The dTTPase mechanism of T7 DNA helicase resembles the binding change mechanism of the F1-ATPase

Bacteriophage T7 DNA helicase is a ring-shaped hexamer that catalyzes duplex DNA unwinding using dTTP hydrolysis as an energy source. Of the six potential nucleotide binding sites on the hexamer, we have found that three are noncatalytic sites and three are catalytic sites. The noncatalytic sites bind nucleotides with a high affinity, but dTTPs bound to these sites do not dissociate or hydrolyze through many dTTPase turnovers at the catalytic sites. The catalytic sites show strong cooperativity which leads to sequential binding and hydrolysis of dTTP. The elucidated dTTPase mechanism of the catalytic sites of T7 helicase is remarkably similar to the binding change mechanism of the ATP synthase. Based on the similarity, a general mechanism for hexameric helicases is proposed. In this mechanism, an F1-ATPase-like rotational movement around the single-stranded DNA, which is bound through the central hole of the hexamer, is proposed to lead to unidirectional translocation along single-stranded DNA and duplex DNA unwinding.

Cultural universals: Measuring the semantic structure of emotion terms in English and Japanese

Research is presented on the semantic structure of 15 emotion terms as measured by judged-similarity tasks for monolingual English-speaking and monolingual and bilingual Japanese subjects. A major question is the relative explanatory power of a single shared model for English and Japanese versus culture-specific models for each language. The data support a shared model for the semantic structure of emotion terms even though some robust and significant differences are found between English and Japanese structures. The Japanese bilingual subjects use a model more like English when performing tasks in English than when performing the same task in Japanese.

Statistical methods for characterizing similarities and differences between semantic structures

This paper describes a variety of statistical methods for obtaining precise quantitative estimates of the similarities and differences in the structures of semantic domains in different languages. The methods include comparing mean correlations within and between groups, principal components analysis of interspeaker correlations, and analysis of variance of speaker by question data. Methods for graphical displays of the results are also presented. The methods give convergent results that are mutually supportive and equivalent under suitable interpretation. The methods are illustrated on the semantic domain of emotion terms in a comparison of the semantic structures of native English and native Japanese speaking subjects. We suggest that, in comparative studies concerning the extent to which semantic structures are universally shared or culture-specific, both similarities and differences should be measured and compared rather than placing total emphasis on one or the other polar position.

Two different neurodegenerative diseases caused by proteins with similar structures

The downstream prion-like protein (doppel, or Dpl) is a paralog of the cellular prion protein, PrPC. The two proteins have ≈25% sequence identity, but seem to have distinct physiologic roles. Unlike PrPC, Dpl does not support prion replication; instead, overexpression of Dpl in the brain seems to cause a completely different neurodegenerative disease. We report the solution structure of a fragment of recombinant mouse Dpl (residues 26–157) containing a globular domain with three helices and a small amount of β-structure. Overall, the topology of Dpl is very similar to that of PrPC. Significant differences include a marked kink in one of the helices in Dpl, and a different orientation of the two short β-strands. Although the two proteins most likely arose through duplication of a single ancestral gene, the relationship is now so distant that only the structures retain similarity; the functions have diversified along with the sequence.

The roles of language processing in a spoken language interface.

This paper provides an overview of the colloquium's discussion session on natural language understanding, which followed presentations by M. Bates [Bates, M. (1995) Proc. Natl. Acad. Sci. USA 92, 9977-9982] and R. C. Moore [Moore, R. C. (1995) Proc. Natl. Acad. Sci. USA 92, 9983-9988]. The paper reviews the dual role of language processing in providing understanding of the spoken input and an additional source of constraint in the recognition process. To date, language processing has successfully provided understanding but has provided only limited (and computationally expensive) constraint. As a result, most current systems use a loosely coupled, unidirectional interface, such as N-best or a word network, with natural language constraints as a postprocess, to filter or resort the recognizer output. However, the level of discourse context provides significant constraint on what people can talk about and how things can be referred to; when the system becomes an active participant, it can influence this order. But sources of discourse constraint have not been extensively explored, in part because these effects can only be seen by studying systems in the context of their use in interactive problem solving. This paper argues that we need to study interactive systems to understand what kinds of applications are appropriate for the current state of technology and how the technology can move from the laboratory toward real applications.

Integration of speech with natural language understanding.

The integration of speech recognition with natural language understanding raises issues of how to adapt natural language processing to the characteristics of spoken language; how to cope with errorful recognition output, including the use of natural language information to reduce recognition errors; and how to use information from the speech signal, beyond just the sequence of words, as an aid to understanding. This paper reviews current research addressing these questions in the Spoken Language Program sponsored by the Advanced Research Projects Agency (ARPA). I begin by reviewing some of the ways that spontaneous spoken language differs from standard written language and discuss methods of coping with the difficulties of spontaneous speech. I then look at how systems cope with errors in speech recognition and at attempts to use natural language information to reduce recognition errors. Finally, I discuss how prosodic information in the speech signal might be used to improve understanding.