Strategies for Continued Successful Treatment in Patients with Alzheimer's Disease : An Overview of Switching Between Pharmacological Agents

Altres ajuts: This review article is sponsored by Novartis Pharma K.K., Tokyo, Japan. The publication processing fees were funded by Novartis Pharma K.K., Tokyo, Japan.Alzheimer's disease (AD) is the most common cause of dementia, characterized by a progressive decline in cognition and function. Current treatment options for AD include the cholines-terase inhibitors (ChEIs) donepezil, galantamine, and rivastigmine, as well as the N-methyl-D-aspartate receptor antagonist memantine. Treatment guidelines recommend the use of ChEIs as the standard of care first-line therapy. Several randomized clinical studies have demonstrated the benefits of ChEIs on cogni-tion, global function, behavior and activities of daily living. However, patients may fail to achieve sus-tained clinical benefits from ChEIs due to lack/loss of efficacy and/or safety, tolerability issues, and poor adherence to the treatment. The purpose of this review is to explore the strategies for continued successful treatment in patients with AD. Literature search was performed for articles published in PubMed and MEDLINE, using pre-specified search terms. Articles were critically evaluated for inclusion based on their titles, abstracts, and full text of the publication. The findings of this review indicate that dose up-titration and switching between ChEIs may help to improve response to ChEI treatment and also address issues such as lack/loss of effica-cy or safety/tolerability in patients with AD. However, well-designed studies are needed to provide robust evidence

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

The glyoxal budget and its contribution to organic aerosol for Los Angeles, California, during CalNex 2010

Recent laboratory and field studies have indicated that glyoxal is a potentially large contributor to secondary organic aerosol mass. We present in situ glyoxal measurements acquired with a recently developed, high sensitivity spectroscopic instrument during the CalNex 2010 field campaign in Pasadena, California. We use three methods to quantify the production and loss of glyoxal in Los Angeles and its contribution to organic aerosol. First, we calculate the difference between steady state sources and sinks of glyoxal at the Pasadena site, assuming that the remainder is available for aerosol uptake. Second, we use the Master Chemical Mechanism to construct a two-dimensional model for gas-phase glyoxal chemistry in Los Angeles, assuming that the difference between the modeled and measured glyoxal concentration is available for aerosol uptake. Third, we examine the nighttime loss of glyoxal in the absence of its photochemical sources and sinks. Using these methods we constrain the glyoxal loss to aerosol to be 0-5 × 10-5 s-1 during clear days and (1 ± 0.3) × 10-5 s-1 at night. Between 07:00-15:00 local time, the diurnally averaged secondary organic aerosol mass increases from 3.2 μg m-3 to a maximum of 8.8 μg m -3. The constraints on the glyoxal budget from this analysis indicate that it contributes 0-0.2 μg m-3 or 0-4% of the secondary organic aerosol mass. Copyright 2011 by the American Geophysical Union

Developing a tool to support diagnostic delivery of dementia

It is increasingly recognised that there are challenges affecting the current delivery of dementia diagnoses. Steps are required to address this. Current good practice guidelines provide insufficient direction and interventions from other healthcare settings do not appear to fully translate to dementia care settings. This project has taken a sequential two-phase design to developing a tool specific to dementia diagnostic delivery. Interviews with 14 participants explored good diagnostic delivery. Thematic analysis produced key themes (overcoming barriers, navigation of multiple journeys and completing overt and covert tasks) that were used to inform the design of a tool for use by clinicians, patients and companions. The tool was evaluated for acceptability in focused group discussions with 13 participants, which indicated a desire to use the tool and that it could encourage good practice. Adaptations were highlighted and incorporated to improve acceptability. Future research is now required to further evaluate the tool

WARNING: Physics Envy May Be Hazardous To Your Wealth!

The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems - and financial markets in particular - that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, "physics envy" has also created a false sense of mathematical precision in some cases. We speculate on the origins of physics envy, and then describe an alternate perspective of economic behavior based on a new taxonomy of uncertainty. We illustrate the relevance of this taxonomy with two concrete examples: the classical harmonic oscillator with some new twists that make physics look more like economics, and a quantitative equity market-neutral strategy. We conclude by offering a new interpretation of tail events, proposing an "uncertainty checklist" with which our taxonomy can be implemented, and considering the role that quants played in the current financial crisis.Comment: v3 adds 2 reference

Coordinated optimization of visual cortical maps (II) Numerical studies

It is an attractive hypothesis that the spatial structure of visual cortical architecture can be explained by the coordinated optimization of multiple visual cortical maps representing orientation preference (OP), ocular dominance (OD), spatial frequency, or direction preference. In part (I) of this study we defined a class of analytically tractable coordinated optimization models and solved representative examples in which a spatially complex organization of the orientation preference map is induced by inter-map interactions. We found that attractor solutions near symmetry breaking threshold predict a highly ordered map layout and require a substantial OD bias for OP pinwheel stabilization. Here we examine in numerical simulations whether such models exhibit biologically more realistic spatially irregular solutions at a finite distance from threshold and when transients towards attractor states are considered. We also examine whether model behavior qualitatively changes when the spatial periodicities of the two maps are detuned and when considering more than 2 feature dimensions. Our numerical results support the view that neither minimal energy states nor intermediate transient states of our coordinated optimization models successfully explain the spatially irregular architecture of the visual cortex. We discuss several alternative scenarios and additional factors that may improve the agreement between model solutions and biological observations.Comment: 55 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:1102.335

Color Perception in Natural Images

This review of color processing in natural image viewing – rather than artificial laboratory images – addresses the role of color edges. Much of the color variation in nature is a result of evolutionary processes in complex organisms that have developed eye-brain systems that use color signals for a variety of biological functions. One aspect of human color processing is the tendency to attribute the appearance of extended color fields to a process of filling-in from the differential color signals at color edges, where one color transitions to another. Does such a process account for the appearance of extended color fields in natural images? Some form of color filling-in must underlie the color filling-in percept known as the Watercolor Effect, but this effect is too weak to account for the appearance of extended color fields in natural images. Moreover, natural images do not look very colorful when their color is restricted to edge transitions. Conversely, purely chromatic images with maximally graded (‘edgeless’) transitions look fully colorful, leading to the conclusion that color filling-in makes no more than a minor contribution to the appearance of extended color regions in natural images. Other effects, such as the selective enhancement of perceived image color by luminance contours coordinated with the color contours and color image structure, also play a role the color perception of natural images

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era