Continuous assay of proteases using a microtiter plate fluorescence reader

The present study was designed to determine if spinal calcium channels, calmodulin, and calcium/calmodulin-dependent protein kinase II were involved in the production of antinociception induced by cold water swimming stress (CWSS). The effects of intrathecal (i.t.) injection of nimodipine, omega-conotoxin GVIA, calmidazolium, or (S)-5-isoquinolinesulfonic acid, 4-[2-[(5-isoquinolinyl-sulfonyl)methylamino]-3-oxo-3-(4-phenyl-1-piperaz inyl)-propyl]phenyl ester (KN-62) on CWSS-induced antinociception were studied in ICR mice. The antinociception was assessed by the tail-flick test. CWSS produced inhibition of the tail-flick response. Various doses of nimodipine (10-40 ng), omega-conotoxin GVIA (5-40 ng), calmidazolium (10-40 ng), or KN-62 (5-40 ng) injected i.t. alone did not show any antinociceptive effect in the tail-flick test. I.t. pretreatment with omega-conotoxin GVIA, calmidazolium, or KN-62 dose dependently attenuated the CWSS-induced inhibition of the tail-flick response. However, i.t. pretreatment with nimodipine did not affect the inhibition of the tail-flick response induced by CWSS. Our results suggest that spinal N-type calcium channel, calmodulin and calcium/calmodulin-dependent protein kinase II may be involved in the production of antinociception induced by CWSS. On the other hand, CWSS-induced antinociception appears not to be mediated via the spinal L-type calcium channel.     

Sorption of copper ions by granulated chitosan

The features of sorption of Cu²⁺ ions from aqueous solutions of CuSO₄ by granulated chitosan sorbent in static conditions and the different contribution of surface and bulk amino groups in the sorbent to complexation are discussed. Sorption with freshly formed swollen and maximally amorphized granules with accessible bulk amino groups can be described by Langmuir’s equation; it attains 5.6 mmole/g of dry sorbent, which is close to the sorption calculated from the equimolar composition of the amino-copper complex. Sorption of initially dry granules with less accessible bulk amino groups does not obey Langmuir’s equation due to precipitation of copper hydroxide and hydrosulfate on the surface of the sorbent. __________ Translated from Khimicheskie Volokna, No. 2, pp. 11–14, March–April, 2006.     

Can a curved beam bistable mechanism have a secondary equilibrium that is more stable than its stress-free configuration?

A new concept for the geometry of a curved beam bistable micromechanical mechanism is proposed aiming at producing a secondary equilibrium state that is more stable (in terms of transformation force) than the primary, stress-free, configuration. The proposed geometry is simple and compatible with common MEMS mass-production processes. A parametric study is performed by means of a non-linear finite-elements analysis in order to study the post-buckling behavior and performance of the mechanism. Based on this analysis, we show that it is theoretically possible to have a mechanism that exhibits a secondary equilibrium state that is more stable than the stress-free configuration. We also find that the post-buckling response of the proposed design is very sensitive to a particular geometrical feature. Besides this particular weakness, the mechanism is rather robust with respect to geometrical inaccuracies. We report our findings with the hope of inspiring others to propose new ideas for overcoming this geometrical sensitivity.     

Multi-Class Segmentation with Relative Location Prior

Multi-class image segmentation has made significant advances in recent years through the combination of local and global features. One important type of global feature is that of inter-class spatial relationships. For example, identifying “tree” pixels indicates that pixels above and to the sides are more likely to be “sky” whereas pixels below are more likely to be “grass.” Incorporating such global information across the entire image and between all classes is a computational challenge as it is image-dependent, and hence, cannot be precomputed. In this work we propose a method for capturing global information from inter-class spatial relationships and encoding it as a local feature. We employ a two-stage classification process to label all image pixels. First, we generate predictions which are used to compute a local relative location feature from learned relative location maps. In the second stage, we combine this with appearance-based features to provide a final segmentation. We compare our results to recent published results on several multi-class image segmentation databases and show that the incorporation of relative location information allows us to significantly outperform the current state-of-the-art.     

