Evaluation of Floxuridine Oligonucleotide Conjugates Carrying Potential Enhancers of Cellular Uptake

Conjugation of small molecules such as lipids or receptor ligands to anti-cancer drugs has been used to improve their pharmacological properties. In this work, we studied the biological effects of several small-molecule enhancers into a short oligonucleotide made of five floxuridine units. Specifically, we studied adding cholesterol, palmitic acid, polyethyleneglycol (PEG 1000), folic acid and triantennary N-acetylgalactosamine (GalNAc) as potential enhancers of cellular uptake. As expected, all these molecules increased the internalization efficiency with different degrees depending on the cell line. The conjugates showed antiproliferative activity due to their metabolic activation by nuclease degradation generating floxuridine monophosphate. The cytotoxicity and apoptosis assays showed an increase in the anti-cancer activity of the conjugates related to the floxuridine oligomer, but this effect did not correlate with the internalization results. Palmitic and folic acid conjugates provide the highest antiproliferative activity without having the highest internalization results. On the contrary, cholesterol oligomers that were the best-internalized oligomers had poor antiproliferative activity, even worse than the unmodified floxuridine oligomer. Especially relevant is the effect induced by palmitic and folic acid derivatives generating the most active drugs. These results are of special interest for delivering other therapeutic oligonucleotides.     

A robot learning from demonstration framework to perform force-based manipulation tasks

This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human–robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher’s demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher’s one.     

Incremental Learning of Skills in a Task-Parameterized Gaussian Mixture Model

Programming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones used to estimate the model need to be added. One of the ways to re-train a robot is by incremental learning, which supplies additional information of the task and does not require teaching the whole task again. The present study proposes three techniques to add trajectories to a previously estimated task-parameterized Gaussian mixture model. The first technique estimates a new model by accumulating the new trajectory and the set of trajectories generated using the previous model. The second technique permits adding to the parameters of the existent model those obtained for the new trajectories. The third one updates the model parameters by running a modified version of the Expectation-Maximization algorithm, with the information of the new trajectories. The techniques were evaluated in a simulated task and a real one, and they showed better performance than that of the existent model.     

Effects of imperfect automation on decision making in a simulated command and control task

OBJECTIVE: Effects of four types of automation support and two levels of automation reliability were examined. The objective was to examine the differential impact of information and decision automation and to investigate the costs of automation unreliability. BACKGROUND: Research has shown that imperfect automation can lead to differential effects of stages and levels of automation on human performance. Method: Eighteen participants performed a "sensor to shooter" targeting simulation of command and control. Dependent variables included accuracy and response time of target engagement decisions, secondary task performance, and subjective ratings of mental work-load, trust, and self-confidence. RESULTS: Compared with manual performance, reliable automation significantly reduced decision times. Unreliable automation led to greater cost in decision-making accuracy under the higher automation reliability condition for three different forms of decision automation relative to information automation. At low automation reliability, however, there was a cost in performance for both information and decision automation. CONCLUSION: The results are consistent with a model of human-automation interaction that requires evaluation of the different stages of information processing to which automation support can be applied. APPLICATION: If fully reliable decision automation cannot be guaranteed, designers should provide users with information automation support or other tools that allow for inspection and analysis of raw data.     

A new methodology to solve non‐linear equation systems using genetic algorithms. Application to combined cyclegas turbine simulation

This paper shows a methodology to sort out the equations of a non‐linear system in order to solve it by the fixed‐point method. The arrangement of the equations is established by a genetic algorithm that deals with a population of possible resolution processes of the system. The method is specially useful in the following situations: first, when the system is very non‐linear and has many variables (where the Newton–Raphson method does not work properly); second, when the number of equations and variables may be altered because the equation system may change in each simulation and, therefore, more than one only solution process is needed if the fixed‐point process is employed. As an example, the methodology has been applied to solve the equation system that models the behaviour of a heat recovery steam generator of a combined cycle power plant at full load and part load conditions. Copyright © 2005 John Wiley & Sons, Ltd.     

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.     

Food safety management system performance in the lamb chain

This study describes a performance measurement of implemented food safety management system (FSMS) along the lamb chain using an FSMS-diagnostic instrument (FSMS-DI) and a Microbiological Assessment Scheme (MAS). Three slaughterhouses, 1 processing plant and 5 butcher shops were evaluated. All the actors along the lamb chain achieved a moderate risky contextual situation, operating in a basic-average FSMS, which was not enough to obtain a good food safety output. Different suggestions are advised for each actor along the lamb chain for improvements towards higher FSMS activity levels or lower risk levels in context characteristics. The combined assessment is a useful tool to identify the possible causes of poor food safety performance in the lamb chain using few sampling locations, saving time and money.