2,6-Bis(1,4,7,10-tetraazacyclododecan-1-ylmethyl)pyridine and Its Benzene Analog as Nonmetallic Cleaving Agents of RNA Phosphodiester Linkages

2,6-Bis(1,4,7,10-tetraazacyclododecan-1-ylmethyl)pyridine (11a) and 1,3-bis(1,4,7,10-tetraazacyclododecan-1-ylmethyl)benzene (11b) have been shown to accelerate at 50 mmol·L⁻¹ concentration both the cleavage and mutual isomerization of uridylyl-3′,5′-uridine and uridylyl-2′,5′-uridine by up to two orders of magnitude. The catalytically active ionic forms are the tri- (in the case of 11b) tetra- and pentacations. The pyridine nitrogen is not critical for efficient catalysis, since the activity of 11b is even slightly higher than that of 11a. On the other hand, protonation of the pyridine nitrogen still makes 11a approximately four times more efficient as a catalyst, but only for the cleavage reaction. Interestingly, the respective reactions of adenylyl-3′,5′-adenosine were not accelerated, suggesting that the catalysis is base moiety selective.     

Constructing the market position of a project-based firm

Project marketing research has shown that project-based firms (PBFs) can favorably differentiate themselves from their competitors by developing a strong functional position and a strong relational position in a specific market milieu. Combined, these two interrelated positions constitute the market position of a project-based firm, and inter-organizational relationships (IORs) between a PBF and other actors in the milieu play an important role in its development. Active development of IORs to key actors simultaneously increases the effectiveness of the focal PBF in delivering solutions to its customers (functional position) and simultaneously enables it to occupy a stronger position in the milieu linking it to its customers (relational position). We present evidence from an empirical case focusing on how an automation system supplier gradually constructed a strong market position in the Russian oil and gas industry.     

Prevalence of digital dermatitis using mirror scoring in Finnish freestall dairy herds

Digital dermatitis (DD) is a severe bacterial hoof disease found worldwide. The disease can be classified into 5 different stages, denoted as M1 to M4 and M4.1, by clinical examination. The main objective of this study was to estimate prevalence of DD lesions in Finnish freestall dairy cattle population through hind feet inspection of standing cows with a mirror. Another aim was to estimate the sensitivity and specificity of mirror scoring on standing cows in a pen or in a milking parlor without washing the feet. Three veterinarians visited 81 randomly selected herds across the country. During the herd visits, hind feet of standing cows (n = 7,010) were scored with a mirror without washing the feet, either when the cows were standing in a pen (n = 4,992) or in the milking parlor (n = 2018). In total, 128 cows (111 from pen and 17 from milking parlor) including 256 feet were chosen with cross-sectional sampling and scored in a trimming chute. Animal-level sensitivity for scoring M2 lesions with a mirror was 55% and specificity was 97%; for all active DD lesions (M1, M2, or M4.1), sensitivity was 36% and specificity was 96%. Sensitivity for scoring any DD lesions was 90% and specificity was 82%. The bias-corrected sensitivity and specificity for scoring any DD lesions were 79% and 92%, respectively. The bias-corrected sensitivity and specificity for scoring M2 DD lesions were 10% and 100%. We found M2 lesions in 12.1% of the study herds, and true herd-level prevalence was the same. Altogether, 33.3% (true prevalence 28.4%) of the herds had either M1, M2, or M4.1 DD lesions. However, only 0.7% (true prevalence 5.4%) of cows in total had active M2 lesions. The within-herd prevalence of M2 lesions (in herds where at least 1 cow had a M2 lesion) was 5.7% and varied between 0.4% and 18.8%. Herds with active DD lesions also had more any DD lesions than herds without active DD lesions. The herd-level prevalence was higher than previously thought, with only 1 herd without any DD lesions. However, the animal-level prevalence of active DD lesions was relatively low. Farmers and veterinarians need to be informed of the disease and possible control measures. Because of the low within-herd prevalence, the control of the disease might be easier than in countries where DD is widespread. Further studies are needed to identify factors associated with DD prevalence in Finnish dairy herds.     

