Bone-Like Mineral Nucleating Peptide Nanofibers Induce Differentiation of Human Mesenchymal Stem Cells into Mature Osteoblasts

Cataloged from PDF version of article.A bone implant should integrate to the tissue through a bone-like mineralized interface, which requires increased osteoblast activity at the implant-tissue boundary. Modification of the implant surface with synthetic bioinstructive cues facilitates on-site differentiation of progenitor stem cells to functional mature osteoblasts and results in subsequent mineralization. Inspired by the bioactive domains of the bone extracellular matrix proteins and the mussel adhesive proteins, we synthesized peptide nanofibers to promote bone-like mineralization on the implant surface. Nanofibers functionalized with osteoinductive collagen I derived Asp-Gly-Glu-Ala (DGEA) peptide sequence provide an advantage in initial adhesion, spreading, and early commitment to osteogenic differentiation for mesenchymal stem cells (hMSCs). In this study, we demonstrated that this early osteogenic commitment, however, does not necessarily guarantee a priority for maturation into functional osteoblasts. Similar to natural biological cascades, early commitment should be further supported with additional signals to provide a long-term effect on differentiation. Here, we showed that peptide nanofibers functionalized with Glu-Glu-Glu (EEE) sequence enhanced mineralization abilities due to osteoinductive properties for late-stage differentiation of hMSCs. Mussel-inspired functionalization not only enables robust immobilization on metal surfaces, but also improves bone-like mineralization under physiologically simulated conditions. The multifunctional osteoinductive peptide nanofiber biointerfaces presented here facilitate osseointegration for long-term clinical stability. © 2014 American Chemical Society

Logistics service provider selection for disaster preparation: a socio-technical systems perspective

Since 1990s, the world has seen a lot of advances in providing humanitarian aid through sophisticated logistics operations. The current consensus seems to be that humanitarian relief organizations (HROs) can improve their relief operations by collaborating with logistics service providers (CLSPs) in the commercial sector. The question remains: how can HROs select the most appropriate CLSP for disaster preparation? Despite its practical significance, no explicit effort has been done to identify the criteria/factors in prioritising and selecting a CLSP for disaster relief. The present study aims to address this gap by consolidating the list of criteria from a socio-technical systems (STS) perspective. Then, to handle the interdependence among the criteria derived from the STS, we develop a hybrid multi-criteria decision making model for CLSP selection in the disaster preparedness stage. The proposed model is then evaluated by a real-life case study, providing insights into the decision-makers in both HROs and CLSPs

Disaster preparedness using risk-assessment methods from earthquake engineering

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Analyzing the uncertainties associated with disaster occurrences is critical to make effective disaster preparedness plans. In this study, we focus on pre-positioning emergency supplies for earthquake preparedness. We present a new method to compute earthquake likelihood and the number of the affected people. Our approach utilizes forecasting methods from the earthquake engineering literature, and avoids using probabilistic scenarios to represent the uncertainties related to earthquake occurrences. We validate the proposed technique by using historical earthquake data from Turkey, a country under significant earthquake risk. We also present a case study that illustrates the implementation of our method to solve the inventory allocation problem of the Turkish Red Crescen

Karl Otmar Freiherr von Aretin - bedeutender Historiker des 20. Jahrhunderts

Vor hundert Jahren, am 2. Juli 1923, wurde Karl Otmar von Aretin in München geboren. Bekannt wurde er als Professor für Zeitgeschichte und aufgrund seiner prägenden Stellung an der TH Darmstadt

Collaborative Prepositioning Network Design for Regional Disaster Response

We present a collaborative prepositioning strategy to strengthen the disaster preparedness of the Caribbean countries, which are frequently hit by hurricanes. Since different subsets of countries are affected in each hurricane season, significant risk pooling benefits can be achieved through horizontal collaboration, which involves joint ownership of prepositioned stocks. We worked with the intergovernmental Caribbean Disaster and Emergency Management Agency to design a collaborative prepositioning network in order to improve regional response capacity. We propose a novel insurance-based method to allocate the costs incurred to establish and operate the proposed collaborative prepositioning network among the partner countries. We present a stochastic programming model, which determines the locations and amounts of relief supplies to store, as well as the investment to be made by each country such that their premium is related to the cost associated with the expected value and the standard deviation of their demand. We develop a realistic data set for the network by processing real-world data. We conduct extensive numerical analyses and present insights that support practical implementation. We show that a significant reduction in total inventory can be achieved by applying collaborative prepositioning as opposed to a decentralized policy. Our results also demonstrate that reducing the replenishment lead time during the hurricane season and improving sea connectivity are essential to increasing the benefits resulting from the network.TÜBİTAK ; Institute for Data Valorisation (IVADO) ; Natural Sciences and Engineering Research Council of Canad

Collaborative Prepositioning Network Design for Regional Disaster Response

We present a collaborative prepositioning strategy to strengthen the disaster preparedness of the Caribbean countries, which are frequently hit by hurricanes. Since different subsets of countries are affected in each hurricane season, significant risk pooling benefits can be achieved through horizontal collaboration, which involves joint ownership of prepositioned stocks. We worked with the intergovernmental Caribbean Disaster and Emergency Management Agency to design a collaborative prepositioning network in order to improve regional response capacity. We propose a novel insurance-based method to allocate the costs incurred to establish and operate the proposed collaborative prepositioning network among the partner countries. We present a stochastic programming model, which determines the locations and amounts of relief supplies to store, as well as the investment to be made by each country such that their premium is related to the cost associated with the expected value and the standard deviation of their demand. We develop a realistic data set for the network by processing real-world data. We conduct extensive numerical analyses and present insights that support practical implementation. We show that a significant reduction in total inventory can be achieved by applying collaborative prepositioning as opposed to a decentralized policy. Our results also demonstrate that reducing the replenishment lead time during the hurricane season and improving sea connectivity are essential to increasing the benefits resulting from the network.</p

