Measurement of interaction strength innature

Understanding and predicting the dynamics of multispecies systems generally require estimates of interaction strength among species. Measuring interaction strength is difficult because of the large number of interactions in any natural system, long-term feedback, multiple pathways of effects between species pairs, and possible nonlinearities in interaction-strength functions. Presently, the few studies that extensively estimate interaction strength suggest that distributions of interaction strength tend to be skewed toward few strong and many weak interactions. Modeling studies indicate that such skewed patterns tend to promote system stability and arise during assembly of persistent communities. Methods for estimating interaction strength efficiently from traits of organisms, such as allometric relationships, show some promise. Methods for estimating community response to environmental perturbations without an estimate of interaction strength may also be of use. Spatial and temporal scale may affect patterns of interaction strength, but these effects require further investigation and new multispecies modeling frameworks. Future progress will be aided by development of long-term multispecies time series of natural communities, by experimental tests of different methods for estimating interaction strength, and by increased understanding of nonlinear functional forms.

Abeta oligomer toxicity inhibitor protects memory in models of synaptic toxicity

BACKGROUND AND PURPOSE:

Amyloid-ß (Aß) aggregation into synaptotoxic, prefibrillar oligomers is a major pathogenic event underlying the neuropathology of Alzheimer's disease (AD). The pharmacological and neuroprotective properties of a novel Aß aggregation inhibitor, SEN1269, were investigated on aggregation and cell viability and in test systems relevant to synaptic function and memory, using both synthetic Aß(1-42) and cell-derived Aß oligomers.
EXPERIMENTAL APPROACH:

Surface plasmon resonance studies measured binding of SEN1269 to Aß(1-42) . Thioflavin-T fluorescence and MTT assays were used to measure its ability to block Aß(1-42) -induced aggregation and reduction in cell viability. In vitro and in vivo long-term potentiation (LTP) experiments measured the effect of SEN1269 on deficits induced by synthetic Aß(1-42) and cell-derived Aß oligomers. Following i.c.v. administration of the latter, a complex (alternating-lever cyclic ratio) schedule of operant responding measured effects on memory in freely moving rats.
KEY RESULTS:

SEN1269 demonstrated direct binding to monomeric Aß(1-42) , produced a concentration-related blockade of Aß(1-42) aggregation and protected neuronal cell lines exposed to Aß(1-42) . In vitro, SEN1269 alleviated deficits in hippocampal LTP induced by Aß(1-42) and cell-derived Aß oligomers. In vivo, SEN1269 reduced the deficits in LTP and memory induced by i.c.v. administration of cell-derived Aß oligomers.
CONCLUSIONS AND IMPLICATIONS:

SEN1269 protected cells exposed to Aß(1-42) , displayed central activity with respect to reducing Aß-induced neurotoxicity and was neuroprotective in electrophysiological and behavioural models of memory relevant to Aß-induced neurodegeneration. It represents a promising lead for designing inhibitors of Aß-mediated synaptic toxicity as potential neuroprotective agents for treating AD.

Feedforward Control of Shape Memory Alloy Actuators Using Fuzzy-Based Inverse Preisach Model

This brief investigates a possible application of the inverse Preisach model in combination with the feedforward and feedback control strategies to control shape memory alloy actuators. In the feedforward control design, a fuzzy-based inverse Preisach model is used to compensate for the hysteresis nonlinearity effect. An extrema input history and a fuzzy inference is utilized to replace the inverse classical Preisach model. This work allows for a reduction in the number of experimental parameters and computation time for the inversion of the classical Preisach model. A proportional-integral-derivative (PID) controller is used as a feedback controller to regulate the error between the desired output and the system output. To demonstrate the effectiveness of the proposed controller, real-time control experiment results are presented.

Optimal proportional-integral-derivative control of shape memory alloy actuators using genetic algorithm and the Preisach model

Shape memory alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics, and so on. Although the number of applications is increasing, there has been limited success in precise motion control owing to the hysteresis effect of these smart actuators. The present paper proposes an optimization of the proportional-integral-derivative (PID) control method for SMA actuators by using genetic algorithm and the Preisach hysteresis model.

