Brain-derived neurotrophic factor signaling in the HVC is required for testosterone-induced song of female canaries

Testosterone-induced singing in songbirds is thought to involve testosterone-dependent morphological changes that include angiogenesis and neuronal recruitment into the HVC, a central part of the song control circuit. Previous work showed that testosterone induces the production of vascular endothelial growth factor (VEGF) and its receptor (VEGFR2 tyrosine kinase), which in turn leads to an upregulation of brain-derived neurotrophic factor (BDNF) production in HVC endothelial cells. Here we report for the first time that systemic inhibition of the VEGFR2 tyrosine kinase is sufficient to block testosterone-induced song in adult female canaries, despite sustained androgen exposure and the persistence of the effects of testosterone on HVC morphology. Expression of exogenous BDNF in HVC, induced locally by in situ transfection, reversed the VEGFR2 inhibition-mediated blockade of song development, thereby restoring the behavioral phenotype associated with androgen-induced song. The VEGFR2-inhibited, BDNF-treated females developed elaborate malelike song that included large syllable repertoires and high syllable repetition rates, features known to attract females. Importantly, although functionally competent new neurons were recruited to HVC after testosterone treatment, the time course of neuronal addition appeared to follow BDNF-induced song development. These findings indicate that testosterone-associated VEGFR2 activity is required for androgen-induced song in adult songbirds and that the behavioral effects of VEGFR2 inhibition can be rescued by BDNF within the adult HVC. Copyright © 2009 Society for Neuroscience

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio

Induced Technological Change: Exploring its Implications for the Economics of Atmospheric Stabilization: Synthesis Report from the Innovation Modeling Comparison Project

This paper summarizes results from ten global economy-energy-environment models implementing mechanisms of endogenous technological change (ETC). Climate policy goals represented as different CO2 stabilization levels are imposed, and the contribution of induced technological change (ITC) to meeting the goals is assessed. Findings indicate that climate policy induces additional technological change, in some models substantially. Its effect is a reduction of abatement costs in all participating models. The majority of models calculate abatement costs below 1 percent of present value aggregate gross world product for the period 2000-2100. The models predict different dynamics for rising carbon costs, with some showing a decline in carbon costs towards the end of the century. There are a number of reasons for differences in results between models; however four major drivers of differences are identified. First, the extent of the necessary CO2 reduction which depends mainly on predicted baseline emissions, determines how much a model is challenged to comply with climate policy. Second, when climate policy can offset market distortions, some models show that not costs but benefits accrue from climate policy. Third, assumptions about long-term investment behavior, e.g. foresight of actors and number of available investment options, exert a major influence. Finally, whether and how options for carbon-free energy are implemented (backstop and end-of-the-pipe technologies) strongly affects both the mitigation strategy and the abatement costs

Theory and Applications of X-ray Standing Waves in Real Crystals

Theoretical aspects of x-ray standing wave method for investigation of the real structure of crystals are considered in this review paper. Starting from the general approach of the secondary radiation yield from deformed crystals this theory is applied to different concreat cases. Various models of deformed crystals like: bicrystal model, multilayer model, crystals with extended deformation field are considered in detailes. Peculiarities of x-ray standing wave behavior in different scattering geometries (Bragg, Laue) are analysed in detailes. New possibilities to solve the phase problem with x-ray standing wave method are discussed in the review. General theoretical approaches are illustrated with a big number of experimental results.Comment: 101 pages, 43 figures, 3 table

Post-2020 climate agreements in the major economies assessed in the light of global models

Integrated assessment models can help in quantifying the implications of international climate agreements and regional climate action. This paper reviews scenario results from model intercomparison projects to explore different possible outcomes of post-2020 climate negotiations, recently announced pledges and their relation to the 2 °C target. We provide key information for all the major economies, such as the year of emission peaking, regional carbon budgets and emissions allowances. We highlight the distributional consequences of climate policies, and discuss the role of carbon markets for financing clean energy investments, and achieving efficiency and equity

Fiscal Federalism and Foreign Transfers: Does Inter-Jurisdictional Competition Increase Foreign Aid Effectiveness?

This paper empirically studies the impact of decentralization and inter-jurisdictional competition on foreign aid effectiveness. For this purpose we examine a commonly used empirical growth model, considering different measures of fiscal decentralization. Our panel estimations reveal that expenditure decentralization and inter-jurisdictional competition - reflected by the degree of tax revenue decentralization - negatively impact aid effectiveness. We therefore conclude that donor countries should carefully consider how both anti-poverty instruments - foreign assistance and decentralization - work together

Validating anthropogenic threat maps as a tool for assessing river ecological integrity in Andean-Amazon basins

Anthropogenic threat maps are commonly used as a surrogate for the ecological integrity of rivers in freshwater conservation, but a clearer understanding of their relationships is required to develop proper management plans at large scales. Here, we developed and validated empirical models that link the ecological integrity of rivers to threat maps in a large, heterogeneous and biodiverse Andean-Amazon watershed. Through fieldwork, we recorded data on aquatic invertebrate community composition, habitat quality, and physical-chemical parameters to calculate the ecological integrity of 140 streams/rivers across the basin. Simultaneously, we generated maps that describe the location, extent, and magnitude of impact of nine anthropogenic threats to freshwater systems in the basin. Through seven-fold cross-validation procedure, we found that regression models based on anthropogenic threats alone have limited power for predicting the ecological integrity of rivers. However, the prediction accuracy improved when environmental predictors (slope and elevation) were included, and more so when the predictions were carried out at a coarser scale, such as microbasins. Moreover, anthropogenic threats that amplify the incidence of other pressures (roads, human settlements and oil activities) are the most relevant predictors of ecological integrity. We concluded that threat maps can offer an overall picture of the ecological integrity pattern of the basin, becoming a useful tool for broad-scale conservation planning for freshwater ecosystems. While it is always advisable to have finer scale in situ measurements of ecological integrity, our study shows that threat maps provide fast and cost-effective results, which so often are needed for pressing management and conservation actions

Lumbar spine segmentation in MR images: a dataset and a public benchmark

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain. It was collected from four different hospitals and was divided into a training (179 patients) and validation (39 patients) set. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. We set up a continuous segmentation challenge to allow for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation, and improve the diagnostic value of lumbar spine MRI