Assessing the impact of forest management on aboriginal hunters : evidence from stated and revealed preference data; plenary paper

Meeting: EEPSEA Biannual Workshop, 19th, Nov. 2002, Singapore, S

Experimental Determination of Genetic and Environmental Influences on the Viscosity of Triticale

Version Number : 1.0Low viscosity in cereals is important for monogastric livestock feeding. With respect to triticale, knowledge on the variability of its viscosity and its environmental dependence is deplorably low. Six winter varieties with similar earliness at maturity were chosen that covered a large range of potential applied viscosity (PAV) (individual values ranging from 1.8 to 4.9 ml/g). These were cultivated in four locations in Switzerland, at altitudes ranging between 430 and 700 m a.s.l., in 2008 and 2009. The effect of genotype on the PAV was significant and clearly influenced by the location factor. Although variety x location and variety x year interactions were rather low, they were still very important for the PAV compared with other variables such as grain yield and specific grain weight. The PAV expression of one variety seemed not to be susceptible to environmental conditions. The varietal range in viscosity demonstrates a high potential for breeding to raise quality, especially as the viscosity and the grain yield were not correlated. The favourable relationship between the PAV and protein content found in the present study may provide a further incentive to improve this trait to yield high-quality triticale. Existing variability might be used to guide the choice of favourable varieties

Aggregation Bias in Recreation Site Choice Models: Resolving the Resolution Problem

This paper examines the effect of differing levels of spatial resolution on recreation site choice models and welfare resulting from changes in site attributes. These issues are important where the spatial scale at which recreationists make choices is unknown, but information exists on choice attributes at larger spatial scales. We estimate choice models at various scales of spatial resolution and incorporate the size of the aggregate sites and heterogeneity parameters in the model. Accounting for the size of the aggregates in estimation improved model fit and alleviated aggregate parameter bias. We provide advice for applied modeling based on these results.

Cheetah: A Computational Toolkit for Cybergenetic Control

Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah, a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterize, and control cells over time. We demonstrate Cheetah's core capabilities by analyzing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah's segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells