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A series of compounds were designed and synthesized as antagonists of cIAP1/2, ML-IAP, and XIAP based on the N-terminus, AVPI, of mature Smac. Compound 1 (GDC-0152) has the best profile of these compounds; it binds to the XIAP BIR3 domain, the BIR domain of ML-IAP, and the BIR3 domains of cIAP1 and cIAP2 with Ki values of 28, 14, 17 and 43 nM, respectively. These compounds promote degradation of cIAP1, induce activation of caspase-3/7, and lead to decreased viability of breast cancer cells without affecting normal mammary epithelial cells. Compound 1 inhibits tumor growth when dosed orally in the MDA-MB-231 breast cancer xenograft model. Compound 1 was advanced to human clinical trials and it exhibited linear pharmacokinetics over the dose range (0.049 to 1.48 mg/kg) tested. Mean plasma clearance in humans was 9 ± 3 mL/min/kg and volume of distribution was 0.6 ± 0.2 L/kg.

This paper is the first part of a comprehensive survey on pickup and delivery problems. Basically, two problem classes can be distinguished. The first class, discussed in this paper, deals with the transportation of goods from the depot to linehaul customers and from backhaul customers to the depot. This class is denoted as Vehicle Routing Problems with Backhauls (VRPB). Four subtypes can be considered, namely the Vehicle Routing Problem with Clustered Backhauls (VRPCB -all linehauls before backhauls), the Vehicle Routing Problem with Mixed linehauls and Backhauls (VRPMB -any sequence of linehauls and backhauls permitted), the Vehicle Routing Problem with Divisible Delivery and Pickup (VRPDDP -customers demanding delivery and pickup service can be visited twice), and the Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP -customers demanding both services have to be visited exactly once). The second class, dealt with in the second part of this survey, refers to all those problems where goods are transported between pickup and delivery locations. These are the Pickup and Delivery Vehicle Routing Problem (PDVRP -unpaired pickup and delivery points), the classical Pickup and Delivery Problem (PDP -paired pickup and delivery points), and the Dial-A-Ride Problem (DARP -passenger transportation between paired pickup and delivery points and user inconvenience taken into consideration). Single as well as multi vehicle versions of the mathematical problem formulations are given for all four VRPB types, the corresponding exact, heuristic, and metaheuristic solution methods are discussed. Zusammenfassung Der vorliegende Artikel ist Teil I einer umfassendenÜberblicks-arbeit in zwei Teilenüber pickup and delivery Probleme. Grundsätzlich können zwei Problemklassen unterschieden werden. Die erste Problemklasse, mit der sich dieser Artikel befasst, beinhaltet all jene Probleme, die Auslieferungen von einem Depot zu Auslieferungs-Kunden (linehaul customers) und Abholungen von Rück-ladungs-Kunden (backhaul customers) zu einem Depot behandeln. Diese Problemklasse wird im Folgenden als Vehicle Routing Problems with Backhauls (VRPB) bezeichnet. Vier verschiedene Problemtypen können weiters unterschieden werden: das Vehicle Routing Problem with Clustered Backhauls (VRPCB), alle Auslieferungen müssen vor den Abholungen durchgeführt werden, das Vehicle Routing Problem with Mixed linehauls and Backhauls (VRPMB), gemischte Ausliefer-und Abholsequenzen sind gestattet, das Vehicle Routing Problem with Divisible Delivery and Pickup (VRPDDP), Kunden, die Ausliefer-und Abholservice verlangen, können zweimal besucht werden, und das Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP), Kunden, die beide Services verlangen, können nur genau einmal angefahren werden. Die zweite pickup and delivery Problemklasse wird in Teil II dieser Arbeit behandelt. Sie beinhaltet all jene Problemtypen, die sich mit Transporten zwischen Abhol-und Auslieferungsorten befassen: das Pickup and Delivery Vehicle Routing Problem (PD...

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Emergency service providers are facing the following problem: how and where to locate vehicles in order to cover potential future demand effectively. Ambulances are supposed to be located at designated locations such that in case of an emergency the patients can be reached in a time-efficient manner. A patient is said to be covered by a vehicle if (s)he can be reached by an ambulance within a predefined time limit. Due to variations in speed and the resulting travel times it is not sufficient to solve the static ambulance location problem once using fixed average travel times, as the coverage areas themselves change throughout the day. Hence we developed a multi-period version, taking into account time-varying coverage areas, where we allow vehicles to be repositioned in order to maintain a certain coverage standard throughout the planning horizon. We have formulated a mixed integer program for the problem at hand, which tries to optimize coverage at various points in time simultaneously. The problem is solved metaheuristically using variable neighborhood search. We show that it is essential to consider time-dependent variations in travel times and coverage respectively. When ignoring them the resulting objective will be overestimated by more than 24%. By taking into account these variations explicitly the solution on average can be improved by more than 10%.

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Kinesin-I is essential for the transport of membrane-bound organelles in neural and nonneural cells. However, the means by which kinesin interacts with its intracellular cargoes, and the means by which kinesin-cargo interactions are regulated in response to cellular transport requirements are not fully understood. The C terminus of the Drosophila kinesin heavy chain (KHC) was used in a two-hybrid screen of a Drosophila cDNA library to identify proteins that bind specifically to the kinesin tail domain. UNC-76 is an evolutionarily conserved cytosolic protein that binds to the tail domain of KHC in two-hybrid and copurification assays, indicating that kinesin and UNC-76 form a stable complex in vivo. Loss of Drosophila Unc-76 function results in locomotion and axonal transport defects reminiscent of the phenotypes observed in kinesin mutants, suggesting that UNC-76 is required for kinesin-dependent axonal transport. Unc-76 exhibits dosage-sensitive genetic relationships with Khc and Kinesin light chain mutations, further supporting the hypothesis that UNC-76 and kinesin-I work in a common transport pathway. Given the interaction of FEZ1, the mammalian homolog of UNC-76, with protein kinase Czeta, and the role of FEZ1 in axon outgrowth, we propose that UNC-76 helps integrate kinesin activity in response to transport requirements in axons.

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