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.     

Optimising by the response surface methodology the enzymatic elimination of clogging of a microfiltration membrane by pectin cake

The aim of this paper, in which clogging occurs on the filter at a constant vacuum pressure and constant pectin weight, is to find the optimal values of the variables, temperature and time, to know the best conditions to reduce this clogging by enzymatic hydrolysis. Moreover, different properties of the compression of the cake in the filter were determined such as the filtering medium total resistance (R_fT), bed porosity (ε) and the cake‐specific resistance (α) at different times and temperatures. The filter area was 0.142 m², and the value of the mass fraction of solids in the filtered suspension (S) was 0.12. Scanning electron microsopy (SEM) analyses were performed. The results showed that the compressive stress corresponding to the cake improves the flow rate in the filtration flux at the optimal temperature and time for the process, where the enzyme reaches its highest activity. The test results of this study can be applied in different system of filters during cross‐flow filtration.     

Cheaper membrane materials for microalgae dewatering

Among different strategies to reduce costs in microalgae dewatering process via cross-flow filtration, the one related to membrane material was investigated in order to be decreased. Several materials were tested, starting with the ones commonly used in membrane technology [ceramic, polysulfone (PSf) and polyacrylonitrile (PAN)] to the ones generally employed in packaging industry [acrylonitrile butadiene styrene (ABS), glycol-modified polyethylene terephthalate (PETG) and polylactic acid (PLA)], the latter being considerably cheaper. Experiments carried out showed promising results in terms of permeabilities for PSf–Pluronic^® F127 blended membranes and PAN membranes (11 ± 1 L/h/m²/bar and 22 ± 1 L/h/m²/bar, respectively, instead of 2 ± 2 L/h/m²/bar of PSf membranes), but with high related costs. PLA membranes showed good mechanical properties, biodegradability and price, but low permeability values (5 ± 1 L/h/m²/bar). PETG membranes showed attractive results in terms of costs and permeability, but poor mechanical properties. The polymer that offered the best results was the ABS that reached membrane permeabilities of 19 ± 1 L/h/m²/bar, maintaining good mechanical properties while filtering the microalgae Phaeodactylum tricornutum Bohlin. Thus, a novel functionality was found for these not so common polymers in microalgae dewatering. This indicates that use of these materials could also be considered in other aqueous micro/ultrafiltration applications. In addition, the biodegradable PLA polymer introduces a new concept of cheap and environmental friendly membrane in this application.     

Toward the prediction of porous membrane permeability from morphological data

One of the challenges in membrane technology is predicting permeability in porous membranes for liquid applications in an easy and inexpensive way. This is the aim of this work. To achieve this objective, several techniques can be considered. In this study, a morphological approach from two‐dimensional scanning electron micrographs is proposed. First, numerical membrane morphological parameters have been determined from micrographs by using the QUANTS tool, which applies a texture recognition process. Second, the obtained data have been fit to the Darcy's and Hagen–Poiseuille models to calculate permeations. The QUANTS results have also been compared with the ones obtained through a mercury porosimeter, which is a classic and well‐known methodology. Each parameter of the Hagen–Poiseuille model has been analyzed. A comparison between experimentally measured permeations and calculated ones has been performed. An even easier approach is proposed to predict flow rate with the only knowledge of membrane surface mean pore size. This method is based on cross‐section pore size interpolation by using function fits from surface mean pore sizes. The obtained results show a reasonable agreement between measured and computed results, making this technique a valid approach for predicting membrane permeability. POLYM. ENG. SCI., 56:118–124, 2016. © 2015 Society of Plastics Engineers     

Architecture-independent approximation of functions

We show that minimizing the expected error of a feedforward network over a distribution of weights results in an approximation that tends to be independent of network size as the number of hidden units grows. This minimization can be easily performed, and the complexity of the resulting function implemented by the network is regulated by the variance of the weight distribution. For a fixed variance, there is a number of hidden units above which either the implemented function does not change or the change is slight and tends to zero as the size of the network grows. In sum, the control of the complexity depends on only the variance, not the architecture, provided it is large enough.