Modeling all alternative solutions for highly renewable energy systems

As the world is transitioning towards highly renewable energy systems, advanced tools are needed to analyze such complex networks. Energy system design is, however, challenged by real-world objective functions consisting of a blurry mix of technical and socioeconomic agendas, with limitations that cannot always be clearly stated. As a result, it is highly likely that solutions which are techno-economically suboptimal will be preferable. Here, we present a method capable of determining the continuum containing all techno-economically near-optimal solutions, moving the field of energy system modeling from discrete solutions to a new era where continuous solution ranges are available. The presented method is applied to study a range of technical and socioeconomic metrics on a model of the European electricity system. The near-optimal region is found to be relatively flat allowing for solutions that are slightly more expensive than the optimum but better in terms of equality, land use, and implementation time.Comment: 25 pages, 7 figures, also available as preprint at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=368204

CFTR is involved in the regulation of glucagon secretion in human and rodent alpha cells

Glucagon is the main counterregulatory hormone in the body. Still, the mechanism involved in the regulation of glucagon secretion from pancreatic alpha cells remains elusive. Dysregulated glucagon secretion is common in patients with Cystic Fibrosis (CF) that develop CF related diabetes (CFRD). CF is caused by a mutation in the Cl(-) channel Cystic fibrosis transmembrane conductance regulator (CFTR), but whether CFTR is present in human alpha cells and regulate glucagon secretion has not been investigated in detail. Here, both human and mouse alpha cells showed CFTR protein expression, whereas CFTR was absent in somatostatin secreting delta cells. CFTR-current activity induced by cAMP was measured in single alpha cells. Glucagon secretion at different glucose levels and in the presence of forskolin was increased by CFTR-inhibition in human islets, whereas depolarization-induced glucagon secretion was unaffected. CFTR is suggested to mainly regulate the membrane potential through an intrinsic alpha cell effect, as supported by a mathematical model of alpha cell electrophysiology. In conclusion, CFTR channels are present in alpha cells and act as important negative regulators of cAMP-enhanced glucagon secretion through effects on alpha cell membrane potential. Our data support that loss-of-function mutations in CFTR contributes to dysregulated glucagon secretion in CFRD

Genomic Prediction in Tetraploid Ryegrass Using Allele Frequencies Based on Genotyping by Sequencing

Perennial ryegrass is an outbreeding forage species and is one of the most widely used forage grasses in temperate regions. The aim of this study was to investigate the possibility of implementing genomic prediction in tetraploid perennial ryegrass, to study the effects of different sequencing depth when using genotyping-by-sequencing (GBS), and to determine optimal number of single-nucleotide polymorphism (SNP) markers and sequencing depth for GBS data when applied in tetraploids. A total of 1,515 F2 tetraploid ryegrass families were included in the study and phenotypes and genotypes were scored on family-pools. The traits considered were dry matter yield (DM), rust resistance (RUST), and heading date (HD). The genomic information was obtained in the form of allele frequencies of pooled family samples using GBS. Different SNP filtering strategies were designed. The strategies included filtering out SNPs having low average depth (FILTLOW), having high average depth (FILTHIGH), and having both low average and high average depth (FILTBOTH). In addition, SNPs were kept randomly with different data sizes (RAN). The accuracy of genomic prediction was evaluated by using a “leave single F2 family out” cross validation scheme, and the predictive ability and bias were assessed by correlating phenotypes corrected for fixed effects with predicted additive breeding values. Among all the filtering scenarios, the highest estimates for genomic heritability of family means were 0.45, 0.74, and 0.73 for DM, HD and RUST, respectively. The predictive ability generally increased as the number of SNPs included in the analysis increased. The highest predictive ability for DM was 0.34 (137,191 SNPs having average depth higher than 10), for HD was 0.77 (185,297 SNPs having average depth lower than 60), and for RUST was 0.55 (188,832 SNPs having average depth higher than 1). Genomic prediction can help to optimize the breeding of tetraploid ryegrass. GBS data including about 80–100 K SNPs are needed for accurate prediction of additive breeding values in tetraploid ryegrass. Using only SNPs with sequencing depth between 10 and 20 gave highest predictive ability, and showed the potential to obtain accurate prediction from medium-low coverage GBS in tetraploids