A Mutual Catastrophe Insurance Framework for Horizontal Collaboration in Prepositioning Strategic Reserves

We develop a mutual catastrophe insurance framework for the prepositioning of strategic reserves to foster horizontal collaboration in preparedness against low-probability high-impact natural disasters. The framework consists of a risk-averse insurer pooling the risks of a portfolio of risk-averse policyholders. It encompasses the operational functions of planning the prepositioning network in preparedness for incoming insurance claims, in the form of units of strategic reserves, setting coverage deductibles and limits of policyholders, and providing insurance coverage to the claims in the emergency response phase. It also encompasses the financial functions of ensuring the insurer’s solvency by efficiently managing its capital and allocating yearly premiums among policyholders. We model the framework as a very large-scale nonlinear multistage stochastic program, and solve it through a Benders decomposition algorithm. We study the case of Caribbean countries establishing a horizontal collaboration for hurricane preparedness. Our results show that the collaboration is more effective when established over a longer planning horizon, and is more beneficial when outsourcing becomes expensive. Moreover, the correlation of policyholders affected simultaneously under the extreme realizations and the position of their claims in their global claims distribution directly affects which policyholders get deductibles and limits. This underlines the importance of prenegotiating policyholders’ indemnification policies at the onset of collaboration

CONTRA Project report #1: Requirements identification and system mapping

The research project “COVID-19 Network Technology-based Responsive Action” (CONTRA), funded by the Research Council of Norway, commenced in June 2020. The CONTRA project develops a decision support system (DSS) based on mathematical modeling and stochastic optimization, and machine learning tools for designing a robust COVID-19 vaccine distribution network. The project follows two main objectives within two phases. In response to the on-going COVID-19 outbreak, rapid analyses will provide actionable advice to public health authorities in Norway regarding vaccine distribution and delivery to responders. This phase involved a systematic study of vaccine distribution system actors in Norway and their decision-making needs. Based on such insights, the project will develop a DSS based on mathematical models to support designing the vaccine distribution network. The DSS should contribute to the effectiveness, efficiency, equity, and sustainability of the COVID-19 vaccine distribution. The proposed solution will also support vaccine distribution in future pandemics. The report describes the results of the first work package (WP) in the CONTRA project. The WP1 aims to identify the key actors in the vaccine distribution network in Norway, map their relation to each other, and distinguish critical decisions in the system. Moreover, the report presents an overview of related research on vaccine distribution networks, related decision support systems, and the progress in the literature about the COVID-19 pandemic. Through preliminary interviews, document review, and a workshop with multiple representatives from Norwegian public health authorities, the current vaccine distribution system is analyzed, and its actors have been mapped. This system map is the basis for further discussion both within the project team and with stakeholders. It should be noted that this map will change throughout the project due to the additional insights from other validation opportunities and the fact that the COVID-19 context is dynamic and is changing permanently. However, the system map has served as a basis for the problem definition in the CONTRA project. Based on our findings from the stakeholder workshop and system mapping, we have decided to focus on defining and studying the central vaccine allocation problem (CVAP), which is faced by Public Health Institute (FHI). As such, the CONTRA will investigate the problem of determining the amount of each vaccine to be shipped to every municipality. CVAP is challenged by the scarce amount of vaccines, the current immunization level, population, and priority groups in each municipality. In our project, CVAP will be formulated as a multi-objective resource allocation problem. Specifically, we will define and formulate objectives related to the following performance dimensions: efficacy (e.g., total coverage, coverage per priority group, etc.), efficiency and sustainability (e.g., logistics costs, waste), and fairness (e.g., distribution of efficacy among municipalities). The next step in the project will be to validate the problem definition and develop the mathematical model (second work package). Moreover, two individual reports for the actors map and system map will be published in the upcoming months by project partners.submittedVersio

Project report #2: Mathematical Modelling and Performance Measurement

The vaccine distribution network within a country starts from the main entry point of the country, in which COVID-19 vaccine supplies can arrive in multiple supply waves. In each supply wave, the vaccines, which arrive at a national storage point must be allocated among the smaller administrative units (municipalities or districts) of the country. This is called central vaccine allocation problem (CVAP). In CVAP, the decision makers must consider a variety of factors simultaneously in making allocation decisions such as the type of the vaccine, the size and distribution of the priority groups in the country, and the current status of infection. Moreover, multiple objectives must be considered such as total coverage achieved at national level as well as equity achieved across the municipalities. Cost of logistics can also be a concern, although at a lower criticality, due to implications of decisions on saving human lives. We propose an integer programming model to solve the CVAP. The mathematical model can incorporate different features depending on the decision making needs. The trade-offs among different objectives result in various allocation decisions. In CVAP, decision-makers can prefer whether to achieve a more efficient allocation or more equitable allocation at the beginning of the model. We elaborate on such issues through several illustrative examples.publishedVersio