Reified temporal logics: an overview

There are three main approaches to the representation of temporal information in AI literature: the so-called method of temporal arguments that simply extends functions and predicates of first-order language to include time as the additional argument; modal temporal logics which are extensions ofthe propositional or predicate calculus with modal temporal operators; and reified temporal logics which reify standard propositions of some initial language (e.g., the classical first-order or modal logic) as objects denoting propositional terms. The objective of this paper is to provide an overview onthe temporal reified approach by looking closely atsome representative existing systems featuring reified propositions, including those of Allen, McDermott, Shoham, Reichgelt, Galton, and Ma and Knight. We shall demonstrate that, although reified logics might be more complicated in expressing assertions about some given objects with respect to different times, they accord a special status to time and therefore have several distinct advantages in talking about some important issues which would be difficult (if not impossible) to express in other approaches.

Representing temporal relationships between events and their effects

Temporal relationships between events and their effects are complex. As the effects of a given event, a proposition may change its truth value immediately after the occurrence of the event and remain true until some other events occur, while another proposition may only become true/false from some time after the causal event has occurred. Expressing delayed effects of events has been a problematic question in most existing theories of action and change. This paper presents a new formalism for representing general temporal causal relationships between events and their effects. It allows expressions of both immediate and delayed effects of events, and supports common-sense assertions such as "effects cannot precede their causes".

A framework for state-based time-series analysis and prediction

Time-series analysis and prediction play an important role in state-based systems that involve dealing with varying situations in terms of states of the world evolving with time. Generally speaking, the world in the discourse persists in a given state until something occurs to it into another state. This paper introduces a framework for prediction and analysis based on time-series of states. It takes a time theory that addresses both points and intervals as primitive time elements as the temporal basis. A state of the world under consideration is defined as a set of time-varying propositions with Boolean truth-values that are dependent on time, including properties, facts, actions, events and processes, etc. A time-series of states is then formalized as a list of states that are temporally ordered one after another. The framework supports explicit expression of both absolute and relative temporal knowledge. A formal schema for expressing general time-series of states to be incomplete in various ways, while the concept of complete time-series of states is also formally defined. As applications of the formalism in time-series analysis and prediction, we present two illustrating examples.

A sensitive bioassay for the mycotoxin aflatoxin B1, which also responds to the mycotoxins aflatoxin G1 and T-2 toxin, using engineered baker's yeast

A novel aflatoxin B(1) bioassay was created by introducing a Lipomyces kononenkoae alpha-amylase gene into a strain of S. cerevisiae capable of expressing the human cytochrome P450 3A4 (CYP3A4), and the cognate human CYP450 reductase. This strain and a dextranase-expressing strain were used in the development of a microtitre plate mycotoxin bioassay, which employed methanol as the solvent and polymyxin B nonapeptide as a permeation enhancer. Stable co-expression of the CYP3A4 gene system and of the dextranase and amylase genes in the two bioassay strains was demonstrated. The bioassay signalled toxicity as inhibition of secreted carbohydrase activity, using sensitive fluorimetric assays. The amylase-expressing strain could detect aflatoxin B(1) at 2 ng/ml, and was more sensitive than the dextranase-expressing strain. Aflatoxin G(1) could be detected at 2 microg/ml, and the trichothecene mycotoxin T-2 toxin was detectable at 100 ng/ml.