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Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.

Abstract The doubly-fed induction generator (DFIG) with virtual inertia control and reactive damping control gives a renewable energy generation system inertia and damping characteristics similar to those of a thermal power plant, and the parameters of the control strategy have a direct impact on the small-signal stability of the system. This paper firstly introduces the operating characteristics and control strategies of DFIG-based damping control and virtual inertia control, establishes a small-signal model of the control-based DFIG integrated interconnected system, and investigates the effects of virtual inertia and reactive damping values on the small-signal stability of the system; then, the maximum damping ratio of the interval oscillation mode in small disturbance analysis is taken as the optimization objective, and the control parameters are the optimization variables. An optimization method of inertia and damping parameters is established for improving the small disturbance stability of the system. The results show that the optimization procedure could improve the damping ratio of the interval oscillation mode while ensuring the system frequency. The effects of virtual inertia and reactive damping values on the small signal stability of the system are investigated, and an optimal allocation model and method for virtual inertia used to improve the small disturbance stability of the system is proposed.

Previous study consistently showed that lower serum sodium (SNa) was associated with a greater risk of mortality in hemodialysis (HD) patients. However, few studies have focused on the change in SNa (ΔSNa = post-HD SNa – pre-HD SNa) during an HD session. In a retrospective cohort of maintenance HD adults, all-cause mortality and cardio-cerebrovascular event (CCVE) were followed up for a medium of 82 months. Baseline pre-HD SNa and ΔSNa were collected; time-averaged pre-HD SNa and ΔSNa were computed as the mean values within 1-year, 2-year and 3-year intervals after enrollment. Cox proportional hazards models were used to evaluate the relationships of pre-HD and ΔSNa with outcomes. Time-averaged pre-HD SNa were associated with all-cause mortality (2-year pre-HD SNa: HR [95% CI] 0.86 [0.74–0.99], p = 0.042) and CCVE (3-year pre-HD SNa: HR [95% CI] 0.83 [0.72–0.96], p = 0.012) with full adjustment. Time-averaged ΔSNa also demonstrated an association with all-cause mortality (3-year ΔSNa: HR [95% CI] 1.26 [1.03–1.55], p = 0.026) as well as with CCVE (3-year ΔSNa: HR [95% CI] 1.51 [1.21–1.88], p = <0.001) when fully adjusted. Baseline pre-HD SNa and ΔSNa didn't exhibit association with both outcomes. Lower time-averaged pre-HD SNa and higher time-averaged ΔSNa were associated with a greater risk of all-cause mortality and CCVE in HD patients. [ABSTRACT FROM AUTHOR]

SUMMARY: Radish (Raphanus sativus L.) is one of the most important root vegetable crops worldwide. However, gene function exploration and germplasm innovation still face tremendous challenges due to its extremely low transformation efficiency. Here, an economic and highly efficient genetic transformation method for radish was explored by Agrobacterium rhizogenes‐mediated transformation with the help of combining special developmental regulator (DR) genes and the visual identification reporter. Firstly, the RUBY gene, a betalain biosynthesis system, could result in a visual red‐violet color used as a convenient and effective reporter for monitoring transgenic hairy roots screening of radish. However, the hairy roots‐to‐shoots conversion system of radish still stands as a barrier to the obtainment of whole transgenic plants, although different hormone combinations and various culture conditions were tried. Following, two DR genes including Wuschel2 (Wus2) and isopentenyl transferase (ipt), as well as their combination Wus2‐ipt were introduced for the shoot regeneration capacity improvement. The results showed that the transgenic shoots could be directly generated without externally supplying any hormones in the presence of a Wus2‐ipt combination. Then, Wus2‐ipt along with the RUBY reporter was employed to establish an efficient genetic transformation system of radish. Moreover, this system was applied in generating gene‐edited radish plants and the phytoene desaturase (RsPDS) gene was effectively knockout through albino phenotype observation and sequencing analysis. These findings have the potential to be widely applied in genetic transformation and genome‐editing genetic improvement of other vegetable species. Significance Statement: We successfully explored an economic and highly efficient genetic transformation method for radish by Agrobacterium rhizogenes‐mediated transformation with the help of the Wus2‐ipt‐RUBY system, which did not need any hormone supplement for shoot regeneration and could achieve a transformation efficiency of 29.21–40.00% in less than 4 months and be used to generate knockout mutants through CRISPR/Cas9 genome editing. These findings have the potential to be widely applied in genetic transformation and genome‐editing genetic improvement of other vegetable species. [ABSTRACT FROM AUTHOR]

Offshore wind farms connected in series, with each wind turbine connected in series with one another, enhance the coupling between them. Significant differences in wind speeds between neighboring DC wind turbines (DCWTs) might result in a substantial disparity in the output voltage, hence posing a risk of overvoltage. Nevertheless, implementing voltage-limiting configurations for DCWTs might lead to the dissipation of wind energy, thereby diminishing the wind farm's capacity to deliver electricity. This work introduces a half-bridge voltage balancing circuit (HVBC) topology as a solution to the issue of DCWT output voltage changes affecting the stable operation of wind farms. The proposed HVBC topology is designed specifically for large-capacity series-connected all-DC wind farms where wind speed variations occur. This design achieves power decoupling for series-connected all-DC wind farms by providing current compensation to the series-connected DCWTs. A control strategy is devised by examining the decoupling principle and operational characteristics of the HVBC. A 60 kV/48 MW tandem-type all-DC wind farm model consisting of six DCWTs in series is built in Matlab/Simulink. The model is then simulated to evaluate its performance under conditions of unequal wind speed, rapid changes in wind speed, and wind turbine failure shutdown. This research verifies the feasibility of the HVBC topology and improves the stability of the series-type all-DC wind farm. [ABSTRACT FROM AUTHOR]

In response to the problems that abnormal yaw position causes during the yawing process—on the one hand leading to the accumulation of yaw position errors, affecting the accuracy of yawing to the wind or safety due to excessive cable twisting, and on the other hand, with the phenomena of frequent position jumps or frequent short-term position maintenance generating certain yaw errors, affecting the stability of yaw control, thus resulting in a high occurrence frequency of yaw system failures and high operation and maintenance costs—a data-driven fault diagnosis method is proposed to give early warnings for abnormal conditions of the yaw position of the wind turbine unit. Firstly, for the massive data in the SCADA (Supervisory Control and Data Acquisition) system, the ReliefF feature algorithm based on standardized interaction gain (Standardized Interaction Gain and ReliefF, SIG–ReliefF) is used for accurately identifying and screening the characteristic parameters that have a greater impact on the yaw system failure of wind turbines. The advantage of this method lies in its ability to effectively consider the correlation between features and retain the relevant features and interaction features of yaw system failures to the greatest extent. Then, an Informer yaw position prediction model is established, combined with the two-stage attention mechanism (two-stage attention and Informer, TSA–Informer), and the distribution of residuals is statistically analyzed through the sliding window method to determine the fault threshold. Finally, the validity and accuracy of the proposed method are verified through examples, and through comparison with other algorithms, it is verified that it has better abnormal early warning performance. Relevant conclusions can provide a reference for the fault diagnosis of the actual yaw system. [ABSTRACT FROM AUTHOR]