Non-Systematic Evolution of Ligands by EXponential enrichment (SELEX)selection of aptamers, a novel technology for aptamer selection from libraries of random DNA (or RNA) sequences, involves repetitive steps of partitioning without polymerase chain reaction (PCR) amplification between them. This selection is based on non-equilibrium capillary electrophoresis of equilibrium mixtures (NECEEM) and has exceptionally high efficiency. In this paper, a mathematical analysis was carried out to predict the levels of enrichment of non-SELEX selection under different conditions such as different protein concentrations and different efficiencies of partitioning. Investigated results suggest that the magnitude of the bulk affinity (k d) being 10(4) or 10(5) μM for the initial pool has no obvious effect on selective enrichment and that the first, second, and third rounds of non-SELEX selection have different optimum protein concentration values [T f] that give maximum enrichment levels when [T f] ranges from 0.0005 to 0.5 μM. The significance of analyzing selective enrichment of NECEEM-based non-SELEX with the efficiency of partitioning target-bound ligands from free ligands has been demonstrated.

BackgroundSynthesizing and characterizing aptamers with high affinity and specificity have been extensively carried out for analytical and biomedical applications. Few publications can be found that describe structure–activity relationships (SARs) of candidate aptamer sequences.MethodologyThis paper reports pattern recognition with support vector machine (SVM) classification techniques for the identification of streptavidin-binding aptamers as “low” or “high” affinity aptamers. The SVM parameters C and γ were optimized using genetic algorithms. Four descriptors, the topological descriptor PW4 (path/walk 4 - Randic shape index), the connectivity index X3A (average connectivity index chi-3), the topological charge index JGI2 (mean topological charge index of order 2), and the free energy E of the secondary structure, were used to describe the structures of candidate aptamer sequences from SELEX selection (Schütze et al. (2011) PLoS ONE (12):e29604).ConclusionsThe predicted fractions of winning streptavidin-binding aptamers for ten rounds of SELEX conform to the aptamer evolutionary principles of SELEX-based screening. The feasibility of applying pattern recognition based on SVM and genetic algorithms for streptavidin-binding aptamers has been demonstrated.

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The selection of optimal search effort for air-sea integrated search has become the most concerned issue for maritime search and rescue (MSAR) departments. Helicopters play an important role in maritime search because of their strong maneuverability and hovering ability. In this work, the requirements of maritime search were analyzed, from which a global optimization model with quantitative constraints for vessels and aircraft was developed by setting the least search time as single-objective optimization problem; then the improved Dinkelbach algorithm was used to solve the continuous programming problem, and the discrete mission planning algorithm was used to improve the calculation accuracy of search time and area. A case study shows that the errors in calculating search time and area decrease from 12–18 min to 36 s and from 76.5 to 0.45 n mile2, respectively. The results obtained from the discrete mission planning algorithm can provide better guidance for MASR departments in selecting optimal search scheme.

Due to the lack of real multi-agent data and timeconsuming of labeling, existing multi-agent cooperative perception algorithms usually select the simulated sensor data for training and validating. However, the perception performance is degraded when these simulation-trained models are deployed to the real world, due to the significant domain gap between the simulated and real data. In this paper, we propose the first Simulation-to-Reality transfer learning framework for multiagent cooperative perception using a novel Vision Transformer, named as S2R-ViT, which considers both the Implementation Gap and Feature Gap between simulated and real data. We investigate the effects of these two types of domain gaps and propose a novel uncertainty-aware vision transformer to effectively relief the Implementation Gap and an agentbased feature adaptation module with inter-agent and egoagent discriminators to reduce the Feature Gap. Our intensive experiments on the public multi-agent cooperative perception datasets OPV2V and V2V4Real demonstrate that the proposed S2R-ViT can effectively bridge the gap from simulation to reality and outperform other methods significantly for point cloud-based 3D object detection.