SOD1, ANG, TARDBP and FUS mutations have been associated with amyotrophic lateral sclerosis (ALS). Our goal was to extend molecular genetic analysis to newly identified ALS genetic loci and to determine the frequency of mutations, distribution of disease genes, and variant spectrum of these genes in a large United States ALS-phenotype cohort. We screened 1220 probands with an ALS phenotype, referred originally for SOD1 molecular genetic analysis. 1128 SOD1-negative probands were screened for ANG, and 277 and 223 SOD1- and ANG-negative samples were screened for TARDBP and FUS, respectively. One hundred additional probands were specifically screened only for FUS exon 15. We identified a total of 36 different SOD1 mutations, including three novel mutations, in 92 probands. ANG screening identified three mutations, including two novel mutations, and TARDBP screening identified two previously reported TARDBP mutations. We also identified four mutations in FUS, including the reported FUS in-frame deletion, c.430_447del, p.Gly144_Tyr149del, in a patient with inclusion body myositis, and two known FUS missense mutations. From this study, we estimate frequencies for SOD1, ANG, TARDBP and FUS mutations, in this United States cohort, to be 7.5%, 0.71%, 0.72% and 1.9%, respectively. In conclusion, we identify novel variants in SOD1, ANG, TARDBP and FUS, and expand the FUS-associated clinicopathologic phenotype.

Abstract. This paper presents the new and now open-source version 2.1 of the REgional Model of INvestments and Development (REMIND). REMIND, as an integrated assessment model (IAM), provides an integrated view of the global energy–economy–emissions system and explores self-consistent transformation pathways. It describes a broad range of possible futures and their relation to technical and socio-economic developments as well as policy choices. REMIND is a multiregional model incorporating the economy and a detailed representation of the energy sector implemented in the General Algebraic Modeling System (GAMS). It uses non-linear optimization to derive welfare-optimal regional transformation pathways of the energy-economic system subject to climate and sustainability constraints for the time horizon from 2005 to 2100. The resulting solution corresponds to the decentralized market outcome under the assumptions of perfect foresight of agents and internalization of external effects. REMIND enables the analyses of technology options and policy approaches for climate change mitigation with particular strength in representing the scale-up of new technologies, including renewables and their integration in power markets. The REMIND code is organized into modules that gather code relevant for specific topics. Interaction between different modules is made explicit via clearly defined sets of input and output variables. Each module can be represented by different realizations, enabling flexible configuration and extension. The spatial resolution of REMIND is flexible and depends on the resolution of the input data. Thus, the framework can be used for a variety of applications in a customized form, balancing requirements for detail and overall runtime and complexity.

Abstract. This paper presents the new and now open-source version 2.1 of the REgional Model of INvestments and Development (REMIND). REMIND, as an Integrated Assessment Model (IAM), provides an integrated view on the global energy-economy-emissions system and explores self-consistent transformation pathways. It describes a broad range of possible futures and their relation to technical and socio-economic developments as well as policy choices. REMIND is a multi-regional model incorporating the economy and a detailed representation of the energy sector implemented in the General Algebraic Modeling System (GAMS). It uses non-linear optimization to derive welfare-optimal regional transformation pathways of the energy-economic system subject to climate and sustainability constraints for the time horizon 2005 to 2100. The resulting solution corresponds to the decentral market outcome under the assumptions of perfect foresight of agents and internalization of external effects. REMIND enables analyses of technology options and policy approaches for climate change mitigation with particular strength in representing the scale-up of new technologies, including renewables and their integration in power markets. The REMIND code is organized into modules that gather code relevant for specific topics. Interaction between different modules is made explicit via clearly defined sets of input/output variables. Each module can be represented by different realizations enabling flexible configuration and extension. The spatial resolution of REMIND is flexible and depends on the resolution of the input data. The framework can thus be used for a variety of applications in a customized form balancing requirements for detail and overall run-time and complexity.