Comparison of loop-mediated isothermal amplification and conventional PCR tests for diagnosis of common Brucella species

Objective: Rapid, reliable, and affordable detection of Brucella species via the molecular methods remains a challenge. In recent years, loop-mediated isothermal amplification (LAMP) is a functional nucleic acid amplification technique offering a substitute to polymerase chain reaction (PCR). So, we compared the LAMP assay with the conventional PCR for the identification of common Brucella species in Iran. In this study, LAMP assay was comprehensively evaluated against the common PCR method. A group of specific LAMP primers were used to amplify a highly specific fragment from the sequence of the Brucella abortus, bcsp31 gene. Sensitivity and specificity values of tests were done with a set of 78 (50 Brucella and 28 non-Brucella) strains. Results: A dilution series of B. abortus DNA indicated that the LAMP reaction could reliably detect 10 (fg/µl) DNA target copies per reaction within 36 min, which is 10 times greater than the PCR assay. In summary, we conclude that LAMP assay provide accurate and fast test results to identify of common Brucella species in low-complexity labs, mainly in low and lower middle income countries. © 2020, The Author(s)

Dynamic vehicle routing problem with cooperative strategy in disaster relief: .

International audienceMany studies have focused on static vehicle routing problems (VRP) in which all information is known in advance. However, in recent years, the growth of technology has brought about a new range of problems, referred to as dynamic vehicle routing problems. In these problems, a part of the orders are received in advance before departure of vehicles from depots, but some new orders will come in after the vehicle’s departure. In both situations utilising multiple vehicles and cooperative strategy can decrease costs. Although cooperative strategy has not received considerable attention in the literature, it could be a possibility in practice which can help reduce costs. Multiple vehicles in this strategy are allowed to travel and they can transfer goods between one another (from the main vehicle) when they meet in demand points so as to better satisfy the late demands. The benefits of such optimisations, which consider dynamic orders, are evaluated in case of emergencies far from the expected zones and the distribution centres. A mixed integer nonlinear mathematical model is proposed for multi-vehicle routing problem considering product transshipment between vehicles in dynamic situations. The objective function is to minimise the total cost of transportation as well as the number of lost sales. A robust genetic algorithm is then developed to deal with the complexity of the problem. The experimental results show that cooperative strategy is an attractive possibility to reduce unsatisfied late received demands and costs

Dynamic vehicle routing problem with cooperative strategy in disaster relief: .

International audienceMany studies have focused on static vehicle routing problems (VRP) in which all information is known in advance. However, in recent years, the growth of technology has brought about a new range of problems, referred to as dynamic vehicle routing problems. In these problems, a part of the orders are received in advance before departure of vehicles from depots, but some new orders will come in after the vehicle’s departure. In both situations utilising multiple vehicles and cooperative strategy can decrease costs. Although cooperative strategy has not received considerable attention in the literature, it could be a possibility in practice which can help reduce costs. Multiple vehicles in this strategy are allowed to travel and they can transfer goods between one another (from the main vehicle) when they meet in demand points so as to better satisfy the late demands. The benefits of such optimisations, which consider dynamic orders, are evaluated in case of emergencies far from the expected zones and the distribution centres. A mixed integer nonlinear mathematical model is proposed for multi-vehicle routing problem considering product transshipment between vehicles in dynamic situations. The objective function is to minimise the total cost of transportation as well as the number of lost sales. A robust genetic algorithm is then developed to deal with the complexity of the problem. The experimental results show that cooperative strategy is an attractive possibility to reduce unsatisfied late received demands and costs

A robust location-inventory model for food supply chains operating under disruptions with ripple effects: .

International audienceGiven the inevitable globalisation in the food sector and the specific security challenges this industry faces, designing food supply chains has become a substantial topic for academics and practitioners. The integration of food product-specific characteristics and potential disruptions has continuously gained importance because it better reflects real-world problems and responds to a crucial need for resilience, robustness, and competitiveness. In this article, a generic two-stage mixed-integer mathematical model is developed to integrate key features of location-allocation and inventory-replenishment decisions. Then, food-specific disruptions with ripple effects are incorporated through plausible scenarios. For such a setting, three resiliency strategies – namely, readiness, flexibility, and responsiveness – are used to deal with uncertainties. Based on extensive numerical experiments, the solutions obtained highlight behaviour of different design models to hedge against ripple effects as well as the importance of incorporating food-specific assumptions and risk aversion attitudes

The design of resilient food supply chain networks prone to epidemic disruptions: .

International audienceFood supply chains are nowadays perturbed by an increased supply and demand uncertainty, and more and more suffering from unexpected disruptions. In the specific context of food supply chains (FSC) for perishable products, these could be linked to natural hazards, industrial accidents or epidemics and their impact could lead to huge economic losses. The case of epidemic events has been little studied in the existing literature, although there are numerous cases reported in practice. At the strategic level, this requires a novel risk modeling approach to tackle the correlation and propagation features and advanced stochastic multi-period models to design the FSC network. Our interest in this research is to propose a comprehensive two-stage scenario-based mathematical model to design a resilient food supply chain under demand uncertainty and epidemic disruptions. In order to adequately characterize epidemic disruptions, they are modeled as a compound stochastic process and a Monte Carlo procedure is developed to generate plausible scenarios. The modeling approach covers the special characteristics of FSC, such as products perishability in time and discount prices based on product's age. In addition, a number of resiliency strategies are incorporated into the core model to enhance the resilience level of the FSC network design. The developed models are solved through an efficient solution approach relying on scenario reduction technique and Benders decomposition. Numerous problem instances are used to validate the modeling approach and to derive managerial insights

The design of resilient food supply chain networks prone to epidemic disruptions: .

International audienceFood supply chains are nowadays perturbed by an increased supply and demand uncertainty, and more and more suffering from unexpected disruptions. In the specific context of food supply chains (FSC) for perishable products, these could be linked to natural hazards, industrial accidents or epidemics and their impact could lead to huge economic losses. The case of epidemic events has been little studied in the existing literature, although there are numerous cases reported in practice. At the strategic level, this requires a novel risk modeling approach to tackle the correlation and propagation features and advanced stochastic multi-period models to design the FSC network. Our interest in this research is to propose a comprehensive two-stage scenario-based mathematical model to design a resilient food supply chain under demand uncertainty and epidemic disruptions. In order to adequately characterize epidemic disruptions, they are modeled as a compound stochastic process and a Monte Carlo procedure is developed to generate plausible scenarios. The modeling approach covers the special characteristics of FSC, such as products perishability in time and discount prices based on product's age. In addition, a number of resiliency strategies are incorporated into the core model to enhance the resilience level of the FSC network design. The developed models are solved through an efficient solution approach relying on scenario reduction technique and Benders decomposition. Numerous problem instances are used to validate the modeling approach and to derive managerial insights