No abstract

EXTENDED ABSTRACTWe present main characteristics of Electre family methods, designed for multiple criteria decision aiding. These methods use as a preference model an outranking relation in the set of actions -it is constructed in result of concordance and non-discordance tests involving a specific input preference information. After a brief description of the constructivist conception in which the Electre methods are inserted, we present the main features of these methods. We discuss such characteristic features as: the possibility of taking into account positive and negative reasons in the modeling of preferences, without any need for recoding the data; using of thresholds for taking into account the imperfect knowledge of data; the absence of systematic compensation between "gains" and "losses". The main weaknesses are also presented. Then, some aspects related to new developments are outlined. These are related to some new methodological developments, new procedures, axiomatic analysis, software tools, and several other aspects. The paper ends with conclusions.

Multicriteria Portfolio Analysis spans several methods which typically employ build on MCDA to guide the selection of a subset (i.e., portfolio) of available objects, with the aim of maximising the performance of the resulting portfolio with regard to multiple criteria, subject to the requirement that the selected portfolio does not consume of resources consumed by the portfolio does not exceed the availability of resources and, moreover, satis…es other relevant constraints as well. In this chapter, we present a formal model of this selection problem and describe how this model can present both challenges (e.g. portfolio value may, due to the interactions of elements, depend on project-level decisions in complex and non-additive ways) and opportunities (e.g. triage rules can be used to focus elicitation on projects which are critical) for value assessment. We also survey the application of Portfolio Decision Analysis in several domains, such as allocation of R&D expenditure, military procurement, prioritisation of healthcare projects, and environment and energy planning, and conclude by outlining possible future research directions.

Saliva is a biofluid that is sensitive to metabolic changes and is straightforward to collect in a non-invasive manner, but it is seldom used for metabolite analysis when studying neurodegenerative disorders. We present a procedure for both an untargeted and targeted analysis of the saliva metabolome in which nuclear magnetic resonance (NMR) spectroscopy is used in combination with multivariate data analysis. The applicability of this approach is demonstrated on saliva samples selected from the 25 year prospective Betula study, including samples from dementia subjects with either Alzheimer's disease (AD) or vascular dementia at the time of sampling or who developed it by the next sampling/assessment occasion five years later, and age-, gender-, and education-matched control individuals without dementia. Statistically significant multivariate models were obtained that separated patients with dementia from controls and revealed seven discriminatory metabolites. Dementia patients showed significantly increased concentrations of acetic acid (fold change (fc) = 1.25, p = 2 × 10(-5)), histamine (fc = 1.26, p = 0.019), and propionate (fc = 1.35, p = 0.002), while significantly decreased levels were observed for dimethyl sulfone (fc = 0.81, p = 0.005), glycerol (fc = 0.79, p = 0.04), taurine (fc = 0.70, p = 0.007), and succinate (fc = 0.62, p = 0.008). Histamine, succinate, and taurine are known to be important in AD, and acetic acid and glycerol are involved in related pathways. Dimethyl sulfone and propionate originate from the diet and bacterial flora and might reflect poorer periodontal status in the dementia patients. For these seven metabolites, a weak but statistically significant pre-diagnostic value was observed. Taken together, we present a robust and general NMR analysis approach for studying the saliva metabolome that has potential use for screening and early detection of dementia.

Regional climate change scenarios predict increased temperature and precipitation in the northern Baltic Sea, leading to a greater runoff of fresh water and terrestrial dissolved organic carbon (DOC) within the second part of the 21st century. As a result, the current north to south gradient in temperature and salinity is likely to be shifted further toward the south. To examine if such climate change effects would cause alterations in the environmental fate of organic pollutants, spatial variations of DOC quality and sorption behavior toward organic contaminants were examined using multiple analytical methods. The results showed declining contents of aromatic functional groups in DOC along a north to south gradient. Similarly, the sorption of a diverse set of organic contaminants to DOC also showed spatial differences. The sorption behavior of these contaminants was modeled using poly parameter linear energy relationships. The resulting molecular descriptors indicated clear differences in the sorption properties of DOC sampled in northern and southern parts of the Baltic Sea, which imply that more organic contaminants are sorbed to DOC in the northern part. The extent of this sorption process determines whether individual contaminants will partition to biota via direct uptake or through sorption to DOC, which serves as food source for bacteria-based food-webs.