Enantiomer separation by countercurrent chromatography using cinchona alkaloid derivatives as chiral selectors

Cinchona-derived anion-exchange-type chiral selectors have been adapted and employed in countercurrent chromatography (CCC) for the separation of enantiomers of N-derivatized amino acids and 2-aryloxypropionic acids. The accurate optimization of the enantioseparation in terms of solvent system composition, pH values, ionic strength, and CCC operating conditions was performed. A wide range of solvent mixtures was evaluated. Successful resolutions were achieved in systems such as ammonium acetate buffer/tert-amyl alcohol/methanol/heptane and especially ammonium acetate buffer/methyl isobutyl ketone or diisopropyl ether. Up to 300 mg (0.92 mmol) of N-(3,5-dinitrobenzoyl)-(+/-)-leucine was totally resolved in a single run using a 10 mM concentration of chiral selector in 122 mL of stationary phase. This amount could be increased up to 900 mg (2.77 mmol) when pH-zone-refining mode was applied. The results here presented account for the high potential of CCC as a preparative enantiomer separation technique.     

Detection of objects composed of several regions by a region-configuration-estimation method

The task of detection of objects composed of several regions by means of statistical filters is analyzed. The target is assumed to have different unknown mean values in each of its regions. The detection is based on likelihood estimation, after performing an estimation of the actual configuration of the mean values in the target region. A simplified filter that reduces the computational complexity is also proposed. The statistical performance is analyzed theoretically and tested in computer experiments.     

Enhanced osteoinductive capacity and decreased variability by enrichment of demineralized bone matrix with a bone protein extract

Osteoinductive capacity of demineralized bone matrix (DBM) is sometimes insufficient or shows high variability between different batches of DBM. Here, we tried to improve its osteoinductive activity by alkali-urea or trypsin treatment but this strategy was unsuccessful. Then, we tested the enrichment of DBM with a bone protein extract (BPE) containing osteogenic growth factors comparing two sources: cortical bone powder and DBM. The osteoinductive capacity (alkaline phosphatase activity) of the obtained BPEs was evaluated in vitro in C2C12 cells. Specific protein levels present in the different BPE was determined by enzyme-linked immunosorbent assay or by a multiplex assay. BPE from cortical bone powder showed a lack of osteoinductive effect, in agreement with the low content on osteoinductive factors. In contrast, BPE from DBM showed osteoinductive activity but also high variability among donors. Thus, we decided to enrich DBM with BPE obtained from a pool of DBM from different donors. Following this strategy, we achieved increased osteoinductive activity and lower variability among donors. In conclusion, the use of a BPE obtained from a pool of demineralized bone to enrich DBM could be used to increase its osteoinductive effect and normalize the differences between donors.

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Drop Tower Setup to Study the Diffusion-driven Growth of a Foam Ball in Supersaturated Liquids in Microgravity Conditions

The diffusion-driven growth of a foam ball is a phenomenon that appears in many manufacturing process as well as in a variety of geological phenomena. Usually these processes are greatly affected by gravity, as foam is much lighter than the surrounding liquid. However, the growth of the foam free of gravity effects is still very relevant, as it is connected to manufacturing in space and to the formation of rocks in meteorites and other small celestial bodies. The aim of this research is to investigate experimentally the growth of a bubble cloud growing in a gas-supersaturated liquid in microgravity conditions. Here, we describe the experiments carried out in the drop tower of the Center of Applied Space Technology and Microgravity (ZARM). In few words, a foam seed is formed with spark-induced cavitation in carbonated water, whose time evolution is recorded with two high-speed cameras. Our preliminary results shed some light on how the size of the foam ball scales with time, in particular at times much longer than what could be studied in normal conditions, i.e. on the surface of the Earth, where the dynamics of the foam is already dominated by gravity after several milliseconds.     

Embedding Arabidopsis Plant Cell Suspensions in Low-Melting Agarose Facilitates Altered Gravity Studies

Gravity plays a role in modulating plant growth and development and its alteration induces changes in these processes. Microgravity research has recently been extended to the use of in vitro plant cell cultures which are considered as an ideal model system to study cell proliferation and growth. In general, among the ground-based facilities available for microgravity simulation, the 2D pipette clinostat had been previously considered a suitable facility to be used for unicellular biological models although studies using single plant cell cultures raised some concerns. The incompatibility comes from the standard requirement of shaking a suspension culture for assuring its viability and active proliferation status in the control samples. Moreover, a related issue applies to the use of the random positioning machine (RPM) for cell suspension experiments. Here, we demonstrate an alternative culture method based on the immobilization of the culture before the altered gravity treatment occurs, such that it behaves as a solid object. Our immobilization procedure preserved plant cell culture viability without compromising basic cell properties as viability, morphology, cell cycle phases distribution, or chromatin organization, when compared with a standard cell suspension under shaking as a control. This approach should allow the space biology community to improve the quantity and quality of plant cell results in future simulated microgravity experiments or spaceflight opportunities.     

