Modulatory effects of heparin and short-length oligosaccharides of heparin on the metastasis and growth of LMD MDA-MB 231 breast cancer cells in vivo

Expression of the chemokine receptor CXCR4 allows breast cancer cells to migrate towards specific metastatic target sites which constitutively express CXCL12. In this study, we determined whether this interaction could be disrupted using short-chain length heparin oligosaccharides. Radioligand competition binding assays were performed using a range of heparin oligosaccharides to compete with polymeric heparin or heparan sulphate binding to I125 CXCL12. Heparin dodecasaccharides were found to be the minimal chain length required to efficiently bind CXCL12 (71% inhibition; P<0.001). These oligosaccharides also significantly inhibited CXCL12-induced migration of CXCR4-expressing LMD MDA-MB 231 breast cancer cells. In addition, heparin dodecasaccharides were found to have less anticoagulant activity than either a smaller quantity of polymeric heparin or a similar amount of the low molecular weight heparin pharmaceutical product, Tinzaparin. When given subcutaneously in a SCID mouse model of human breast cancer, heparin dodecasaccharides had no effect on the number of lung metastases, but did however inhibit (P<0.05) tumour growth (lesion area) compared to control groups. In contrast, polymeric heparin significantly inhibited both the number (P<0.001) and area of metastases, suggesting a differing mechanism for the action of polymeric and heparin-derived oligosaccharides in the inhibition of tumour growth and metastases

An Estimation of Distribution Algorithm for Nurse Scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems

Algorithms for the Container Loading Problem

: In this paper we consider the three-dimensional problem of optimal packing of a container with rectangular pieces. We propose an approximation algorithm based on the &quot;forward state strategy&quot; of dynamic programming. A suitable description of packings is developed for the implementation of the approximation algorithm, and some computational experience is reported. Zusammenfassung: Zur naherungsweisen Losung von Containerbeladungsproblemen wird ein allgemeiner Algorithmus, der auf der &quot;Forward State Strategy&quot; der Dynamischen Optimierung basiert, vorgestellt. Eine fur die Implementierung passende Darstellung von teilweisen Packungen wird entwickelt und Ergebnisse einiger Testrechnungen werden angegeben. 1 Introduction and Problem Formulation The effective employment of capacity gets a more and more increasing importance in many problems of production and transportation planning. The reasons in transportation are e.g. the enlarging trade and growing transportation costs. In many cases t..