Explore the vast range of titles available.

Facial aging is the most visible manifestation of aging. People desire to look younger than others of the same chronological age. Hence, perceived age is often used as a visible marker of aging, while biological age, often estimated by methylation markers, is used as an objective measure of age. Multiple epigenetics-based clocks have been developed for accurate estimation of general biological age and the age of specific organs, including the skin. However, it is not clear whether the epigenetic biomarkers (CpGs) used in these clocks are drivers of aging processes or consequences of aging. In this proof-of-concept study, we integrate data from GWAS on perceived facial aging and EWAS on CpGs measured in blood. By running EW Mendelian randomization, we identify hundreds of putative CpGs that are potentially causal to perceived facial aging with similar numbers of damaging markers that causally drive or accelerate facial aging and protective methylation markers that causally slow down or protect from aging. We further demonstrate that while candidate causal CpGs have little overlap with known epigenetics-based clocks, they affect genes or proteins with known functions in skin aging, such as skin pigmentation, elastin, and collagen levels. Overall, our results suggest that blood methylation markers reflect facial aging processes, and thus can be used to quantify skin aging and develop anti-aging solutions that target the root causes of aging.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that adversely affects maternal and offspring health. A variety of risk factors, such as BMI and age, have been associated with increased risks of gestational diabetes. However, in many cases, gestational diabetes occurs in healthy nulliparous women with no obvious risk factors. Emerging data suggest that the tendency to develop gestational diabetes has genetic and environmental components. Here we develop a polygenic risk score for GDM and investigate relationships between its genetic architecture and genetically constructed risk factors and biomarkers. Our results demonstrate that the polygenic risk score can be used as an early screening tool that identifies women at higher risk of GDM before its onset allowing comprehensive monitoring and preventative programs to mitigate the risks.

Preeclampsia and gestational hypertensive disorders (GHD) are common complications of pregnancy that adversely affect maternal and offspring health, often with long-term consequences. High BMI, advanced age, and pre-existing conditions are known risk factors for GHD. Yet, assessing a woman’s risk of GHD based on only these characteristics needs to be reevaluated in order to identify at-risk women, facilitate early diagnosis, and implement lifestyle recommendations. This study demonstrates that a risk score developed with machine learning from the case-control genetics dataset can be used as an early screening test for GHD. We further confirm BMI as a risk factor for GHD and investigate a relationship between GHD and genetically constructed anthropometric measures and biomarkers. Our results show that polygenic risk score can be used as an early screening tool that, together with other known risk factors and medical history, would assist in identifying women at higher risk of GHD before its onset to enable stratification of patients into low-risk and high-risk groups for monitoring and preventative programs to mitigate the risks.

Purpose – The paper aims to provide a methodology by which organisational knowledge can be extracted and visualised dynamically over time, providing a glimpse into the knowledge evolution processes that occur within organisations. Design/methodology/approach – Recursive analysis of email interactions is offered as a case to account for the knowledge structure evolution related to the different programs of international non-governmental organization (INGO). Several methods are used: analysis of the network expansion to see whether the process is random or uniform is performed, visualisation of the network configuration changes throughout studied time period; and the statistical examination of network formation. Findings – The results of the presented study indicate that content structure of electronic knowledge networks exhibits hierarchical and centralised tendencies. The social network analysis results suggest that INGO exhibits non-hierarchical and decentralized structure of the individuals contributing to the discussion lists. Research limitations/implications – By providing the means to carry out network evolution analysis of content structure dynamics and social interactions, the presented work provides a means for probabilistically modelling patterns of organisational knowledge evolution. Practical implications – The approach allows the exploration of the dynamics of tacit to explicit knowledge, from individual to the group and from informal groups to the whole organisation. Originality/value – By displaying the large collection of the key phrases that reflected the evolution of the organisational knowledge structure over the time, organisational emails are placed in meaningful context explaining the language of the organisation and context of knowledge structure evolution.

Knowledge Management (KM) is critical in ensuring process efficiency, outcome effectiveness and improved organizational memory for the modern day business enterprises. Knowledge Sharing (KS) is fast becoming a rapidly growing area of interest in the domain of knowledge management. The purpose of this paper is to enlist a set of generalized metrics that can be used to evaluate the efficiency and the effectiveness of knowledge sharing in an enterprise network. The metrics proposed in this research are those that can be readily measured by various types of enterprise knowledge sharing systems, and link usage information to organizational outputs. The paper uses an illustrative case example of how an enterprise might make use of the metrics in measuring the efficiency and effectiveness of its knowledge sharing system.

There's long been a need for a book that addresses concrete socio-technical network (STN) design issues from algorithmic and engineering perspectives. Filling this need, this book provides a complete introduction to the fundamentals of STN-including its definition, historical background, and models. Covering basic STN architecture, it considers the system design process in a typical STN and includes three in-depth case studies that consider applications in healthcare, virtual communities, and power plant management.

