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You’ll change more than I will:Adults’ predictions about their own and others’ future preferences

It has been argued that adults underestimate the extent to which their preferences will change over time. We sought to determine whether such mis-predictions are the result of a difficulty imagining that one’s own current and future preferences may differ or whether it also characterizes our predictions about the future preferences of others. We used a perspective- taking task in which we asked young people how much they liked stereotypically-young-person items (e.g., Top 40 music, adventure vacations) and stereotypically-old-person items (e.g., jazz, playing bridge) now, and how much they would like them in the distant future (i.e., when they are 70 years old). Participants also made these same predictions for a generic same-age, same-sex peer. In a third condition, participants predicted how much a generic older (i.e., age 70) same-sex adult would like items from both categories today. Participants predicted less change between their own current and future preferences than between the current and future preferences of a peer. However, participants estimated that, compared to a current older adult today, their peer would like stereotypically-young items more in the future and stereotypically-old items less. The fact that peers’ distant-future estimated preferences were different from the ones they made for “current” older adults suggests that even though underestimation of change of preferences over time is attenuated when thinking about others, a bias still exists

Artturi Assists Finnish Advisers and Farmers to Succeed in Grass-Based Dairy Production

Artturi is a collective name for a wide range of services. It is a common tool for different bodies who share an interest in strengthening grass-based dairy production in Finland: research, advisory service and industries. The Service is named after A. I. (Artturi Ilmari) Virtanen, the Finnish scientist who was awarded the Nobel prize in 1945, partly based on his work in developing the ensiling process of grass. The Artturi web site is available in Internet at: http://www.agronet.fi/artturi. Access to Artturi Services is free and no registration is required. The language used is Finnish. During summer 2003, 15,000 visits were recorded at the web site

Synthesis, in vitro evaluation, and radiolabeling of fluorinated puromycin analogues: potential candidates for PET imaging of protein synthesis

There is currently no ideal radiotracer for imaging protein synthesis rate (PSR) by positron emission tomography (PET). Existing fluorine-18 labelled amino acid-based radiotracers predominantly visualize amino acid transporter processes, and in many cases they are not incorporated into nascent proteins at all. Others are radiolabelled with the short half-life positron emitter carbon-11 which is rather impractical for many PET centers. Based on the puromycin (6) structural manifold, a series of 10 novel derivatives of 6 was prepared via Williamson ether synthesis from a common intermediate. A bioluminescence assay was employed to study their inhibitory action on protein synthesis which identified fluoroethyl analogue (7b) as a lead compound. The fluorine-18 analogue was prepared via nucleophilic substitution of the corresponding tosylate precursor in modest radiochemical yield 2±0.6% and excellent radiochemical purity (>99%) and showed complete stability over 3 h at ambient temperature

An overview of population-based algorithms for multi-objective optimisation

In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided

Pathways towards a sustainable future envisioned by early-career conservation researchers

Scientists have warned decision-makers about the severe consequences of the global environmental crisis since the 1970s. Yet ecological degradation continues and little has been done to address climate change. We investigated early-career conservation researchers' (ECR) perspectives on, and prioritization of, actions furthering sustainability. We conducted a survey (n = 67) and an interactive workshop (n = 35) for ECR attendees of the 5th European Congress of Conservation Biology (2018). Building on these data and discussions, we identified ongoing and forthcoming advances in conservation science. These include increased transdisciplinarity, science communication, advocacy in conservation, and adoption of a transformation-oriented social-ecological systems approach to research. The respondents and participants had diverse perspectives on how to achieve sustainability. Reformist actions were emphasized as paving the way for more radical changes in the economic system and societal values linked to the environment and inequality. Our findings suggest that achieving sustainability requires a strategy that (1) incorporates the multiplicity of people's views, (2) places a greater value on nature, and (3) encourages systemic transformation across political, social, educational, and economic realms on multiple levels. We introduce a framework for ECRs to inspire their research and practice within conservation science to achieve real change in protecting biological diversity

Optimal In Silico Target Gene Deletion through Nonlinear Programming for Genetic Engineering

Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized.Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy.Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial-and-error procedure