Persuasive software design patterns for social influence

This article describes software design techniques for social influence as software design patterns, instantiating social influence features defined in the persuasive systems design (PSD) model. The article draws on literature from PSD, social psychology and software patterns to derive the social influence patterns, which are then implemented in a conceptual system. This paves the way for an important research track within persuasive systems research. The ultimate aim of these persuasive software patterns was to develop generalizable techniques that could aid the development of social support features in any persuasive system.     

Design of a sampling strategy to optimally calibrate a reactive transport model: Exploring the potential for Escherichia coli in the Scheldt Estuary

For the calibration of any model, measurements are necessary. As measurements are expensive, it is of interest to determine beforehand which kind of samples will provide maximal information. Using a criterion related to the Fisher information matrix as a measure for information content, it is possible to design a sampling scheme that will enable the most precise parameter estimates. This approach was applied to a reactive transport model (based on the Second-generation Louvain-la-Neuve Ice-ocean Model, SLIM) of Escherichia coli concentrations in the Scheldt Estuary. As this estuary is highly influenced by the tide, it is expected that careful timing of the samples with respect to the tidal cycle can have an effect on the quality of the data. The timing and also the positioning of samples were optimised according to the proposed criterion. In the investigated case studies the precision of the estimated parameters could be improved by up to a factor of ten, confirming the usefulness of this approach to maximize the amount of information that can be retrieved from a fixed number of samples. Precise parameter values will result in more reliable model simulations, which can be used for interpretation, or can in turn serve to plan subsequent sampling campaigns to further constrain the model parameters.     

Compressing Two-Dimensional Routing Tables

We consider an algorithmic problem that arises in the context of routing tables used by Internet routers. The Internet addressing scheme is hierarchical, where a group of hosts are identified by a prefix that is common to all the hosts in that group. Each host machine has a unique 32-bit address. Thus, all traffic between a source group s and a destination group d can be routed along a particular route c by maintaining a routing entry (s, d, c) at all intermediate routers, where s and d are binary bit strings. Many different routing tables can achieve the same routing behavior. In this paper we show how to compute the most compact routing table. In particular, we consider the following optimization problem: given a routing table D with N entries of the form (s, d, c) , determine a conflict-free routing table with fewest entries that has the same routing behavior as D. If the source and destination fields have up to w bits, and there are at most K different route values, then our algorithm runs in worst-case time O( NK w ² ) .     

Simplified dynamic model for heat input and output of heat storing stoves

A simplified method to describe the dynamical heat output from heat storing stoves for small houses with a single or several successive firing batches is presented. Both measurements in a stove and theoretical and experimental studies of the combustion of single wood logs can be applied to study the heat input from the batch firing to the furnace walls. Different distributions for presenting heat input as function of time from the firing of a batch are discussed. Three different approaches can be made. The first is to present the measured heat input by fitting a statistical distribution such as lognormal or gamma distribution. The second approach is to apply a distribution that is based on the approximate combustion physics of a wood log. These two types of input distributions can be applied as a boundary condition for numerical simulation of heat output of different stove constructions. The third alternative is to select input distribution function, which is so simple that analytical solution for the heat output calculations of an idealized stove for approximate and fast simulation is possible.     

AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents

Two minimal requirements for a satisfactory multiagent learning algorithm are that it 1. learns to play optimally against stationary opponents and 2. converges to a Nash equilibrium in self-play. The previous algorithm that has come closest, WoLF-IGA, has been proven to have these two properties in 2-player 2-action (repeated) games—assuming that the opponent’s mixed strategy is observable. Another algorithm, ReDVaLeR (which was introduced after the algorithm described in this paper), achieves the two properties in games with arbitrary numbers of actions and players, but still requires that the opponents' mixed strategies are observable. In this paper we present AWESOME, the first algorithm that is guaranteed to have the two properties in games with arbitrary numbers of actions and players. It is still the only algorithm that does so while only relying on observing the other players' actual actions (not their mixed strategies). It also learns to play optimally against opponents that eventually become stationary. The basic idea behind AWESOME (Adapt When Everybody is Stationary, Otherwise Move to Equilibrium) is to try to adapt to the others' strategies when they appear stationary, but otherwise to retreat to a precomputed equilibrium strategy. We provide experimental results that suggest that AWESOME converges fast in practice. The techniques used to prove the properties of AWESOME are fundamentally different from those used for previous algorithms, and may help in analyzing future multiagent learning algorithms as well. Editors: Amy Greenwald and Michael Littman