The GAINS Model for Greenhouse Gases - Version 1.0: HFC, PFC AND SF6

Many of the traditional air pollutants and greenhouse gases have common sources, offering a cost-effective potential for simultaneous improvements of traditional air pollution problems and climate change. A methodology has been developed to extend the RAINS integrated assessment model to explore synergies and trade-offs between the control of greenhouse gases and air pollution. With this extension, the GAINS (GHG-Air pollution INteraction and Synergies) model will allow the assessment of emission control costs for the six greenhouse gases covered under the Kyoto Protocol (CO2, CH4, N2O and the three F-gases) together with the emissions of air pollutants SO2, NOx, VOC, NH3 and PM. This report describes the first implementation (Version 1.0) of the model extension model to incorporate emissions of the Fgases, i.e., HFC, PFC and SF6. GAINS Version 1.0 assesses 230 options for reducing F-gas emissions from the various source categories. It quantifies for 43 countries/regions in Europe country-specific application potentials of the various options in the different sectors of the economy, and estimates the societal resource costs of these measures. Mitigation potentials are estimated in relation to an exogenous baseline projection that reflects current planning. The initial implementation of GAINS 1.0 estimates for 1995 total F-gas emissions in the European model domain (39 countries including the European part of Russia) at around 87 Mt CO2eq. With current legislation emissions are expected to increase by a factor two in 2020, due to the expected increase in HFC emissions from mobile air conditioning and refrigerating. 34 mitigation options for F-gases have been identified and implemented in GAINS 1.0. Full implementation of these options could reduce in 2020 total European F-gas emissions by more than 70 percent (compared to the current legislation baseline projection), which would keep these emissions below their 1995 levels. Marginal costs of these options range from 0.1 to 64 Euro/tCO2eq. More than half of these options have costs below 20 Euro/tCO2eq. More than half of these options have costs below 20 Euro/tCO2eq. Uncertainties in the estimates of emissions (and hence control costs) are large due to uncertainties in emission factors, the future penetration of technologies and abatement measures as well as lack of data on activities in a number of countries

Potentials and Costs for Mitigation of Non-CO2 Greenhouse Gases in Annex 1 Countries: Version 2.0

This report documents the specific methodology of IIASA's GAINS model on methane, nitrous oxide and fluorinated gases that has been used for comparing mitigation efforts across Annex I Parties. More details are available at gains.iiasa.ac.at

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 s and no pre-processing requirements. We trained and validated MU-Net on 128 T2-weighted mouse MRI volumes as well as on the publicly available MRM NeAT dataset of 10 MRI volumes. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). Further, we explored the effectiveness of our network in the presence of different architectural features, including skip connections and recently proposed framing connections, and the effects of the age range of the training set animals. These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net

The Extension of the RAINS Model to Greenhouse Gases

Many of the traditional air pollutants and greenhouse gases have common sources, offering a cost-effective potential for simultaneous improvements for both traditional air pollution problems as well as climate change. A methodology has been developed to extend the RAINS integrated assessment model to explore synergies and trade-offs between the control of greenhouse gases and air pollution. With this extension, the RAINS model allows now the assessment of emission control costs for the six greenhouse gases covered under the Kyoto Protocol (CO2, CH4, N2O and the three F-gases) together with the emissions of air pollutants SO2, NOX, VOC, NH3 AND PM. In the first phase of the study, emissions, costs and control potentials for the six greenhouse gases covered in the Kyoto Protocol have been estimated and implemented in the RAINS model. Emission estimates are based on methodologies and emission factors proposed by the IPCC emission reporting guidelines. The large number of control options for greenhouse gases have been grouped into approximately 150 packages of measures and implemented in the RAINS model for the European countries. These control options span a wide range of cost-effectiveness. There a re certain advanced technical measures with moderate costs, and certain measures exist for which the economic assessment suggests even negative costs, if major side impacts (cost savings) are calculated. Illustrative example calculations clearly demonstrate that conclusions on the cost-effectiveness of emission reduction strategies are crucially depending on the boundaries of the analysis. The net cost of greenhouse gas control strategies are significantly lower if the immediate cost-savings from avoided air pollution control costs are taken into consideration. For a 15 percent reduction of the CO2 emissions from the power sector in the EU, avoided pollution control costs could compensate two third of the CO2 control costs. Depending on the design of the control strategy, net costs of greenhouse gas mitigation could even be negative, which is in stark contrast to conclusions for a CO2 only strategy. However, there are certain greenhouse gas mitigation measures, such as increased use of biomass that could deteriorate the negative impacts of air pollution, while yielding very little economic synergies. A combined approach towards greenhouse gas mitigation and air pollution control would not only reveal economic synergies, but also harness additional environmental benefits. Even in a situation with stringent emission control requirements for air pollution as it is required by the EU legislation, modifications in fuel use geared towards reductions of greenhouse gases could lead as a side impact to significant reductions in the residual emissions of air pollutants. The economic benefits of such "windfall emission reductions" could be substantial. The extended RAINS model framework will offer a tool to systematically investigate such economic and environmental synergies between greenhouse gas mitigation and air pollution control while avoiding negative side impacts

Histopathological modeling of status epilepticus-induced brain damage based on in vivo diffusion tensor imaging in rats

