Knockdown of heterochromatin protein 1 binding protein 3 recapitulates phenotypic, cellular, and molecular features of aging.

Identifying genetic factors that modify an individual\u27s susceptibility to cognitive decline in aging is critical to understanding biological processes involved and mitigating risk associated with a number of age-related disorders. Recently, heterochromatin protein 1 binding protein 3 (Hp1bp3) was identified as a mediator of cognitive aging. Here, we provide a mechanistic explanation for these findings and show that targeted knockdown of Hp1bp3 in the hippocampus by 50%-75% is sufficient to induce cognitive deficits and transcriptional changes reminiscent of those observed in aging and Alzheimer\u27s disease brains. Specifically, neuroinflammatory-related pathways become activated following Hp1bp3 knockdown in combination with a robust decrease in genes involved in synaptic activity and neuronal function. To test the hypothesis that Hp1bp3 mediates susceptibility to cognitive deficits via a role in neuronal excitability, we performed slice electrophysiology demonstrate transcriptional changes after Hp1bp3 knockdown manifest functionally as a reduction in hippocampal neuronal intrinsic excitability and synaptic plasticity. In addition, as Hp1bp3 is a known mediator of miRNA biogenesis, here we profile the miRNA transcriptome and identify mir-223 as a putative regulator of a portion of observed mRNA changes, particularly those that are inflammatory-related. In summary, work here identifies Hp1bp3 as a critical mediator of aging-related changes at the phenotypic, cellular, and molecular level and will help inform the development of therapeutics designed to target either Hp1bp3 or its downstream effectors in order to promote cognitive longevity

Quantum Anomalous Hall Effect in Graphene from Rashba and Exchange Effects

We investigate the possibility of realizing quantum anomalous Hall effect in graphene. We show that a bulk energy gap can be opened in the presence of both Rashba spin-orbit coupling and an exchange field. We calculate the Berry curvature distribution and find a non-zero Chern number for the valence bands and demonstrate the existence of gapless edge states. Inspired by this finding, we also study, by first principles method, a concrete example of graphene with Fe atoms adsorbed on top, obtaining the same result.Comment: 4 papges, 5 figure

Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes

In the electricity market environment, electricity price forecasting plays an essential role in the decision-making process of a power generation company, especially in developing the optimal bidding strategy for maximizing revenues. Hence, it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm. Given this background, this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted $K$-nearest neighborhood (WKNN) method and the Gaussian process regression (GPR) approach. In the first step, several predictors, i.e., operation indicators, are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators. In the second step, the outputs of the first step are regarded as a new predictor, and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach. The proposed algorithm is verified by actual market data in Pennsylvania-New Jersey-Maryland Interconnection (PJM), and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data