Standard systems engineering processes, such as the ANSI/EIA 632 or the ISO/IEC 15288 process standards, are primarily geared towards systems with homogenous stakeholders and few tightly coupled non-linear interactions within and between physical subsystems. When dealing with Complex, Large-scale, Interconnected, Open, Sociotechnological (CLIOS) systems such as the national power grid, regional transportation systems, the world wide web or the air traffic control system or other systems with wide-ranging social and environmental impact, there is a need for a new framework that takes into consideration the physical and institutional system complexities and their respective interactions in an iterative and adaptive manner. This paper presents a 12-step framework for concurrent analysis, design and management of coupled complex technological and institutional systems in the face of uncertainty. The framework can be used to analyze a CLIOS system’s underlying structure and behavior, to explore different design options in the face of uncertainty, and to identify and deploy strategic alternatives for improving the system’s performance.

Environmental planning and policy analysis, design and implementation are complicated by the fact that environmental problems are almost always part of a complex sociotechnical system, the behaviour of which is not intuitive. The complexity of the problem necessitates a technical and scientific analysis process, which by its nature excludes the majority of the stakeholders in the given problem. To overcome these challenges, this research proposes the engagement of stakeholders from very early on in the process using computer-assisted visualisation and representation of complex sociotechnical systems. Specifically it proposed the use of system dynamics simulation in illustrating the complex interactions of the different sociotechnical system elements. Using computer simulation of the system, stakeholders can then decide on the best strategies to address the issue on a more objective basis. Ubiquitous computing enables the assessment of a vast number of strategies in a relatively short time.

There is a growing realization that stakeholder involvement in decision-making for large-scale engineering systems is necessary and crucial, both from an ethical perspective, as well as for improving the chances of success for an engineering systems project. Traditionally however, stakeholders have only been involved after decision-makers and experts have completed the initial decision-making process with little or no input from stakeholders. This has resulted in conflict and delays for engineering systems with brilliant technical designs that do not address the larger context of the broader social goals. One of the fears of experts is that the involvement of stakeholders will result in technical solutions that are of poor quality.The hypothesis of this research is that an effective involvement of stakeholders in the decision-making process for engineering systems from the problem definition stage through the system representation can produce a system representation that is superior to representations produced in an expert-centered process. This dissertation proposes a Stakeholder-Assisted Modeling and Policy Design (SAM-PD) process for effectively involving stakeholders in engineering systems with wide-ranging social and environmental impact. The SAM-PD process is designed based on insights from existing engineering systems methodologies and alternative dispute resolution literature.Starting with a comprehensive analysis of engineering systems methodologies, the role of experts in engineering systems decision-making and existing stakeholder involvement mechanisms, this research explores the role of cognitive biases of engineering systems representation through actual experiments, and concludes that the process of defining a system through its boundaries, components and linkages is quite subjective, and prone to implicit value judgments of those participating in the system representation process. Therefore to account for stakeholder interests, concerns and knowledge in engineering systems decision-making, it is important to have a collaborative process that enables stakeholders to jointly shape the problem definition and model outputs necessary for decision-making.Based on insights from the literature, this research developed a collaborative process for engineering systems decision-making, and explored its merits and drawbacks in applying it to the Cape Wind offshore wind energy project involving actual stakeholders in the system representation process. It further explored the potential application of such a process to the Mexico City transportation/air pollution system and the Cape and Islands Renewable Energy Planning project.The Cape Wind case study showed that a stakeholder-assisted system representation was superior to the equivalent expert-centered system representation used by the permitting agency as a basis for decision-making, in that it served as a thought expander for stakeholders, captured some effects that the expert-centered representation could not capture, better took into account social, economic and political feasibility and was more useful in suggesting better alternative strategies for the system.The case studies also highlighted the importance of the convening organization, institutional readiness for collaborative processes, the importance of stakeholder selection and process facilitation, the potentials of system representation as a basis for stakeholder dialogue and the importance of quantification versus evaluation of system representations.The basic implication of this research is that it would be myopic of engineering systems professionals to shift the burden of stakeholder involvement to decision-makers, and keep the analysis a merely expert-centered process. Due to the many subjective choices that have to be made with regards to system boundaries, choice of components, inclusion of linkages, nature of outputs and performance metrics and assumptions about data and relationships, system analysts are in fact not producing the analysis that will help the decision-making process. The best airport designs done with multi-tradeoff analysis and intricate options analysis may lead to nowhere if stakeholders affected by the project do not see their interests reflected in the analysis. The notion is that a good systems analysis is not one that impresses other engineering systems professionals with its complexity, but one that can actually address the problems at hand.

Facial aging is the most visible manifestation of aging. People desire to look younger than others of the same chronological age. Hence, perceived age is often used as a visible marker of aging, while biological age, often estimated by methylation markers, is used as an objective measure of age. Multiple epigenetics-based clocks have been developed for accurate estimation of general biological age and the age of specific organs, including the skin. However, it is not clear whether the epigenetic biomarkers (CpGs) used in these clocks are drivers of aging processes or consequences of aging. In this proof-of-concept study, we integrate data from GWAS on perceived facial aging, and EWAS on CpGs measured in blood. By running EW Mendelian randomization, we identify hundreds of putative CpGs that are potentially causal to perceived facial aging with similar numbers of damaging markers that causally drive or accelerate facial aging and protective methylation markers that causally slow down or protect from aging. We further demonstrate that while candidate causal CpGs have little overlap with known epigenetics-based clocks, they affect genes or proteins with known functions in skin aging such as skin pigmentation, elastin, and collagen levels. Overall, our results suggest that blood methylation markers reflect facial aging processes, and thus can be used to quantify skin aging and develop anti-aging solutions that target the root causes of aging.