Non-invasive magnetic resonance imaging (MRI) methods have proved useful in the diagnosis and prognosis of neurodegenerative diseases. However, the interpretation of imaging outcomes in terms of tissue pathology is still challenging. This study goes beyond the current interpretation of in vivo diffusion tensor imaging (DTI) by constructing multivariate models of quantitative tissue microstructure in status epilepticus (SE)-induced brain damage. We performed in vivo DTI and histology in rats at 79 days after SE and control animals. The analyses focused on the corpus callosum, hippocampal subfield CA3b, and layers V and VI of the parietal cortex. Comparison between control and SE rats indicated that a combination of microstructural tissue changes occurring after SE, such as cellularity, organization of myelinated axons, and/or morphology of astrocytes, affect DTI parameters. Subsequently, we constructed a multivariate regression model for explaining and predicting histological parameters based on DTI. The model revealed that DTI predicted well the organization of myelinated axons (cross-validated R = 0.876) and astrocyte processes (cross-validated R = 0.909) and possessed a predictive value for cell density (CD) (cross-validated R = 0.489). However, the morphology of astrocytes (cross-validated R > 0.05) was not well predicted. The inclusion of parameters from CA3b was necessary for modeling histopathology. Moreover, the multivariate DTI model explained better histological parameters than any univariate model. In conclusion, we demonstrate that combining several analytical and statistical tools can help interpret imaging outcomes to microstructural tissue changes, opening new avenues to improve the non-invasive diagnosis and prognosis of brain tissue damage

Steerable3D: An ImageJ plugin for neurovascular enhancement in 3-D segmentation

PurposeImage processing plays a fundamental role in the study of central nervous system, for example in the analysis of the vascular network in neurodegenerative diseases. Synchrotron X-ray Phase-contrast micro-Tomography (SXPCT) is a very attractive method to study weakly absorbing samples and features, such as the vascular network in the spinal cord (SC). However, the identification and segmentation of vascular structures in SXPCT images is seriously hampered by the presence of image noise and strong contrast inhomogeneities, due to the sensitivity of the technique to small electronic density variations. In order to help with these tasks, we implemented a user-friendly ImageJ plugin based on a 3D Gaussian steerable filter, tuned up for the enhancement of tubular structures in SXPCT images.MethodsThe developed 3D Gaussian steerable filter plugin for ImageJ is based on the steerability properties of Gaussian derivatives. We applied it to SXPCT images of ex-vivo mouse SCs acquired at different experimental conditions.ResultsThe filter response shows a strong amplification of the source image contrast-to-background ratio (CBR), independently of structures orientation. We found that after the filter application, the CBR ratio increases by a factor ranging from ~6 to ~60. In addition, we also observed an increase of 35% of the contrast to noise ratio in the case of injured mouse SC.ConclusionThe developed tool can generally facilitate the detection/segmentation of capillaries, veins and arteries that were not clearly observable in non-filtered SXPCT images. Its systematic application could allow obtaining quantitative information from pre-clinical and clinical images

Sex-specific transcriptional and proteomic signatures in schizophrenia

It has remained unclear why schizophrenia typically manifests after adolescence and which neurobiological mechanisms are underlying the cascade leading to the actual onset of the illness. Here we show that the use of induced pluripotent stem cell-derived neurons of monozygotic twins from pairs discordant for schizophrenia enhances disease-specific signal by minimizing genetic heterogeneity. In proteomic and pathway analyses, clinical illness is associated especially with altered glycosaminoglycan, GABAergic synapse, sialylation, and purine metabolism pathways. Although only 12% of all 19,462 genes are expressed differentially between healthy males and females, up to 61% of the illness-related genes are sex specific. These results on sex-specific genes are replicated in another dataset. This implies that the pathophysiology differs between males and females, and may explain why symptoms appear after adolescence when the expression of many sex-specific genes change, and suggests the need for sex-specific treatments.Peer reviewe

Regional Grey Matter Structure Differences between Transsexuals and Healthy Controls-A Voxel Based Morphometry Study.

Gender identity disorder (GID) refers to transsexual individuals who feel that their assigned biological gender is incongruent with their gender identity and this cannot be explained by any physical intersex condition. There is growing scientific interest in the last decades in studying the neuroanatomy and brain functions of transsexual individuals to better understand both the neuroanatomical features of transsexualism and the background of gender identity. So far, results are inconclusive but in general, transsexualism has been associated with a distinct neuroanatomical pattern. Studies mainly focused on male to female (MTF) transsexuals and there is scarcity of data acquired on female to male (FTM) transsexuals. Thus, our aim was to analyze structural MRI data with voxel based morphometry (VBM) obtained from both FTM and MTF transsexuals (n = 17) and compare them to the data of 18 age matched healthy control subjects (both males and females). We found differences in the regional grey matter (GM) structure of transsexual compared with control subjects, independent from their biological gender, in the cerebellum, the left angular gyrus and in the left inferior parietal lobule. Additionally, our findings showed that in several brain areas, regarding their GM volume, transsexual subjects did not differ significantly from controls sharing their gender identity but were different from those sharing their biological gender (areas in the left and right precentral gyri, the left postcentral gyrus, the left posterior cingulate, precuneus and calcarinus, the right cuneus, the right fusiform, lingual, middle and inferior occipital, and inferior temporal gyri). These results support the notion that structural brain differences exist between transsexual and healthy control subjects and that majority of these structural differences are dependent on the biological gender