The complete genome sequence of the phytopathogenic fungus Sclerotinia sclerotiorum reveals insights into the genome architecture of broad host range pathogens

Sclerotinia sclerotiorum is a phytopathogenic fungus with over 400 hosts including numerous economically important cultivated species. This contrasts many economically destructive pathogens that only exhibit a single or very few hosts. Many plant pathogens exhibit a “two-speed” genome. So described because their genomes contain alternating gene rich, repeat sparse and gene poor, repeat-rich regions. In fungi, the repeat-rich regions may be subjected to a process termed repeat-induced point mutation (RIP). Both repeat activity and RIP are thought to play a significant role in evolution of secreted virulence proteins, termed effectors. We present a complete genome sequence of S. sclerotiorum generated using Single Molecule Real-Time Sequencing technology with highly accurate annotations produced using an extensive RNA sequencing data set. We identified 70 effector candidates and have highlighted their in planta expression profiles. Furthermore, we characterized the genome architecture of S. sclerotiorum in comparison to plant pathogens that exhibit “two-speed” genomes. We show that there is a significant association between positions of secreted proteins and regions with a high RIP index in S. sclerotiorum but we did not detect a correlation between secreted protein proportion and GC content. Neither did we detect a negative correlation between CDS content and secreted protein proportion across the S. sclerotiorum genome. We conclude that S. sclerotiorum exhibits subtle signatures of enhanced mutation of secreted proteins in specific genomic compartments as a result of transposition and RIP activity. However, these signatures are not observable at the whole-genome scale

A two-echelon inventory model with lost sales

This paper considers a single-item, two-echelon, continuous-review inventory model. A number of retailers have their stock replenished from a central warehouse. The warehouse in turn replenishes stock from an external supplier. The demand processes on the retailers are independent Poisson. Demand not met at a retailer is lost. The order quantity from each retailer on the warehouse and from the warehouse on the supplier takes the same fixed value Q, an exogenous variable determined by packaging and handling constraints. Retailer i follows a (Q, Ri) control policy. The warehouse operates an (SQ, (S - 1)Q) policy, with non-negative integer S. If the warehouse is in stock then the lead time for retailer i is the fixed transportation time Li from the warehouse to that retailer. Otherwise retailer orders are met, after a delay, on a first-come first-served basis. The lead time on a warehouse order is fixed. Two further assumptions are made: that each retailer may only have one order outstanding at any time and that the transportation time from the warehouse to a retailer is not less than the warehouse lead time. The performance measures of interest are the average total stock in the system and the fraction of demand met in the retailers. Procedures for determining these performance measures and optimising the behaviour of the system are develope

Optimizing a Bi-objective Reliable Facility Location Problem with Adapted Stochastic Measures Using Tuned Parameter Multi-Objective Algorithms

The stochastic process is one the most important tools to overcome uncertainties of supply chain problems. Being a lack of studies on constrained reliable facility location problems (RFLP) with multiple capacity levels, this paper develops a bi-objective RFLP with multiple capacity levels in a three echelon supply chain management while there is a constraint on the coverage levels. Moreover, there is a provider-side uncertainty for distribution-centers (DCs). The aim of this paper is to find a near-optimal solution including suitable locations of DCs and plants, the fraction of satisfied customer demands, and the fraction of items sent to DCs to minimize the total cost and to maximize fill rate, simultaneously. As the proposed model belongs to NP-Hard problems, a meta-heuristic algorithm called multi-objective biogeography-based optimization (MOBBO) is employed to find a near-optimal Pareto solution. Since there is no benchmark in the literature to compare provided solutions, a non-dominated ranking genetic algorithm (NRGA) and a multi objective simulated annealing (MOSA) are used to verify the solution obtained by MOBBO while a two-stage stochastic programming (2-SSP) is employed to capture randomness of DCs. This paper uses the adapted concepts of expected value of perfect information (EVPI) and the value of stochastic solution (VSS) in order to validate 2-SSP. Moreover, the parameters of algorithms are tuned by the response surface methodology (RSM) in the design of experiments. Besides, an exact method, called branch-and-bound method via GAMS optimization software, is used to compare lower and upper bounds of Pareto fronts to optimize two single-objective problems separately