In Vitro and In Silico Screening and Characterization of Antimicrobial Napin Bioactive Protein in Brassica juncea and Moringa oleifera

The study aimed to investigate the antibacterial activity of Mustard (Brassica juncea) and Moringa (Moringa oleifera) leaf extracts and coagulant protein for their potential application in water treatment. Bacterial cell aggregation and growth kinetics studies were employed for thirteen bacterial strains with different concentrations of leaf extracts and coagulant protein. Moringa oleifera leaf extract (MOS) and coagulant protein showed cell aggregation against ten bacterial strains, whereas leaf extract alone showed growth inhibition of five bacterial strains for up to 6 h and five bacterial strains for up to 3 h. Brassica juncea leaf extract (BJS) showed growth inhibition for up to 6 h, and three bacterial strains showed inhibition for up to 3 h. The highest inhibition concentration with 2.5 mg/mL was 19 mm, and furthermore, the minimum inhibitory concentration (MIC) (0.5 mg/mL) and MBC (1.5 mg/mL) were determined to have a higher antibacterial effect for <3 KDa peptides. Based on LCMS analysis, napin was identified in both MOS and BJS; furthermore, the mode of action of napin peptide was determined on lipoprotein X complex (LpxC) and four-chained structured binding protein of bacterial type II topoisomerase (4PLB). The docking analysis has exhibited moderate to potent inhibition with a range of dock score −912.9 Kcal/mol. Thus, it possesses antibacterial-coagulant potential bioactive peptides present in the Moringa oleifera purified protein (MOP) and Brassica juncea purified protein (BJP) that could act as an effective antimicrobial agent to replace currently available antibiotics. The result implies that MOP and Brassica juncea purified coagulant (BJP) proteins may perform a wide degree of antibacterial functions against different pathogens.     

An efficient algorithm for a job shop problem

We study the problem of minimizing makespan in a two-machine job shop with unit processing time operations. An efficient algorithm with respect to a succinct encoding of the problem instances is proposed. The algorithm is an improvement of earlier algorithms proposed for the problem by Brucker [1,2], Hefetz and Adiri [7], and Timkovskiy [15]. The idea behind the algorithm has the potential of extension to job shops with parallel machines.This research is supported in part by NSERC Grants A4619, OGP0105675, OGP0104900, General Motors of Canada, and the Manufacturing Research Corporation of Ontario.     

Supply chain scheduling: Just-in-time environment

We study the benefits of coordinated decision making in a supply chain consisting of a manufacturer, a distributor, and several retailers. The distributor bundles finished goods produced by the manufacturer and delivers them to the retailers to meet their demands. The distributor is responsible for managing finished goods inventory. An optimal production schedule of the manufacturer, if imposed on the distributor, may result in an increased inventory holding cost for the distributor. On the other hand, an optimal distribution schedule of the distributor, if imposed on the manufacturer, may result in an increased production cost for the manufacturer. In this paper we develop mathematical models for individual optimization goals of the two partners and compare the results of these models with the results obtained for a joint optimization model at the system level. We investigate the computational complexities of these scheduling problems. The experimental results indicate that substantial cost savings can be achieved at the system level by joint optimization. We also study conflict and cooperation issues in the supply chain. The cost of conflict of a supply chain partner is a measure of the amount by which the unconstrained optimal cost increases when a decision is to be made under the scheduling constraint imposed by the other partner. We quantify these conflicts and show that the cost of conflicts are significant. We also show that a cooperative decision will generate a positive surplus in the system which can be shared by the two partners to make cooperation and coordination strategy more attractive.     

A theorem on the stability of elastic systems

Summary Similar to the Ziegler's theorem [1] on the influence of nonworking constraints on the stability of elastic systems, a theorem on the influence of elastic supports (constraints), which do perform work during the motion of the system, is derived for conservative elastic systems. The theorem is applicable to both discrete and continuous systems.

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Zusammenfassung In Analogie zu einem Satz von Ziegler über den Einfluss arbeitsloser Bindungen auf die Stabilität elastischer Systeme wird ein Theorem über den Einfluss elastischer Einspannungen, die während der Bewegung Arbeit leisten, für konservative Systeme hergeleitet. Es ist sowohl für diskrete als auch für kontinuierliche Systeme anwendbar.

     

Total synthesis of (-)-himgaline

The first total synthesis of (-)-himgaline and a highly enantioselective synthesis of its congener (-)-GB 13 are described. Decarboxylative aza-Michael reaction of the hexacyclic lactone precursor under acidic conditions, followed by basic workup, yielded (-)-GB 13 in 80% yield. Cyclization of (-)-GB 13 to oxohimgaline under acidic conditions, followed by internally coordinated sodium triacetoxyborohydride reduction, gave (-)-himgaline as the exclusive product.     

Some Applications of the Generalized Travelling Salesman Problem

In the Generalized Travelling Salesman Problem (GTSP), the aim is to determine a least cost Hamiltonian circuit or cycle through several clusters of vertices. It is shown that a wide variety of combinatorial optimization problems can be modelled as GTSPs. These problems include location-routeing problems, material flow system design, post-box collection, stochastic vehicle routeing and arc routeing.     

Maintenance scheduling of rolling stock using a genetic algorithm

We have developed a Genetic algorithm (GA) for the optimisation of maintenance overhaul scheduling of rolling stock (trains) at the Hong Kong Mass Transit Railway Corporation (MTRC). The problem is one of combinatorial optimisation. Genetic algorithms (GAs) belong to the class of heuristic optimisation techniques that utilise randomisation as well as directed smart search to seek the global optima. The workshop at MTRC does have difficulties in establishing good schedules for the overhaul maintenance of the rolling stock. Currently, an experienced scheduler at MTRC performs this task manually. In this paper, we study the problem in a scientific manner and propose ways in which the task can be automated with the help of an algorithm embedded in a computer program. The algorithm enables the scheduler to establish the annual maintenance schedule of the trains in an efficient manner; the objective being to satisfy the maintenance requirements of various units of the trains as closely as possible to their due dates since there is a cost associated with undertaking the maintenance tasks either `too early’ or ‘too late’. The genetic algorithm developed is found to be very effective for solving this intractable problem. Computational results indicate that the genetic algorithm consistently provides significantly better schedules than those established manually at MTRC. More over, we provide evidence that the algorithm delivers close to optimal solutions for randomly generated problems with known optimal solutions. We also propose a local search method to reconfigure the trains in order to improve the schedule and to balance the work load of the overhaul maintenance section of the workshop throughout the planning horizon. We demonstrate that the reconfiguration of trains improves the schedule and reduces cost significantly.     

Operational Decisions in AGV-Served Flowshop Loops: Scheduling

This paper considers operational issues that arise in repetitive manufacturing systems served by automated guided vehicles (AGVs) in loops with unidirectional material flow. The objective considered is the minimization of the steady state cycle time required to produce a minimal job set (or equivalently, throughput rate maximization). Our models allow for delays caused by AGV conflicts. We define and analyze three nondominated and widely used AGV dispatching policies. For each policy, we describe algorithms and intractability results for combined job scheduling and material handling problems. We describe a genetic algorithm that estimates the cycle time within 5% on average for instances with up to 10 machines and four AGVs. Some related fleet sizing and loop decomposition issues are discussed in the companion paper [19].