Agent decision-making in open mixed networks

Computer systems increasingly carry out tasks in mixed networks, that is in group settings in which they interact both with other computer systems and with people. Participants in these heterogeneous human-computer groups vary in their capabilities, goals, and strategies; they may cooperate, collaborate, or compete. The presence of people in mixed networks raises challenges for the design and the evaluation of decision-making strategies for computer agents. This paper describes several new decision-making models that represent, learn and adapt to various social attributes that influence people's decision-making and presents a novel approach to evaluating such models. It identifies a range of social attributes in an open-network setting that influence people's decision-making and thus affect the performance of computer-agent strategies, and establishes the importance of learning and adaptation to the success of such strategies. The settings vary in the capabilities, goals, and strategies that people bring into their interactions. The studies deploy a configurable system called Colored Trails (CT) that generates a family of games. CT is an abstract, conceptually simple but highly versatile game in which players negotiate and exchange resources to enable them to achieve their individual or group goals. It provides a realistic analogue to multi-agent task domains, while not requiring extensive domain modeling. It is less abstract than payoff matrices, and people exhibit less strategic and more helpful behavior in CT than in the identical payoff matrix decision-making context. By not requiring extensive domain modeling, CT enables agent researchers to focus their attention on strategy design, and it provides an environment in which the influence of social factors can be better isolated and studied.     

Two-stage cascaded classification approach based on genetic fuzzy learning for speech/music discrimination

Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents a robust and effective approach for speech/music discrimination, which relies on a two-stage cascaded classification scheme. The cascaded classification scheme is composed of a statistical pattern recognition classifier followed by a genetic fuzzy system. For the first stage of the classification scheme, other widely used classifiers, such as neural networks and support vector machines, have also been considered in order to assess the robustness of the proposed classification scheme. Comparison with well-proven signal features is also performed. In this work, the most commonly used genetic learning algorithms (Michigan and Pittsburgh) have been evaluated in the proposed two-stage classification scheme. The genetic fuzzy system gives rise to an improvement of about 4% in the classification accuracy rate. Experimental results show the good performance of the proposed approach with a classification accuracy rate of about 97% for the best trial.     

A modelling framework to support farmers in designing agricultural production systems

Given the new challenges confronting world agriculture, innovative production systems need to be designed at the farm level. As experiments are not easy to conduct at this level, modelling is required to evaluate ex-ante the multiple impacts of proposed innovations. A conceptual framework based on three sub-systems (biophysical, technical, and decisional) interacting within the farm is proposed to guide this process. The advantages and limitations of the framework are illustrated with three examples. The first describes a conceptual, rule-based model representing management decisions for the cultivation of salad in southern France. The second uses a computerized model of the three sub-systems to study irrigation management strategies on farms producing hay in southern France. The third presents a spreadsheet model designed to assist dairy farms in Morocco to define new livestock and forage system strategies. The lessons learned concern the modelling process, the fit between the type of model and its use, and the advantage of involving stakeholders in the design process of tools and innovations.     

Of robot ants and elephants: A computational comparison

In the robotics community, there exist implicit assumptions concerning the computational capabilities of robots. Two computational classes of robots emerge as focal points of recent research: robot ants and robot elephants. Ants have poor memory and communication capabilities, but are able to communicate using pheromones, in effect, turning their work area into a shared memory. By comparison, elephants are computationally stronger, have large memory, and are equipped with strong sensing and communication capabilities. Unfortunately, not much is known about the relation between the capabilities of these models in terms of the tasks they can address. In this paper, we present formal models of both ants and elephants, and investigate if one dominates the other. We present two algorithms: AntEater, which allows elephant robots to execute ant algorithms and ElephantGun, which converts elephant algorithms-specified as Turing machines-into ant algorithms. By exploring the computational capabilities of these algorithms, we reach interesting conclusions regarding the computational power of both models.     

A framework for a video analysis tool for suspicious event detection

This paper proposes a framework to aid video analysts in detecting suspicious activity within the tremendous amounts of video data that exists in today’s world of omnipresent surveillance video. Ideas and techniques for closing the semantic gap between low-level machine readable features of video data and high-level events seen by a human observer are discussed. An evaluation of the event classification and detection technique is presented and a future experiment to refine this technique is proposed. These experiments are used as a lead to a discussion on the most optimal machine learning algorithm to learn the event representation scheme proposed in this paper.