Microfluidic bioreactors for culture of non-adherent cells

Microfluidic bioreactors (μBR) are becoming increasingly popular for cell culture, sample preparation and analysis in case of routine genetic and clinical diagnostics. We present a novel μBR for non-adherent cells designed to mimic in vivo perfusion of cells based on diffusion of media through a sandwiched membrane. The culture chamber and perfusion chamber are separated by a sandwiched membrane and each chamber has separate inlet/outlets for easy loading/unloading of cells and perfusion of the media. The perfusion of media and exchange of nutrients occur through the sandwiched membrane, which was also verified with simulations. Finally, we present the application of this device for cytogenetic sample preparation, whereby we culture and arrest peripheral T-lymphocytes in metaphase and later fix them in the μBR. The expansion of T-lymphocytes from an unknown patient sample was quantified by means of CFSE staining and subsequent counting in a flow cytometer. To conclude on the applicability of μBR for genetic diagnostics, we prepare chromosome spreads on glass slides from the cultured samples, which is the primary step for metaphase FISH analysis.     

Searching for knee regions of the Pareto front using mobile reference points

Evolutionary Algorithms (EAs) have been recognized to be well suited to approximate the Pareto front of Multi-objective Optimization Problems (MOPs). In reality, the Decision Maker (DM) is not interested in discovering the whole Pareto front rather than finding only the portion(s) of the front that matches at most his/her preferences. Recently, several studies have addressed the decision-making task to assist the DM in choosing the final alternative. Knee regions are potential parts of the Pareto front presenting the maximal trade-offs between objectives. Solutions residing in knee regions are characterized by the fact that a small improvement in either objective will cause a large deterioration in at least another one which makes moving in either direction not attractive. Thus, in the absence of explicit DM’s preferences, we suppose that knee regions represent the DM’s preferences themselves. Recently, few works were proposed to find knee regions. This paper represents a further study in this direction. Hence, we propose a new evolutionary method, denoted TKR-NSGA-II, to discover knee regions of the Pareto front. In this method, the population is guided gradually by means of a set of mobile reference points. Since the reference points are updated based on trade-off information, the population converges towards knee region centers which allows the construction of a neighborhood of solutions in each knee. The performance assessment of the proposed algorithm is done on two- and three-objective knee-based test problems. The obtained results show the ability of the algorithm to: (1) find the Pareto optimal knee regions, (2) control the extent (We mean by extent the breadth/spread of the obtained knee region.) of the obtained regions independently of the geometry of the front and (3) provide competitive and better results when compared to other recently proposed methods. Moreover, we propose an interactive version of TKR-NSGA-II which is useful when the DM has no a priori information about the number of existing knees in the Pareto optimal front.     

Semi-supervised model applied to the prediction of the response to preoperative chemotherapy for breast cancer

Breast cancer is the second most frequent one, and the first one affecting the women. The standard treatment has three main stages: a preoperative chemotherapy followed by a surgery operation, then an post-operatory chemotherapy. Because the response to the preoperative chemotherapy is correlated to a good prognosis, and because the clinical and biological information do not yield to efficient predictions of the response, a lot of research effort is being devoted to the design of predictors relying on the measurement of genes’ expression levels. In the present paper, we report our works for designing genomic predictors of the response to the preoperative chemotherapy, making use of a semi-supervised machine learning approach. The method is based on margin geometric information of patterns of low density areas, computed on a labeled dataset and on an unlabeled one.     

epsilon- DANTE : an ant colony oriented depth search procedure

The eepsilon-Depth ANT Explorer (eepsilon- DANTE ) algorithm applied to a multiple objective optimization problem is presented in this paper. This method is a hybridization of the ant colony optimization algorithm with a depth search procedure, putting together an oriented/limited depth search. A particular design of the pheromone set of rules is suggested for these kinds of optimization problems, which are an adaptation of the single objective case. Six versions with incremental features are presented as an evolutive path, beginning in a single colony approach, where no depth search is applied, to the final eepsilon- DANTE . Versions are compared among themselves in a set of instances of the multiple objective Traveling Salesman Problem. Finally, our best version of eepsilon- DANTE is compared with several established heuristics in the field showing some promising results.     

