Estudi de les addicions de Michael i aza-Michael. I, Catalitzades per lantànids. II, Catalitzades per un nou organocatalitzador suportat en nanopartícules d'or

Aquesta memòria es centra en un gran tema de la química orgànica que és la catàlisi asimètrica de les addicions de Michael. Més concretament, en aquest treball s’estudia la enantioselectivitat que proporcionen dos lligand en qüestió, un d’ells basat en metalls i l’altre organocatalític, en unes reaccions de Michael. Així doncs, s’estudiaren dos tipus de sistemes. El primer cas és l’addició de Michael entre 2-tert-butoxicarbonil-1-indanona i tres acceptors de Michael diferents com són la metilvinilcetona, el Nacriloïloxazolidinona i l’azodicarboxilat de di-tert-butil, catalitzat per combinacions de metalls lantànids (majoritariament Yb) amb lligands quirals de tipus pybox. La segona part de la memòria es centra en l’estudi d’enantioinducció que proporciona un nou organocatalitzador en les addicions de Michael esmentades en l’apartat anterior. Aquest catalitzador està format pel derivat de la cincona 11-(9-mercaptononiltio)-10,11-dihidrocinconina suportat en nanopartícules d’or.

Glutamic acid 286 in subunit I of cytochrome bo3 is involved in proton translocation

Glutamic acid 286 (E286; Escherichia coli cytochrome bo3 numbering) in subunit I of the respiratory heme-copper oxidases is highly conserved and has been suggested to be involved in proton translocation. We report a technique of enzyme reconstitution that yields essentially unidirectionally oriented cytochrome bo3 vesicles in which proton translocation can be measured. Such experiments are not feasible in the E286Q mutant due to strong inhibition of respiration, but this is not the case for the mutants E286D and E286C. The reconstituted E286D mutant enzyme readily translocates protons whereas E286C does not. Loss of proton translocation in the D135N mutant, but not in D135E or D407N, also is verified using proteoliposomes. Stopped-flow experiments show that the peroxy intermediate accumulates in the reaction of the E286Q and E286C mutant enzymes with O2. We conclude that an acidic function of the 286 locus is essential for the mechanism of proton translocation.

Dual-task interference: Attentional and neurophysiological influences

Performing two tasks simultaneously often degrades performance of one or both tasks. While this dual-task interference is classically interpreted in terms of shared attentional resources, where two motor tasks are performed simultaneously interactions within primary motor cortex (i.e., activity-dependent coupling) may also be a contributing factor. In the present study TMS (transcranial magnetic stimulation) was used to examine the contribution of activity-dependent coupling to dual-task interference during concurrent performance of a bimanual coordination task and a discrete probe reaction time (RT) task involving the foot. Experiments 1 and 2 revealed that activity-dependent coupling within the leg corticomotor pathway was greater during dual-task performance than single-task performance, and this was associated with interference on the probe RT task (i.e., increased RT). Experiment 3 revealed that dual-task interference occurred regardless of whether the dual-task involved two motor tasks or a motor and cognitive task, however activity-dependent coupling was present only when a dual motor task was performed. This suggests that activity-dependent coupling is less detrimental to performance than attentional processes operating upstream of the corticomotor system. Finally, while prioritising the RT task reduced, but did not eliminate, dual-task interference the contribution of activity-dependent coupling to dual-task interference was not affected by task prioritisation. This suggests that although activity-dependent coupling may contribute to dual motor-task interference, attentional processes appear to be more important. It also suggests that activity-dependent coupling may not be subject to modulation by attentional processes. (C) 2009 Elsevier B.V. All rights reserved.

The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations

A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of management actions is critical, and this can only be effectively derived from stochastic dynamic programming. The model illustrates the underlying difficulty in determining simple rules of thumb for the sequence of management actions for a metapopulation. This use of a decision theory framework extends the capacity of population viability analysis (PVA) to manage threatened species.

Vicarious dissonance: Attitude change from the inconsistency of others

Three studies support the vicarious dissonance hypothesis that individuals change their attitudes when witnessing members of important groups engage in inconsistent behavior. Study 1, in which participants observed an actor in an induced-compliance paradigm, documented that students who identified with their college supported an issue more after hearing an ingroup member make a counterattitudinal speech in favor of that issue. In Study 2, vicarious dissonance occurred even when participants did not hear a speech, and attitude change was highest when the speaker was known to disagree with the issue. Study 3 showed that speaker choice and aversive consequences moderated vicarious dissonance, and demonstrated that vicarious discomfort-the discomfort observers imagine feeling if in an actor's place-was attenuated after participants expressed their revised attitudes.

Triangulation by Continuous Embedding

When triangulating a belief network we aim to obtain a junction tree of minimum state space. Searching for the optimal triangulation can be cast as a search over all the permutations of the network's vaeriables. Our approach is to embed the discrete set of permutations in a convex continuous domain D. By suitably extending the cost function over D and solving the continous nonlinear optimization task we hope to obtain a good triangulation with respect to the aformentioned cost. In this paper we introduce an upper bound to the total junction tree weight as the cost function. The appropriatedness of this choice is discussed and explored by simulations. Then we present two ways of embedding the new objective function into continuous domains and show that they perform well compared to the best known heuristic.

Neural Networks

We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view learning algorithms as methods for finding parameter values that look probable in the light of the data. We discuss basic issues in representation and learning, and treat some of the practical issues that arise in fitting networks to data. We also discuss links between neural networks and the general formalism of graphical models.

Factorial Hidden Markov Models

We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum likelihood estimation. Analogous to the standard Baum-Welch update rules, the M-step of our algorithm is exact and can be solved analytically. However, due to the combinatorial nature of the hidden state representation, the exact E-step is intractable. A simple and tractable mean field approximation is derived. Empirical results on a set of problems suggest that both the mean field approximation and Gibbs sampling are viable alternatives to the computationally expensive exact algorithm.

Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks

Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework for compactly representing probabilistic information in a variety of unsupervised and supervised learning problems. Often the parameters used in these networks need to be learned from examples. Unfortunately, estimating the parameters via exact probabilistic calculations (i.e, the EM-algorithm) is intractable even for networks with fairly small numbers of hidden units. We propose to avoid the infeasibility of the E step by bounding likelihoods instead of computing them exactly. We introduce extended and complementary representations for these networks and show that the estimation of the network parameters can be made fast (reduced to quadratic optimization) by performing the estimation in either of the alternative domains. The complementary networks can be used for continuous density estimation as well.

Active Learning with Statistical Models

For many types of learners one can compute the statistically 'optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.

Learning from Incomplete Data

Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. The goal is two-fold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorithms, derived from the likelihood-based framework, that handle clustering, classification, and function approximation from incomplete data in a principled and efficient manner. These algorithms are based on mixture modeling and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster, Laird, and Rubin 1977)---both for the estimation of mixture components and for coping with the missing data.

On the Convergence of Stochastic Iterative Dynamic Programming Algorithms

Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(lambda) algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD(lambda) and Q-learning belong.

Hierarchical Mixtures of Experts and the EM Algorithm

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.

Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EMand the Minimum Spanning Tree algorithm to find the ML and MAP mixtureof trees for a variety of priors, including the Dirichlet and the MDL priors.