Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional methods.

The genetic heterogeneities in cancer cells pose challenges to achieving precise drug treatment in a widely applicable manner. Most single-cell gene analysis methods rely on cell lysis for gene extraction and identification, showing limited capacity to provide the correlation of genetic properties and realtime cellular behaviors. Here, we report a single living cell analysis nanoplatform that enables interrogating gene properties and drug resistance in millions of single cells. We designed a Domino-probe to identify intracellular target RNAs while releasing 10-fold amplified fluorescence signals. An on-chip addressable microwellnanopore array was developed for enhanced electro-delivery of the Domino-probe and in situ observation of cell behaviors. The proofof-concept of the system was validated in primary lung cancer cell samples, revealing the positive-correlation of the ratio of EGFR mutant cells with their drug susceptibilities. This platform provides a high-throughput yet precise tool for exploring the relationship between intracellular genes and cell behaviors at the single-cell level.

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graphstructured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNN structures, e. g., graph convolutional networks, are summarized. Key applications in power systems such as fault scenario application, time-series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.

The physical properties of the DNA oligomer d(CGCGCGTTTTCGCGCG) in solvents containing 4 M NaClO4 and 0.1 M NaCl were investigated by proton NMR, optical melting, and circular dichroism spectroscopy. Results of these investigations are as follows: (i) The DNA hexadecamer exists as a unimolecular hairpin in either high or low salt. (ii) In high salt the stem region of the hairpin is in the left-handed Z conformation. (iii) In either high or low salt, the duplex stem of the hairpin is stabilized against melting by approximately 40 degrees C compared to the linear core duplex. The added stability of the hairpin is entropic in origin. (iv) In high salt, as the temperature is elevated, the equilibrium structure of the duplex stem of the hairpin shifts from the Z to the B conformation before melting. (v) In low salt, when the DNA duplex exists in the B conformation, attachment of a T4 single-strand loop to one end only slightly decreases (by 14%) the correlation time of the CH5-CH6 interproton vector. In high salt, when the DNA duplex exists in the Z conformation, the correlation time of the CH5-CH6 interproton vector decreases by 51%. Since these viscosity-corrected correlation times are taken to be indicators of duplex motions on the nanosecond time scale, this result directly suggests a larger amplitude of these motions is present in the duplex stem of the hairpin when it exists in the Z conformation.

Summary Lamin proteins in animals are implicated in important nuclear functions, including chromatin organization, signalling transduction, gene regulation and cell differentiation. Nuclear Matrix Constituent Proteins (NMCPs) are lamin analogues in plants, but their regulatory functions remain largely unknown. We report that OsNMCP1 is localized at the nuclear periphery in rice (Oryza sativa) and induced by drought stress. OsNMCP1 overexpression resulted in a deeper and thicker root system, and enhanced drought resistance compared to the wild‐type control. An assay for transposase accessible chromatin with sequencing (ATAC‐seq) analysis revealed that OsNMCP1‐overexpression altered chromatin accessibility in hundreds of genes related to drought resistance and root growth, including OsNAC10, OsERF48, OsSGL, SNAC1 and OsbZIP23. OsNMCP1 can interact with SWITCH/SUCROSE NONFERMENTING (SWI/SNF) chromatin remodelling complex subunit OsSWI3C. The reported drought resistance or root growth‐related genes that were positively regulated by OsNMCP1 were negatively regulated by OsSWI3C under drought stress conditions, and OsSWI3C overexpression led to decreased drought resistance. We propose that the interaction between OsNMCP1 and OsSWI3C under drought stress conditions may lead to the release of OsSWI3C from the SWI/SNF gene silencing complex, thus changing chromatin accessibility in the genes related to root growth and drought resistance.