Isolation of Lactoferrin and its Concentration in Sows’ Colostrum and Milk During a 21-Day Lactation

Levels of lactoferrin, an Fe-binding protein with bacteriostatic properties, were determined in the colostrum and milk of Yorkshire sows during a 21-d lactation. Lactoferrin levels averaged 1,100 to 1,300 μg/ml near the time of farrowing, then declined sharply during the first week of lactation. Concentration of lactoferrin showed considerable variation among sows, but not among teat positions (anterior to posterior). A method for isolating high purity swine lactoferrin is described

Die technische Infrastruktur zur Teilnahme von Unternehmen an Gemeinschaften in Neuen Medien

Ein Internet-Auftritt eines Unternehmens setzt voraus, daß bezüglich der internen Informations-Infrastruktur bestimmte organisatorische und technische Voraussetzungen erfüllt sind. In diesem Beitrag soll dargelegt werden, daß eine komponentenbasierte Anwendungsarchitektur wegen ihrer leichten Erweiterbarkeit und wegen ihrer Übersichtlichkeit den Internet-Auftritt und damit die Beteiligung an Medien-Gemeinschaften erheblich erleichtern kann. Dies wird anhand einer Prototypentwicklung dargestellt und um Sicherheitsbetrachtungen, die für einen professionellen Einsatz der entwickelten Konzepte unabdingbar sind, ergänzt

Subbarrel patterns in somatosensory cortical barrels can emerge from local dynamic instabilities

Complex spatial patterning, common in the brain as well as in other biological systems, can emerge as a result of dynamic interactions that occur locally within developing structures. In the rodent somatosensory cortex, groups of neurons called "barrels" correspond to individual whiskers on the contralateral face. Barrels themselves often contain subbarrels organized into one of a few characteristic patterns. Here we demonstrate that similar patterns can be simulated by means of local growth-promoting and growth-retarding interactions within the circular domains of single barrels. The model correctly predicts that larger barrels contain more spatially complex subbarrel patterns, suggesting that the development of barrels and of the patterns within them may be understood in terms of some relatively simple dynamic processes. We also simulate the full nonlinear equations to demonstrate the predictive value of our linear analysis. Finally, we show that the pattern formation is robust with respect to the geometry of the barrel by simulating patterns on a realistically shaped barrel domain. This work shows how simple pattern forming mechanisms can explain neural wiring both qualitatively and quantitatively even in complex and irregular domains. © 2009 Ermentrout et al

Providing a Robot with Learning Abilities Improves its Perception by Users

Subjective appreciation and performance evaluation of a robot by users are two important dimensions for Human- Robot Interaction, especially as increasing numbers of people become involved with robots. As roboticists we have to carefully design robots to make the interaction as smooth and enjoyable as possible for the users, while maintaining good performance in the task assigned to the robot. In this paper, we examine the impact of providing a robot with learning capabilities on how users report the quality of the interaction in relation to objective performance. We show that humans tend to prefer interacting with a learning robot and will rate its capabilities higher even if the actual performance in the task was lower. We suggest that adding learning to a robot could reduce the apparent load felt by a user for a new task and improve the user’s evaluation of the system, thus facilitating the integration of such robots into existing work flow

Supervised autonomy for online learning in human-robot interaction

When a robot is learning it needs to explore its environment and how its environment responds on its actions. When the environment is large and there are a large number of possible actions the robot can take, this exploration phase can take prohibitively long. However, exploration can often be optimised by letting a human expert guide the robot during its learning. Interactive machine learning, in which a human user interactively guides the robot as it learns, has been shown to be an effective way to teach a robot. It requires an intuitive control mechanism to allow the human expert to provide feedback on the robot’s progress. This paper presents a novel method which combines Reinforcement Learning and Supervised Progressively Autonomous Robot Competencies (SPARC). By allowing the user to fully control the robot and by treating rewards as implicit, SPARC aims to learn an action policy while maintaining human supervisory oversight of the robot’s behaviour. This method is evaluated and compared to Interactive Reinforcement Learning in a robot teaching task. Qualitative and quantitative results indicate that SPARC allows for safer and faster learning by the robot, whilst not placing a high workload on the human teacher