Position control of shape memory alloy actuators using self tuning fuzzy PID controller

Shape Memory Alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

Optical Cooling Using the Dipole Force

The term `laser cooling' is applied to the use of optical means to cool the motional energies of either atoms and molecules, or micromirrors. In the literature, these two strands are kept largely separate; both, however suffer from severe limitations. Laser cooling of atoms and molecules largely relies on the internal level structure of the species being cooled. As a result, only a small number of elements and a tiny number of molecules can be cooled this way. In the case of micromirrors, the problem lies in the engineering of micromirrors that need to satisfy a large number of constraints---these include a high mechanical Q-factor, high reflectivity and very good optical quality, weak coupling to the substrate, etc.---in order to enable efficient cooling. During the course of this thesis, I will draw these two sides of laser cooling closer together by means of a single, generically applicable scattering theory that can be used to explain the interaction between light and matter at a very general level. I use this `transfer matrix' formalism to explore the use of the retarded dipole--dipole interaction as a means of both enhancing the efficiency of micromirror cooling systems and rendering the laser cooling of atoms and molecules less species selective. In particular, I identify the `external cavity cooling' mechanism, whereby the use of an optical memory in the form of a resonant element (such as a cavity), outside which the object to be cooled sits, can potentially lead to the construction of fully integrated optomechanical systems and even two-dimensional arrays of translationally cold atoms, molecules or even micromirrors.

Valved Dataflow For FPGA Memory Hierarchy Synthesis

For modern FPGA, implementation of memory intensive processing applications such as high end image and video processing systems necessitates manual design of complex multilevel memory hierarchies incorporating off-chip DDR and onchip BRAM and LUT RAM. In fact, automated synthesis of multi-level memory hierarchies is an open problem facing high level synthesis technologies for FPGA devices. In this paper we describe the first automated solution to this problem.
By exploiting a novel dataflow application modelling dialect, known as Valved Dataflow, we show for the first time how, not only can such architectures be automatically derived, but also that the resulting implementations support real-time processing for current image processing application standards such as H.264. We demonstrate the viability of this approach by reporting the performance and cost of hierarchies automatically generated for Motion Estimation, Matrix Multiplication and Sobel Edge Detection applications on Virtex-5 FPGA.

Automatic FPGA Synthesis of Memory Intensive C-based Kernels

Realising high performance image and signal processing
applications on modern FPGA presents a challenging implementation problem due to the large data frames streaming through these systems. Specifically, to meet the high bandwidth and data storage demands of these applications, complex hierarchical memory architectures must be manually specified
at the Register Transfer Level (RTL). Automated approaches which convert high-level operation descriptions, for instance in the form of C programs, to an FPGA architecture, are unable to automatically realise such architectures. This paper
presents a solution to this problem. It presents a compiler to automatically derive such memory architectures from a C program. By transforming the input C program to a unique dataflow modelling dialect, known as Valved Dataflow (VDF), a mapping and synthesis approach developed for this dialect can
be exploited to automatically create high performance image and video processing architectures. Memory intensive C kernels for Motion Estimation (CIF Frames at 30 fps), Matrix Multiplication (128x128 @ 500 iter/sec) and Sobel Edge Detection (720p @ 30 fps), which are unrealisable by current state-of-the-art C-based synthesis tools, are automatically derived from a C description of the algorithm.

Construction of Drug–Polymer Thermodynamic Phase Diagrams Using Flory–Huggins Interaction Theory: Identifying the Relevance of Temperature and Drug Weight Fraction to Phase Separation within Solid Dispersions

Amorphous drug-polymer solid dispersions have the potential to enhance the dissolution performance and thus bioavailability of BCS class II drug compounds. The principle drawback of this approach is the limited physical stability of amorphous drug within the dispersion. Accurate determination of the solubility and miscibility of drug in the polymer matrix is the key to the successful design and development of such systems. In this paper, we propose a novel method, based on Flory-Huggins theory, to predict and compare the solubility and miscibility of drug in polymeric systems. The systems chosen for this study are (1) hydroxypropyl methylcellulose acetate succinate HF grade (HPMCAS-HF)-felodipine (FD) and (2) Soluplus (a graft copolymer of polyvinyl caprolactam-polyvinyl acetate-polyethylene glycol)-FD. Samples containing different drug compositions were mixed, ball milled, and then analyzed by differential scanning calorimetry (DSC). The value of the drug-polymer interaction parameter ? was calculated from the crystalline drug melting depression data and extrapolated to lower temperatures. The interaction parameter ? was also calculated at 25 °C for both systems using the van Krevelen solubility parameter method. The rank order of interaction parameters of the two systems obtained at this temperature was comparable. Diagrams of drug-polymer temperature-composition and free energy of mixing (?G mix) were constructed for both systems. The maximum crystalline drug solubility and amorphous drug miscibility may be predicted based on the phase diagrams. Hyper-DSC was used to assess the validity of constructed phase diagrams by annealing solid dispersions at specific drug loadings. Three different samples for each polymer were selected to represent different regions within the phase diagram

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.