Obtaining fuzzy rules from interval-censored data with genetic algorithms and a random sets-based semantic of the linguistic labels

Fuzzy memberships can be understood as coverage functions of random sets. This interpretation makes sense in the context of fuzzy rule learning: a random-sets-based semantic of the linguistic labels is compatible with the use of fuzzy statistics for obtaining knowledge bases from data. In particular, in this paper we formulate the learning of a fuzzy-rule-based classifier as a problem of statistical inference. We propose to learn rules by maximizing the likelihood of the classifier. Furthermore, we have extended this methodology to interval-censored data, and propose to use upper and lower bounds of the likelihood to evolve rule bases. Combining descent algorithms and a co-evolutionary scheme, we are able to obtain rule-based classifiers from imprecise data sets, and can also identify the conflictive instances in the training set: those that contribute the most to the indetermination of the likelihood of the model.     

Approximating Mexican highways with slime mould

Plasmodium of Physarum polycephalum is a single cell visible by unaided eye. During its foraging behavior the cell spans spatially distributed sources of nutrients with a protoplasmic network. The geometrical structure of the protoplasmic networks allows the plasmodium to optimize transport of nutrients between remote parts of its body. Assuming major Mexican cities are sources of nutrients that need to be distributed across Mexico, how much does the structure of the Physarum protoplasmic network correspond to the structure of Mexican Federal highway network? To address the issue we undertook a series of laboratory experiments with living P. polycephalum. We represent geographical locations of major cities (19 locations) by oat flakes, place a piece of plasmodium in the area corresponding to Mexico city, record the plasmodium’s foraging behavior and extract topology of the resulting nutrient transport networks. Results of our experiments show that the protoplasmic network formed by Physarum is isomorphic, subject to limitations imposed, to a network of principal highways. Ideas and results in the paper may contribute towards future developments in bio-inspired road planning.     

A computational modeling for real ecosystems based on P systems

In this paper, a P systems based general framework for modeling ecosystems dynamics is presented. Particularly, ecosystems are specified by means of multienvironment P systems composed of a finite number of environments, each of them having an extended P system with active membranes. The semantics is of a probabilistic type and it is implemented by assigning each rule of the system a probabilistic constant which depends on the environment and the run time. As a case study, two real ecosystems are described: scavenger birds in the Catalan Pyrenees and the zebra mussel (Dreissena Polymorpha) in Ribarroja reservoir (Spain).     

Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids

We address non-preemptive non-clairvoyant online scheduling of parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. We analyze allocation strategies depending on the type and amount of information they require. We conduct a comprehensive performance evaluation study using simulation and demonstrate that our strategies perform well with respect to several metrics that reflect both user- and system-centric goals. Unfortunately, user run time estimates and information on local schedules does not help to significantly improve the outcome of the allocation strategies. When examining the overall Grid performance based on real data, we determined that an appropriate distribution of job processor requirements over the Grid has a higher performance than an allocation of jobs based on user run time estimates and information on local schedules. In general, our experiments showed that rather simple schedulers with minimal information requirements can provide a good performance.     

Tree search and quantum computation

Traditional tree search algorithms supply a blueprint for modeling problem solving behaviour. A diverse spectrum of problems can be formulated in terms of tree search. Quantum computation, namely Grover’s algorithm, has aroused a great deal of interest since it allows for a quadratic speedup to be obtained in search procedures. In this work we consider the impact of incorporating classical search concepts alongside Grover’s algorithm into a hybrid quantum search system. Some of the crucial points examined include: (1) the reverberations of contemplating the use of non-constant branching factors; (2) determining the consequences of incorporating an heuristic perspective into a quantum tree search model.