense core vesicles in rat solitary tract nuclei.

In central neurons, monamine neurotransmitters are taken up and stored within two distinct classes of regulated secretory vesicles: small synaptic vesicles and large dense core vesicles (DCVs). Biochemical and pharmacological evidence has shown that this uptake is mediated by specific vesicular monamine transporters (VMATs). Recent molecular cloning techniques have identified the vesicular monoamine transporter (VMAT2) that is expressed in brain. This transporter determines the sites of intracellular storage of monoamines and has been implicated in both the modulation of normal monoaminergic neurotransmission and the pathogenesis of related neuropsychiatric disease. We used an antiserum against VMAT2 to examine its ultrastructural distribution in rat solitary tract nuclei, a region that contains a dense and heterogeneous population of monoaminergic neurons. We find that both immunoperoxidase and immunogold labeling for VMAT2 localize to DCVs and small synaptic vesicles in axon terminals, the trans-Golgi network of neuronal perikarya, tubulovesicles of smooth endoplasmic reticulum, and potential sites of vesicular membrane recycling. In axon terminals, immunogold labeling for VMAT2 was preferentially associated with DCVs at sites distant from typical synaptic junctions. The results provide direct evidence that a single VMAT is expressed in two morphologically distinct types of regulated secretory vesicles in central monoaminergic neurons.

Rapid local synchronization of action potentials: toward computation with coupled integrate-and-fire neurons.

The collective behavior of interconnected spiking nerve cells is investigated. It is shown that a variety of model systems exhibit the same short-time behavior and rapidly converge to (approximately) periodic firing patterns with locally synchronized action potentials. The dynamics of one model can be described by a downhill motion on an abstract energy landscape. Since an energy landscape makes it possible to understand and program computation done by an attractor network, the results will extend our understanding of collective computation from models based on a firing-rate description to biologically more realistic systems with integrate-and-fire neurons.