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This study proposes a short-term load prediction method of a bidirectional long short-term memory network based on feature mining of the power consumption big data in combination with the attention mechanism (AT) of Bayesian optimization to address the problems that a considerable amount of feature factors exist and the feature relationship is obscured in the historical power consumption big data. The method comprehensively considers the global features of the power consumption data in space and the local features in time. First, the Cen-CK-means clustering method is used to cluster the electricity consumption data of users, and the statistical, combination, and time category characteristics are extracted according to the meteorological factors related to load over multiple time scales. Second, the Bayesian and bidirectional long and short memory networks are combined to extract the temporal and spatial characteristics of the load data itself. Meanwhile, the AT is introduced to automatically assign the corresponding weights to the hidden layer state of the bidirectional long and short memory. This task is carried out to distinguish the importance of the different time load series, which can effectively reduce the loss of historical information and highlight information about key historical time points. Finally, taking the first type of load as an example, compared with the SVP, RBPNN, BiLSTM, and BO-BiLSTM algorithms, the MAPE index is reduced by 1.05%, 1.75%, 0.52%, and 0.26%, respectively. RMSE decreased by 186.61, 154.93, 91.88, and 15.76 MW, respectively, while R2 increased by 0.04, 0.07, 0.03, and 0.03, respectively. In the one-week forecast time, MAPE index decreased by 1.97%, 2.44%, 1.21%, and 0.6%, respectively; RMSE decreased by 271.18, 305.7, 183.13, and 97.91 MW, respectively; and R2 increased by 0.12, 0.08, 0.04, and 0.03, respectively. [ABSTRACT FROM AUTHOR]

Spatial regulation of RNA plays a critical role in gene expression regulation and cellular function. Understanding spatially resolved RNA dynamics and translation is vital for bringing new insights into biological processes such as embryonic development, neurobiology, and disease pathology. This review explores past studies in subcellular, cellular, and tissue-level spatial RNA biology driven by diverse methodologies, ranging from cell fractionation, in situ and proximity labeling, imaging, spatially indexed next-generation sequencing (NGS) approaches, and spatially informed computational modeling. Particularly, recent advances have been made for near-genome-scale profiling of RNA and multimodal biomolecules at high spatial resolution. These methods enabled new discoveries into RNA's spatiotemporal kinetics, RNA processing, translation status, and RNA-protein interactions in cells and tissues. The evolving landscape of experimental and computational strategies reveals the complexity and heterogeneity of spatial RNA biology with subcellular resolution, heralding new avenues for RNA biology research., Competing Interests: Declaration of interests X.W. is a scientific co-founder of Stellaromics. X.W. and J.R. are inventors on pending patent applications (International Application no. PCT/US2022/031275, PCT/US2022/035271, PCT/US2022/028012) related to STARmap PLUS, RIBOmap, and TEMPOmap., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Characterizing the transcriptional and translational gene expression patterns at the single-cell level within their three-dimensional (3D) tissue context is essential for revealing how genes shape tissue structure and function in health and disease. However, most existing spatial profiling techniques are limited to 5-20 μm thin tissue sections. Here, we developed Deep-STARmap and Deep-RIBOmap, which enable 3D in situ quantification of thousands of gene transcripts and their corresponding translation activities, respectively, within 200-μm thick tissue blocks. This is achieved through scalable probe synthesis, hydrogel embedding with efficient probe anchoring, and robust cDNA crosslinking. We first utilized Deep-STARmap in combination with multicolor fluorescent protein imaging for simultaneous molecular cell typing and 3D neuron morphology tracing in the mouse brain. We also demonstrate that 3D spatial profiling facilitates comprehensive and quantitative analysis of tumor-immune interactions in human skin cancer., Competing Interests: Competing interests X.W. and X.S. are inventors on pending patent applications related to Deep-STARmap and Deep-RIBOmap. X.W. is a scientific co-founder and consultant of Stellaromics. Other authors declare no competing interests.

Although messenger RNA (mRNA) has proved effective as a vaccine, its potential as a general therapeutic modality is limited by its instability and low translation capacity. To increase the duration and level of protein expression from mRNA, we designed and synthesized topologically and chemically modified mRNAs with multiple synthetic poly(A) tails. Here we demonstrate that the optimized multitailed mRNA yielded ~4.7-19.5-fold higher luminescence signals than the control mRNA from 24 to 72 h post transfection in cellulo and 14 days detectable signal versus <7 days signal from the control in vivo. We further achieve efficient multiplexed genome editing of the clinically relevant genes Pcsk9 and Angptl3 in mouse liver at a minimal mRNA dosage. Taken together, these results provide a generalizable approach to synthesize capped branched mRNA with markedly enhanced translation capacity., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)