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Integrated energy systems （IES）， characterized by multi-energy complementarity， can enhance energy utilization and improve economic efficiency through effective scheduling. To achieve optimal scheduling for IES， the energy characteristics of heating and cooling system networks are considered， and an energy transport model for these systems is developed. By leveraging the flexibility of heating and cooling loads and the energy storage properties of the thermal networks’ working medium， a virtual energy storage model for the IES， integrating cooling， heating， and power systems， is developed. The heating and cooling systems are integrated as scheduling resources in the system’s optimal operation， with the objective of minimizing daily operational costs. An optimal scheduling method for IES， considering virtual energy storage， is proposed to achieve energy complementarity among the systems. Simulation results demonstrate that this method effectively balances the mismatch between output and load using the virtual energy storage system， responds proactively to time-of-use tariffs， reduces the peak-valley difference in purchased electricity， and significantly lowers the system’s economic costs.

Septic Cardiomyopathy (SCM) is a syndrome of acute cardiac dysfunction in septic patients, unrelated to cardiac ischemia. Multiomics studies including transcriptomics and proteomics have provided new insights into the mechanisms of SCM. In here, a rat model of SCM was established by intraperitoneal injection of lipopolysaccharide (LPS). Biomarkers of SCM were characterized via a multi-omics analysis. The differentially expressed (DE) mRNAs predominantly appeared in pathways linked to the immune response, inflammatory response, and the complement and coagulation cascades, while DE proteins were mainly enriched in pathways associated with the complement and coagulation cascades. On this basis, the integrated analysis was performed between transcriptome and proteome. The potential biomarkers were further verified by RT-qPCR and WB. The current proteotranscriptomic research has furnished a valuable dataset and fresh perspectives that will enhance our comprehension of the development of SCM. This, in turn, is expected to expedite the formulation of novel approaches for the prevention and management of SCM in patients. [ABSTRACT FROM AUTHOR]

Because the global climate change intensifies as well as the natural disasters frequently occur, extreme events have caused serious impacts on the energy system in urban areas, and at the same time, they have brought great challenges to the supply and scheduling of urban energy systems. Therefore, in order to better integrate and manage various energy resources in urban areas, a Deep Q-Leaning Network-Quasi Upper Confidence Bound model is innovatively constructed using deep reinforcement learning technology to learn the state and behavior mapping relationship of energy system. Use deep learning to fit complex nonlinear models to optimize the entire energy system. Compare and verify the experiment with the real energy system. The improved Deep reinforcement learning algorithm is compared with Q-learning model, PDWoLF PHC algorithm model, Quasi Upper Confidence Bound algorithm model and deep Q-Leaning Network algorithm model. The results show that the research algorithm has the smallest instantaneous error value and absolute value of frequency deviation for area control, and the average value of the research algorithm in the absolute value of the frequency deviation is reduced by 45%–73% compared to other algorithms; over time, the unit output power of the research algorithm is able to flexibly track the stochastic square wave loads. Therefore, the proposed system strategies can provide feasible solutions to meet the challenges of extreme events and promote the sustainable development and safe operation of urban energy systems. [ABSTRACT FROM AUTHOR]

With the development of the economy, people's demand for green energy has increased significantly. However, the traditional single fossil energy supply system cannot meet the needs of low-carbon. Therefore, this study employs energy hub to establish a multi-energy flow network that enables the integration of carbon flow within the network. Additionally, by utilizing the multi-energy flow trend, a carbon flow tracking method is adopted to achieve real-time carbon flow calculation. Results show that this network calculates the electricity cost of 20043 yuan, gas cost of 67253 yuan, and carbon emission cost of 3152 yuan. Compared with the traditional energy flow system, gas cost is reduced by 4.3% and 1.7%, electricity cost by 21.3% and 15.0%, and carbon emission cost by 8.7% and 6.6%. The two-way sharing carbon flow calculation model calculates that the user side and power supply side of the node each bear half of the network loss, proving two-way sharing effectiveness. Test results on IEEE5 machine 14-node system show that the calculation method can accurately find high-emission and low-emission areas, making the carbon emission allocation between power generation and user more fair and reasonable. This research can effectively reduce emissions cost, accurately calculate emissions flow in real time, and facilitate reasonable emission reduction planning. [ABSTRACT FROM AUTHOR]

