Many ways to make darker flies: Intra-and interspecific variation in Drosophila body pigmentation components

Body pigmentation is an evolutionarily diversified and ecologically relevant trait with substantial variation within and between species, and important roles in animal survival and reproduction. Insect pigmentation, in particular, provides some of the most compelling examples of adaptive evolution, including its ecological significance and genetic bases. Pigmentation includes multiple aspects of color and color pattern that may vary more or less independently, and can be under different selective pressures. We decompose Drosophila thorax and abdominal pigmentation, a valuable eco-evo- devo model, into distinct measurable traits related to color and color pattern. We investigate intra-and interspecific variation for those traits and assess its different sources. For each body part, we measured overall darkness, as well as four other pigmentation properties distinguishing between background color and color of the darker pattern elements that decorate each body part. By focusing on two standard D. melanogaster laboratory populations, we show that pigmentation components vary and covary in distinct manners depending on sex, genetic background, and temperature during development. Studying three natural populations of D. melanogaster along a latitudinal cline and five other Drosophila species, we then show t hat evolution of lighter or darker bodies can be achieved by changing distinct component traits. Our results paint a much more complex picture of body pigmentation variation than previous studies could uncover, including patterns of sexual dimorphism, thermal plasticity, and interspecific diversity. These findings underscore the value of detailed quantitative phenotyping and analysis of different sources of variation for a better understanding of phenotypic variation and diversification, and the ecological pressures and genetic mechanisms underlying them.info:eu-repo/semantics/publishedVersio

INTERLAYER COUPLING AND THE METAL-INSULATOR TRANSITION IN Pr-SUBSTITUTED Bi(2)Sr(2)CaCu(2)O(8+y)

Substitution of rare-earth ions for Ca in Bi2Sr2CaCu2O8+y is known to cause a metal-insulator transition. Using resonant photoemission we study how this chemical substitution affects the electronic structure of the material. For the partial Cu-density of states at E_F and in the region of the valence band we observe no significant difference between a pure superconducting sample and an insulating sample with 60% Pr for Ca. This suggests that the states responsible for superconductivity are predomi- nately O-states. The partial Pr-4f density of states was extracted utilizing the Super- Koster-Kronig Pr 4d-4f resonance. It consists of a single peak at 1.36eV binding energy. The peak shows a strongly assymetric Doniach-Sunjic line- shape indicating the presence of a continuum of electronic states with sharp cut off at E_F even in this insulating sample. This finding excludes a bandgap in the insulating sample and supports the existance of a mobility gap caused by spatial localization of the carriers. The presence of such carriers at the Pr-site, between the CuO_2 planes shows that the electronic structure is not purely 2-dimensional but that there is a finite interlayer coupling. The resonance enhancement of the photoemission cross section, at the Pr-4d threshold, was studied for the Pr-4f and for Cu-states. Both the Pr-4f and the Cu-states show a Fano-like resonance. This resonance of Cu-states with Pr-states is another indication of coupling between the the Pr-states and those in the CuO_2 plane. Because of the statistical distribution of the Pr-ions this coupling leads to a non-periodic potential for the states in the CuO_2 plane which can lead to localization and thus to the observed metal-insulator transition.Comment: Gziped uuencoded postscript file including 7 figures Scheduled for publication in Physical Review B, May 1, 1995

Argonne National Laboratory Reports

This report is part of a series of studies designed to analyze the commercialization potential of various concepts of community-scale energy systems that have been termed Integrated Community Energy Systems (ICES). The study reported here concerns ways that affected individuals and organizations will respond to proposed ICES development projects. The intent is an initial examination of several institutional sectors that will: (1) anticipate responses that could impede ICES proposals and (2) provide an information base from which strategies to address adverse responses can be formulated

Compared to conventional, ecological intensive management promotes beneficial proteolytic soil microbial communities for agro-ecosystem functioning under climate change-induced rain regimes

