Functionalizable composite nanoparticles as a dual magnetic resonance imaging/computed tomography contrast agent for medical imaging

A dual contrast agent for computed tomography (CT) and magnetic resonance imaging (MRI) was synthesized via microemulsion polymerization. This contrast agent consists of Fe₃O₄ particles (d = 7 nm) with an iodine-carrying nanopolymeric shell, with overall particle sizes ranging from 50 to 250 nm. 2-Methacryloyloxyethyl(2,3,5-triiodobenzoate) was used as the monomer. Sodium oleate was used as the surfactant and its amount was varied to control the overall particle size. The composite nanoparticles were mainly characterized via dynamic light scattering, with further analyses using transmission electron microscopy and atomic force microscopy. The particles provided a highly visible contrast in CT and MR images. A template for biomedical applications was created by adding a comonomer and the particles were further functionalized with the somatostatin analogue Tyr³-octreotate. The particles were tested for specific uptake into somatostatin receptor-positive AR42J cells. The additional uptake of the functionalized particles was investigated. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47571.     

Good edit similarity learning by loss minimization

Similarity functions are a fundamental component of many learning algorithms. When dealing with string or tree-structured data, measures based on the edit distance are widely used, and there exist a few methods for learning them from data. However, these methods offer no theoretical guarantee as to the generalization ability and discriminative power of the learned similarities. In this paper, we propose an approach to edit similarity learning based on loss minimization, called GESL. It is driven by the notion of (?,??,??)-goodness, a theory that bridges the gap between the properties of a similarity function and its performance in classification. Using the notion of uniform stability, we derive generalization guarantees that hold for a large class of loss functions. We also provide experimental results on two real-world datasets which show that edit similarities learned with GESL induce more accurate and sparser classifiers than other (standard or learned) edit similarities.     

Some natural flavonoids are competitive inhibitors of Caspase-1, -3 and -7 despite their cellular toxicity

A common feature of both apoptosis and inflammation is the activation of caspases. Caspases are aspartate-directed cysteine proteases that have numerous cellular targets. It has been discovered that several flavonoids are inhibitors of caspases. Flavonoids are members of a family of polyphenolic compounds from plants that have many biological properties, one of which is the ability to induce cell death. Some flavonoids are selective inhibitors of particular caspases. Since some of the inhibitory flavonoids are nevertheless cytotoxic, these results suggest that flavonoid-induced cell death may be occurring through a non-classical apoptosis pathway that is not dependent on caspase activity.     

Design of a cost-effective on-grid hybrid wind–hydrogen based CHP system using a modified heuristic approach

An economic model and optimization procedure is developed in this paper for grid-connected hybrid wind–hydrogen combined heat and power systems for residential applications in northeastern Iran. The model considers various significant factors: energy production cost, electrical trade with local grid, electrical power generation from the wind/hydrogen energy system, thermal recovery from the fuel cell, and maintenance. Also, various tariffs for purchasing and selling electrical energy from the local grid are considered for the hybrid system operation. The optimization objective is to minimize the system total cost subject to relevant constraints for residential applications. To achieve this aim, an efficient optimization method is proposed based on particle swarm optimization. The proposed algorithm performance is compared with that for the imperialist competition algorithm. The results show that the hybrid system is the most cost-effective for the residential load, and the results of the proposed algorithm are more promising than those for the alternative algorithm.     

A robust bus evacuation model with delayed scenario information

Due to natural or man-made disasters, the evacuation of a whole region or city may become necessary. Apart from private traffic, the emergency services also need to consider transit-dependent evacuees which have to be transported from collection points to secure shelters outside the endangered region with the help of a bus fleet. We consider a simplified version of the arising bus evacuation problem (BEP), which is a vehicle scheduling problem that aims at minimizing the network clearance time, i.e., the time needed until the last person is brought to safety. In this paper, we consider an adjustable robust formulation without recourse for the BEP, the robust bus evacuation problem (RBEP), in which the exact numbers of evacuees are not known in advance. Instead, a set of likely scenarios is known. After some reckoning time, this uncertainty is eliminated and planners are given exact figures. The problem is to decide for each bus, if it is better to send it right away—using uncertain information on the evacuees—or to wait until the the scenario becomes known. We present a mixed-integer linear programming formulation for the RBEP and discuss solution approaches; in particular, we present a tabu search framework for finding heuristic solutions of acceptable quality within short computation time. In computational experiments using both randomly generated instances and the real-world scenario of evacuating the city of Kaiserslautern, Germany, we compare our solution approaches.