Transcription start site scanning and the requirement for ATP during transcription initiation by RNA polymerase II

Saccharomyces cerevisiae RNA polymerase (Pol) II locates transcription start sites (TSS) at TATA-containing promoters by scanning sequences downstream from the site of preinitiation complex formation, a process that involves the translocation of downstream promoter DNA toward Pol II. To investigate a potential role of yeast Pol II transcription in TSS scanning, HIS4 promoter derivatives were generated that limited transcripts in the 30-bp scanned region to two nucleotides in length. Although we found that TSS scanning does not require RNA synthesis, our results revealed that transcription in the purified yeast basal system is largely ATP-independent despite a requirement for the TFIIH DNA translocase subunit Ssl2. This result is rationalized by our finding that, although they are poorer substrates, UTP and GTP can also be utilized by Ssl2. ATPγS is a strong inhibitor of rNTP-fueled translocation, and high concentrations of ATPγS make transcription completely dependent on added dATP. Limiting Pol II function with low ATP concentrations shifted the TSS position downstream. Combined with prior work, our results show that Pol II transcription plays an important role in TSS selection but is not required for the scanning reaction

Kronecker products and the RSK correspondence

The starting point for this work is an identity that relates the number of minimal matrices with prescribed 1-marginals and coefficient sequence to a linear combination of Kronecker coefficients. In this paper we provide a bijection that realizes combinatorially this identity. As a consequence we obtain an algorithm that to each minimal matrix associates a minimal component, with respect to the dominance order, in a Kronecker product, and a combinatorial description of the corresponding Kronecker coefficient in terms of minimal matrices and tableau insertion. Our bijection follows from a generalization of the dual RSK correspondence to 3-dimensional binary matrices, which we state and prove. With the same tools we also obtain a generalization of the RSK correspondence to 3-dimensional integer matrices

Multiple Criteria Decision Making and Multiattribute Utility Theory

T his paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields

Success-First Decision Theories

The standard formulation of Newcomb's problem compares evidential and causal conceptions of expected utility, with those maximizing evidential expected utility tending to end up far richer. Thus, in a world in which agents face Newcomb problems, the evidential decision theorist might ask the causal decision theorist: "if you're so smart, why ain’cha rich?” Ultimately, however, the expected riches of evidential decision theorists in Newcomb problems do not vindicate their theory, because their success does not generalize. Consider a theory that allows the agents who employ it to end up rich in worlds containing Newcomb problems and continues to outperform in other cases. This type of theory, which I call a “success-first” decision theory, is motivated by the desire to draw a tighter connection between rationality and success, rather than to support any particular account of expected utility. The primary aim of this paper is to provide a comprehensive justification of success-first decision theories as accounts of rational decision. I locate this justification in an experimental approach to decision theory supported by the aims of methodological naturalism

iAggregator: Multidimensional Relevance Aggregation Based on a Fuzzy Operator

International audienceRecently, an increasing number of information retrieval studies have triggered a resurgence of interest in redefining the algorithmic estimation of relevance, which implies a shift from topical to multidimensional relevance assessment. A key underlying aspect that emerged when addressing this concept is the aggregation of the relevance assessments related to each of the considered dimensions. The most commonly adopted forms of aggregation are based on classical weighted means and linear combination schemes to address this issue. Although some initiatives were recently proposed, none was concerned with considering the inherent dependencies and interactions existing among the relevance criteria, as is the case in many real-life applications. In this article, we present a new fuzzy-based operator, called iAggregator, for multidimensional relevance aggregation. Its main originality, beyond its ability to model interactions between different relevance criteria, lies in its generalization of many classical aggregation functions. To validate our proposal, we apply our operator within a tweet search task. Experiments using a standard benchmark, namely, Text REtrieval Conference Microblog,1 emphasize the relevance of our contribution when compared with traditional aggregation schemes. In addition, it outperforms state-of-the-art aggregation operators such as the Scoring and the And prioritized operators as well as some representative learning-to-rank algorithms