Projected climate change and rainfall variability will affect soil microbial communities, biogeochemical cycling and agriculture. Nitrogen (N) is the most limiting nutrient in agroecosystems and its cycling and availability is highly dependent on microbial driven processes. In agroecosystems, hydrolysis of organic nitrogen (N) is an important step in controlling soil N availability. We analyzed the effect of management (ecological intensive vs. conventional intensive) on N-cycling processes and involved microbial communities under climate change-induced rain regimes. Terrestrial model ecosystems originating from agroecosystems across Europe were subjected to four different rain regimes for 263 days. Using structural equation modelling we identified direct impacts of rain regimes on N-cycling processes, whereas N-related microbial communities were more resistant. In addition to rain regimes, management indirectly affected N-cycling processes via modifications of N-related microbial community composition. Ecological intensive management promoted a beneficial N-related microbial community composition involved in N-cycling processes under climate change-induced rain regimes. Exploratory analyses identified phosphorus-associated litter properties as possible drivers for the observed management effects on N-related microbial community composition. This work provides novel insights into mechanisms controlling agro-ecosystem functioning under climate change

Tuning of Adaptive Weight Depth Map Generation Algorithms Exploratory Data Analysis and Design of Computer Experiments (DOCE)

In depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on un planned experiments -- Algorithms' performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury's standards -- This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative inf uence of the parameters and to fine-tune them based on the number of bad pixels -- The implemented methodology improves the performance of adaptive weight based dense depth map algorithms -- As a result, the algorithm improves from 16.78% to 14.48% bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78% to 13

Disentangling the respective contribution of task selection and task execution in self-directed cognitive control development

peer reviewedTask selection and task execution are key constructs in cognitive control development. Yet, little is known about how separable they are and how each contributes to task switching performance. Here, 60 4- to 5-year olds, 60 7- to 8-year olds, and 60 10- to 11-year olds children completed the double registration procedure, which dissociates these two processes. Task selection yielded both mixing and switch costs, especially in younger children, and task execution mostly yielded switch costs at all ages, suggesting that task selection is costlier than task execution. Moreover, both task selection and execution varied with task self-directedness (i.e., to what extent the task is driven by external aids) demands. Whereas task selection and task execution are dissociated regarding performance costs, they nevertheless both contribute to self-directed control

Methods for specifying the target difference in a randomised controlled trial : the Difference ELicitation in TriAls (DELTA) systematic review

Peer reviewedPublisher PD

Design of Experiments for Screening

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared

Longitudinal analysis of a long-Term conservation agriculture experiment in Malawi and lessons for future experimental design

Resilient cropping systems are required to achieve food security in the presence of climate change, and so several long-Term conservation agriculture (CA) trials have been established in southern Africa-one of them at the Chitedze Agriculture Research Station in Malawi in 2007. The present study focused on a longitudinal analysis of 10 years of data from the trial to better understand the joint effects of variations between the seasons and particular contrasts among treatments on yield of maize. Of further interest was the variability of treatment responses in time and space and the implications for design of future trials with adequate statistical power. The analysis shows treatment differences of the mean effect which vary according to cropping season. There was a strong treatment effect between rotational treatments and other treatments and a weak effect between intercropping and monocropping. There was no evidence for an overall advantage of systems where residues are retained (in combination with direct seeding or planting basins) over conventional management with respect to maize yield. A season effect was evident although the strong benefit of rotation in El Niño season was also reduced, highlighting the strong interaction between treatment and climatic conditions. The power analysis shows that treatment effects of practically significant magnitude may be unlikely to be detected with just four replicates, as at Chitedze, under either a simple randomised control trial or a factorial experiment. Given logistical and financial constraints, it is important to design trials with fewer treatments but more replicates to gain enough statistical power and to pay attention to the selection of treatments to given an informative outcome

Predicting sample size required for classification performance

<p>Abstract</p> <p>Background</p> <p>Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target.</p> <p>Methods</p> <p>We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method.</p> <p>Results</p> <p>A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p < 0.05).</p> <p>Conclusions</p> <p>This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.</p