Sharing Economy in Local Energy Markets

With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of 'access over ownership', the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.</p

Sodium glucose co-transporter 2 (SGLT2) inhibition via dapagliflozin improves diabetic kidney disease (DKD) over time associatied with increasing effect on the gut microbiota in db/db mice

BackgroundThe intestinal microbiota disorder gradually aggravates during the progression of diabetes. Dapagliflozin (DAPA) can improve diabetes and diabetic kidney disease(DKD). However, whether the gut microbiota plays a role in the protection of DAPA for DKD remains unclear.MethodsTo investigate the effects of DAPA on DKD and gut microbiota composition during disease progression, in our study, we performed 16S rRNA gene sequencing on fecal samples from db/m mice (control group), db/db mice (DKD model group), and those treated with DAPA (treat group) at three timepoints of 14weeks\18weeks\22weeks.ResultsWe found that DAPA remarkably prevented weight loss and lowered fasting blood glucose in db/db mice during disease progression, eventually delaying the progression of DKD. Intriguingly, the study strongly suggested that there is gradually aggravated dysbacteriosis and increased bile acid during the development of DKD. More importantly, comparisons of relative abundance at the phylum level and partial least squares-discriminant analysis (PLS-DA) plots roughly reflected that the effect of DAPA on modulating the flora of db/db mice increased with time. Specifically, the relative abundance of the dominant Firmicutes and Bacteroidetes was not meaningfully changed among groups at 14 weeks as previous studies described. Interestingly, they were gradually altered in the treat group compared to the model group with a more protracted intervention of 18 weeks and 22 weeks. Furthermore, the decrease of Lactobacillus and the increase of norank_f:Muribaculaceae could account for the differences at the phylum level observed between the treat group and the model group at 18 weeks and 22 weeks.ConclusionWe firstly found that the protective effect of DAPA on DKD may be related to the dynamic improvement of the gut microbiota over time, possibly associated with the impact of DAPA on the bile acid pool and its antioxidation effect

Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice

Manual phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, automated, non-destructive phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale assessment of rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We could explain 30% of the grain yield variance from 2 tiller traits assessed in the early growth stages. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and rice accessions containing haplotype H3 displayed much smaller tiller angles. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting

Brain Region-wise Connectivity-based Psychometric Prediction Framework, Interpretation, Replicability and Generalizability

The study of brain-behavior relationships is a fundamental aspect of neuroscience.Recently, it has become increasingly popular to investigate brain-behavior relationshipsby relating the interindividual variability in psychometric measure to the interindividualvariability in brain imaging data. In particular, prediction approaches withcross-validation can be useful for identifying generalizable brain-behavior relationshipsin a data-driven manner. Nevertheless, it remains to be ascertained what brain-behaviorrelationships can be interpreted from the prediction models, and how generalizable themodels are to fully new cohorts. In this work, we attempt to fill in the gap ofinterpretability by developing a region-wise connectivity-based psychometric prediction(CBPP) framework. This framework involves a region-wise approach where a predictionmodel is estimated and evaluated for each brain region. The prediction accuracy of eachregion-wise model is a direct indication of that brain region’s association with thepsychometric measure predicted. In study 1, we applied the framework to a range ofpsychometric variables from a large healthy cohort and demonstrated the helpfulness ofthe framework in constructing region-wise psychometric prediction profiles orpsychometric-wise prediction pattern across the brain. In study 2, we demonstrated theusefulness of the framework in assessing cross-cohort replicability and generalizability interms of brain-behavior relationships derived from the prediction models, instead of justbased on prediction accuracies. In study 3, we systematically examined existingpsychometric prediction studies, summarizing the trends in the field, calling for the useof large cohorts and external validation. Overall, our work suggested the importance ofinterpretability and generalizability for psychometric prediction, recommending the useof multiple large cohorts in evaluating the interpretability and generalizability

Brain Region-wise Connectivity-based Psychometric Prediction: Framework, Interpretation, Replicability and Generalizability

The study of brain-behavior relationships is a fundamental aspect of neuroscience. Recently, it has become increasingly popular to investigate brain-behavior relationships by relating the interindividual variability in psychometric measure to the interindividual variability in brain imaging data. In particular, prediction approaches with cross-validation can be useful for identifying generalizable brain-behavior relationships in a data-driven manner. Nevertheless, it remains to be ascertained what brain-behavior relationships can be interpreted from the prediction models, and how generalizable the models are to fully new cohorts. In this work, we attempt to fill in the gap of interpretability by developing a region-wise \acrfull{cbpp} framework. This framework involves a region-wise approach where a prediction model is estimated and evaluated for each brain region. The prediction accuracy of each region-wise model is a direct indication of that brain region's association with the psychometric measure predicted. In study 1, we applied the framework to a range of psychometric variables from a large healthy cohort and demonstrated the helpfulness of the framework in constructing region-wise psychometric prediction profiles or psychometric-wise prediction pattern across the brain. In study 2, we demonstrated the usefulness of the framework in assessing cross-cohort replicability and generalizability in terms of brain-behavior relationships derived from the prediction models, instead of just based on prediction accuracies. In study 3, we systematically examined existing psychometric prediction studies, summarizing the trends in the field, calling for the use of large cohorts and external validation. Overall, our work suggested the importance of interpretability and generalizability for psychometric prediction, recommending the use of multiple large cohorts in evaluating the interpretability and generalizability

ACCURATE NONLINEAR MAPPING BETWEEN MNI152/COLIN27 VOLUMETRIC AND FREESURFER SURFACE COORDINATE SYSTEMS

Master'sMASTER OF ENGINEERIN