Cocaine reward models: Conditioned place preference can be established in dopamine- and in serotonin-transporter knockout mice

Cocaine and methylphenidate block uptake by neuronal plasma membrane transporters for dopamine, serotonin, and norepinephrine. Cocaine also blocks voltage-gated sodium channels, a property not shared by methylphenidate. Several lines of evidence have suggested that cocaine blockade of the dopamine transporter (DAT), perhaps with additional contributions from serotonin transporter (5-HTT) recognition, was key to its rewarding actions. We now report that knockout mice without DAT and mice without 5-HTT establish cocaine-conditioned place preferences. Each strain displays cocaine-conditioned place preference in this major mouse model for assessing drug reward, while methylphenidate-conditioned place preference is also maintained in DAT knockout mice. These results have substantial implications for understanding cocaine actions and for strategies to produce anticocaine medications.

Striking sequence similarity in inter- and intra-specific comparisons of class I SLG alleles from Brassica oleracea and Brassica campestris: Implications for the evolution and recognition mechanism

Self-incompatibility in Brassica is controlled by a single multi-allelic locus (S locus), which contains at least two highly polymorphic genes expressed in the stigma: an S glycoprotein gene (SLG) and an S receptor kinase gene (SRK). The putative ligand-binding domain of SRK exhibits high homology to the secretory protein SLG, and it is believed that SLG and SRK form an active receptor kinase complex with a self-pollen ligand, which leads to the rejection of self-pollen. Here, we report 31 novel SLG sequences of Brassica oleracea and Brassica campestris. Sequence comparisons of a large number of SLG alleles and SLG-related genes revealed the following points. (i) The striking sequence similarity observed in an inter-specific comparison (95.6% identity between SLG14 of B. oleracea and SLG25 of B. campestris in deduced amino acid sequence) suggests that SLG diversification predates speciation. (ii) A perfect match of the sequences in hypervariable regions, which are thought to determine S specificity in an intra-specific comparison (SLG8 and SLG46 of B. campestris) and the observation that the hypervariable regions of SLG and SRK of the same S haplotype were not necessarily highly similar suggests that SLG and SRK bind different sites of the pollen ligand and that they together determine S specificity. (iii) Comparison of the hypervariable regions of SLG alleles suggests that intragenic recombination, together with point mutations, has contributed to the generation of the high level of sequence variation in SLG alleles. Models for the evolution of SLG/SRK are presented.

Gene recognition via spliced sequence alignment.

Gene recognition is one of the most important problems in computational molecular biology. Previous attempts to solve this problem were based on statistics, and applications of combinatorial methods for gene recognition were almost unexplored. Recent advances in large-scale cDNA sequencing open a way toward a new approach to gene recognition that uses previously sequenced genes as a clue for recognition of newly sequenced genes. This paper describes a spliced alignment algorithm and software tool that explores all possible exon assemblies in polynomial time and finds the multiexon structure with the best fit to a related protein. Unlike other existing methods, the algorithm successfully recognizes genes even in the case of short exons or exons with unusual codon usage; we also report correct assemblies for genes with more than 10 exons. On a test sample of human genes with known mammalian relatives, the average correlation between the predicted and actual proteins was 99%. The algorithm correctly reconstructed 87% of genes and the rare discrepancies between the predicted and real exon-intron structures were caused either by short (less than 5 amino acids) initial/terminal exons or by alternative splicing. Moreover, the algorithm predicts human genes reasonably well when the homologous protein is nonvertebrate or even prokaryotic. The surprisingly good performance of the method was confirmed by extensive simulations: in particular, with target proteins at 160 accepted point mutations (PAM) (25% similarity), the correlation between the predicted and actual genes was still as high as 95%.

State of the art in continuous speech recognition.

In the past decade, tremendous advances in the state of the art of automatic speech recognition by machine have taken place. A reduction in the word error rate by more than a factor of 5 and an increase in recognition speeds by several orders of magnitude (brought about by a combination of faster recognition search algorithms and more powerful computers), have combined to make high-accuracy, speaker-independent, continuous speech recognition for large vocabularies possible in real time, on off-the-shelf workstations, without the aid of special hardware. These advances promise to make speech recognition technology readily available to the general public. This paper focuses on the speech recognition advances made through better speech modeling techniques, chiefly through more accurate mathematical modeling of speech sounds.