A comparative study of lignocellulosic ethanol productivities by <Emphasis Type="Italic">Kluyveromyces marxianus</Emphasis> and <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis>

The yeast Saccharomyces cerevisiae is commonly employed in industrial ethanol production, regardless of the capability of Kluyveromyces marxianus strains to produce ethanol at similar or higher levels and on inhibitory conditions. Therefore, in this work strains of S. cerevisiae (ethanol RED and AR5) and K. marxianus (SLP1 and OFF1) were compared for ethanol production from sugarcane bagasse (SCB) and wheat straw (WS) hydrolysates. As it is known, during the lignocellulosic hydrolysis not only free sugars were obtained (SCB, g L^?1: glucose 7.64, xylose 8.38, arabinose 2.43; and WS, g L^?1: glucose 6.07, xylose 6.36, arabinose 2.09) but also growth inhibitors of yeast such as hydroxymethylfurfural and furfural that could modify the fermentation capability. The volumetric ethanol productivity (Q _p) was evaluated, and it was observed that the K. marxianus SLP1 was the most efficient for ethanol production reaching a Q _p of 0.292 and 0.250 g L^?1 h^?1 on SCB and WS hydrolysates, respectively. In contrast, S. cerevisiae AR5 and ethanol RED exhibited a reduced Q _p on SCB, but similar values of Q _p to K. marxianus OFF1 on WS. The results obtained show that it is possible to select K. marxianus yeast strains for ethanol production using SCB and WS hydrolysates obtaining higher Q _p than S. cerevisiae yeast strains. Considering the efficiency of ethanol production and the tolerance to inhibitors, K. marxianus strain SLP1 possesses a great potential as an industrial yeast for lignocellulosic ethanol production.     

Medical doctor rostering problem in a hospital emergency department by means of genetic algorithms

Organising shifts, or work rosters, is a problem that affects a large number of businesses where employees are subject to some kind of work rotation. Researchers in the fields of Operations Research and Artificial Intelligence have resorted to several different optimisation systems to solve the problem. The motivation for the medical-staff shift-rotation research presented in this paper stems from the needs of an actual hospital emergency department (HED) and from the observed growing staff of these services in Spain. The problem approach, which has been hardly dealt with in the literature, intends to automate the creation of time-tables by applying genetic algorithms (GAs) in an actual HED. HEDs work organisation becomes different because of the combination of shifts and 24-h duties. After knowing the HED workers’ requirements (which will allow to identify the hard and soft constraints imposed to the problem) and after defining the adequate encoding to be used in the solutions, a heuristic-schedule builder –designed ad hoc to satisfy the hard constraints – produces an initial population of feasible solutions. Afterwards, iteratively, GA obtains new generations of feasible individuals, thanks to the use of a specific crossover operator, based in the exchange of whole work weeks, that operates together with a repair function. Once the optimum is reached, the results obtained are discussed as a function of the degree of satisfaction of the constraints under which the system operates and of the adaptability of the system as the constraints vary.     

A method to learn the inverse kinematics of multi-link robots by evolving neuro-controllers

A general method to learn the inverse kinematic of multi-link robots by means of neuro-controllers is presented. We can find analytical solutions for the most used and well-known robots in the literature. However, these solutions are specific to a particular robot configuration and are not generally applicable to other robot morphologies. The proposed method is general in the sense that it is independent of the robot morphology. The method is based on the evolutionary computation paradigm and works obtaining incrementally better neuro-controllers. Furthermore, the proposed method solves some specific issues in robotic neuro-controller learning: it avoids any neural network learning algorithm which relies on the classical supervised input-target learning scheme and hence it lets to obtain neuro-controllers without providing targets. It can converge beyond local optimal solutions, which is one of the main drawbacks of some neural network training algorithms based on gradient descent when applied to highly redundant robot morphologies. Furthermore, using learning algorithms such as the neuro-evolution of augmenting topologies it is also possible to learn the neural network topology which is a common source of empirical testing in neuro-controllers design. Finally, experimental results are provided when applying the method to two multi-link robot learning tasks and a comparison between structural and parametric evolutionary strategies on neuro-controllers is shown.     

Self-modeling in humanoid soccer robots

In this paper we discuss the applicability, potential benefits, open problems and expected contributions that an emerging set of self-modeling techniques might bring on the development of humanoid soccer robots. The idea is that robots might continuously generate, validate and adjust physical models of their sensorimotor interaction with the world. These models are exploited for adapting behavior in simulation, enhancing the learning skills of a robot with the regular transference of controllers developed in simulation to reality. Moreover, these simulations can be used to aid the execution of complex sensorimotor tasks, speed up adaptation and enhance task planning. We present experiments on the generation of behaviors for humanoid soccer robots using the Back-to-Reality algorithm. General motivations are presented, alternative algorithms are discussed and, most importantly, directions of research are proposed.