Prediction of absolute rate coefficients and product branching ratios for the C(P-3) plus allene reaction system

Complex chemical reactions in the gas phase can be decomposed into a network of elementary (e.g., unimolecular and bimolecular) steps which may involve multiple reactant channels, multiple intermediates, and multiple products. The modeling of such reactions involves describing the molecular species and their transformation by reaction at a detailed level. Here we focus on a detailed modeling of the C(P-3)+allene (C3H4) reaction, for which molecular beam experiments and theoretical calculations have previously been performed. In our previous calculations, product branching ratios for a nonrotating isomerizing unimolecular system were predicted. We extend the previous calculations to predict absolute unimolecular rate coefficients and branching ratios using microcanonical variational transition state theory (mu-VTST) with full energy and angular momentum resolution. Our calculation of the initial capture rate is facilitated by systematic ab initio potential energy surface calculations that describe the interaction potential between carbon and allene as a function of the angle of attack. Furthermore, the chemical kinetic scheme is enhanced to explicitly treat the entrance channels in terms of a predicted overall input flux and also to allow for the possibility of redissociation via the entrance channels. Thus, the computation of total bimolecular reaction rates and partial capture rates is now possible. (C) 2002 American Institute of Physics.

Analysis of the mouse transcriptome for genes involved in the function of the nervous system

We analyzed the mouse Representative Transcript and Protein Set for molecules involved in brain function. We found full-length cDNAs of many known brain genes and discovered new members of known brain gene families, including Family 3 G-protein coupled receptors, voltage-gated channels, and connexins. We also identified previously unknown candidates for secreted neuroactive molecules. The existence of a large number of unique brain ESTs suggests an additional molecular complexity that remains to be explored. A list of genes containing CAG stretches in the coding region represents a first step in the potential identification of candidates for hereditary neurological disorders.

The blood volumes of the primary and secondary circulatory system in the Atlantic cod Gadus morhua L., using plasma bound Evans Blue and compartmental analysis

The volume of the primary (PCS) and secondary (SCS) circulatory system in the Atlantic cod Gadus morhua was determined using a modified dye dilution technique. Cod (N=10) were chronically cannulated in the second afferent branchial artery with PE-50 tubing. Evans Blue dye was bound to harvested fish plasma at a concentration of 1 mg dye ml(-1) plasma, and injected at a concentration of 1 mg kg(-1) body mass. Serial sampling from the cannula produced a dye dilution curve, which could be described by a double exponential decay equation. Curve analysis enabled the calculation of the primary circulatory and total distribution volume. The difference between these volumes is assumed to be the volume of the SCS. From the dilution curve, it was also possible to calculate flow rates between and within the systems. The results of these experiments suggest a plasma volume in the PCS of 3.42+/-0.89 ml 100 g(-1) body mass, and in the SCS of 1.68+/-0.35 ml 100 g(-1) body mass (mean +/- S.D.) or approximately 50% that of the PCS. Flow rates to the SCS were calculated as 2.7% of the resting cardiac output. There was an allometric relationship between body mass and blood volumes. Increasing condition factor showed a tendency towards smaller blood volumes of the PCS, expressed as percentage body mass, but this was not evident for the volume of the SCS.

Learning the dynamics of embedded clauses

Recent work by Siegelmann has shown that the computational power of recurrent neural networks matches that of Turing Machines. One important implication is that complex language classes (infinite languages with embedded clauses) can be represented in neural networks. Proofs are based on a fractal encoding of states to simulate the memory and operations of stacks. In the present work, it is shown that similar stack-like dynamics can be learned in recurrent neural networks from simple sequence prediction tasks. Two main types of network solutions are found and described qualitatively as dynamical systems: damped oscillation and entangled spiraling around fixed points. The potential and limitations of each solution type are established in terms of generalization on two different context-free languages. Both solution types constitute novel stack implementations - generally in line with Siegelmann's theoretical work - which supply insights into how embedded structures of languages can be handled in analog hardware.