Day-ahead scheduling strategy is an effective way to improve the renewable energy accommodation. To increase the renewable energy accommodation in the regional power grids, reduce the total costs of the power system, and improve the supply reliability of the power system, this research suggests a multi-time-scale "source-storage-load" coordinated dispatching strategy that considers the distribution and characteristics of pumped energy storage and loss of the network. Taking the wind curtailment penalty costs, the system operating costs, and the load loss penalty costs as the objective functions, a day-ahead coordinated scheduling strategy for source storage and load considering demand response and lines loss is established. Finally, the commercial software package CPLEX is called through the MATLAB platform to complete the optimization of mixed integer programming. Simulation results shows that the proposed scheduling strategy could build the power generation plant, effectively adjust the output power of pumped storage, and regulate the assumption of translationable load and transferable load. [ABSTRACT FROM AUTHOR]

Blockchain is an important part of the new generation of information technology. It is a new database software integrated with distributed network, encryption technology, smart contract and other technologies. Over the past decade, blockchain technology has had a wide impact on a global scale. Today's blockchain technology has shifted from its initial focus on the decentralization of cryptocurrency and payment to the decentralization of the market. The emergence of smart contract makes the decentralized finance (De-Fi) based on blockchain technology enter a state of rapid development, and various auction scenarios in the context of blockchain also emerge. From the perspective of mechanism design, this paper is the first to summarize and analyze the auction mechanisms on the blockchain in recent years by taking the transaction fee mechanism, NFT auction and MEV auction as the main objects. In addition, we also highlight the challenges and the open problems of the auction mechanism design, based on the characteristics of blockchain. [ABSTRACT FROM AUTHOR]

Background: Our previous single-cell RNA sequencing study in the adult human heart revealed that cardiomyocytes from both the atrium and ventricle display high activities of Krüppel-like factor 2 (KLF2) regulons. However, the role of the transcription factor KLF2 in cardiomyocyte biology remains largely unexplored., Methods and Results: We employed transverse aortic constriction surgery in male C57BL/6 J mice to develop an in vivo model of cardiac hypertrophy, and generated different in vitro cardiac hypertrophy models in neonatal rat ventricular myocytes and human embryonic stem cell-derived cardiomyocytes. Our results demonstrated a significant reduction in KLF2 expression during the progression of cardiac hypertrophy. In vitro, Klf2 deficiency exacerbates cardiac hypertrophy and enhances hypertrophic reprogramming, while KLF2 overexpression attenuates cardiac hypertrophy and reverses hypertrophic transcriptome reprogramming. Mechanistically, combined RNA-seq and cleavage under targets & tagmentation (CUT&Tag) analysis revealed that KLF2 exerts its protective effects by directly regulating a set of genes associated with cardiac hypertrophy. In vivo, KLF2 overexpression specifically in cardiomyocytes effectively prevents TAC-induced cardiac hypertrophy in mice. Additionally, we found that simvastatin elevates KLF2 expression in cardiomyocytes, which subsequently alleviates cardiomyocyte hypertrophy., Conclusions: This study provides the first evidence that transcription factor KLF2 serves as a negative regulator of cardiac hypertrophy. Our findings highlight the therapeutic potential of enhancing KLF2 expression, particularly through simvastatin administration, as a promising strategy in the treatment of cardiac hypertrophy., Competing Interests: Declaration of competing interest All the authors declared no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Mechanisms underpinning signals from genome-wide association studies remain poorly understood, particularly for noncoding variation and for complex diseases such as type 2 diabetes mellitus (T2D) where pathogenic mechanisms in multiple different tissues may be disease driving. One approach is to study relevant endophenotypes, a strategy we applied to the UBE2E2 locus where noncoding single nucleotide variants (SNVs) are associated with both T2D and visceral adiposity (a pathologic endophenotype). We integrated CRISPR targeting of SNV-containing regions and unbiased CRISPR interference (CRISPRi) screening to establish candidate cis-regulatory regions, complemented by genetic loss of function in murine diet-induced obesity or ex vivo adipogenesis assays. Nomination of a single causal gene was complicated, however, because targeting of multiple genes near UBE2E2 attenuated adipogenesis in vitro; CRISPR excision of SNV-containing noncoding regions and a CRISPRi regulatory screen across the locus suggested concomitant regulation of UBE2E2, the more distant UBE2E1, and other neighborhood genes; and compound heterozygous loss of function of both Ube2e2 and Ube2e1 better replicated pathological adiposity and metabolic phenotypes compared with homozygous loss of either gene in isolation. This study advances a model whereby regulatory effects of noncoding variation not only extend beyond the nearest gene but may also drive complex diseases through polygenic regulatory effects.