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The specific induction of hepatic differentiation presents a significant challenge in developing alternative liver cell sources and viable strategies for clinical therapy of acute liver failure (ALF). The past decade has witnessed the blossom of microRNAs in regenerative medicine. Herein, microRNA 122-functionalized tetrahedral framework nucleic acid (FNA-miR-122) has emerged as an unprecedented and potential platform for directing the hepatic differentiation of adipose-derived mesenchymal stem cells (ADMSCs), which offers a straightforward and cost-effective method for generating functional hepatocyte-like cells (FNA-miR-122-iHep). Additionally, we have successfully established a liver organoid synthesis strategy by optimizing the co-culture of FNA-miR-122-iHep with endothelial cells (HUVECs), resulting in functional Hep:HUE-liver spheroids. Transcriptome analysis not only uncovered the potential molecular mechanisms through which miR-122 influences hepatic differentiation in ADMSCs, but also clarified that Hep:HUE-liver spheroids could further facilitate hepatocyte maturation and improved tissue-specific functions, which may provide new hints to be used to develop a hepatic organoid platform. Notably, compared to transplanted ADMSCs and Hep-liver spheroid, respectively, both FNA-miR-122-iHep-based single cell therapy and Hep:HUE-liver spheroid-based therapy showed high efficacy in treating ALF in vivo. Collectively, this research establishes a robust system using microRNA to induce ADMSCs into functional hepatocyte-like cells and to generate hepatic organoids in vitro, promising a highly efficient therapeutic approach for ALF.

Significant advancements in semantic parsing for text-to-SQL (T2S) tasks have been achieved through the employment of neural network models, such as LSTM, BERT, and T5. The exceptional performance of large language models, such as ChatGPT, has been demonstrated in recent research, even in zero-shot scenarios. However, the inherent transparency of T2S models presents them as black boxes, concealing their inner workings from both developers and users, which complicates the diagnosis of potential error patterns. Despite the fact that numerous visual analysis studies have been conducted in natural language processing communities, scant attention has been paid to addressing the challenges of semantic parsing, specifically in T2S tasks. This limitation hinders the development of effective tools for model optimization and evaluation. This article presents an interactive visual analysis tool, MAVIDSQL, to assist model developers and users in understanding and diagnosing T2S tasks. The system comprises three modules: the model manager, the feature extractor, and the visualization interface, which adopt a model-agnostic approach to diagnose potential errors and infer model decisions by analyzing input–output data, facilitating interactive visual analysis to identify error patterns and assess model performance. Two case studies and interviews with domain experts demonstrate the effectiveness of MAVIDSQL in facilitating the understanding of T2S tasks and identifying potential errors.

Chloroplasts regulate plant development and immunity. Here we report that potato chloroplast elongation factors StTuA and StTuB, targeted by Phytophthora infestansRXLR effector Pi22926, positively regulate immunity and growth. Plants expressing Pi22926, or silenced for TuA/B, show increased P. infestanssusceptibility and decreased photosynthesis, plant growth and tuber yield. By contrast, StTuA/Boverexpression reduces susceptibility, elevates chloroplast-derived reactive oxygen species production and increases photosynthesis and potato tuber yield by enhancing chloroplast protein translation. Another plant target of Pi22926, StMAP3Kβ2, interacts with StTuB, phosphorylating it to promote its translocation into chloroplasts. However, Pi22926 attenuates StTuB association with StMAP3Kβ2 and phosphorylation. This reduces StTuB translocation into chloroplasts, leading to its proteasome-mediated turnover in the cytoplasm. We uncover new mechanisms by which a pathogen effector inhibits immunity by disrupting key chloroplast functions. This work shows that StTuA/B break the growth–immunity trade-off, promoting both disease resistance and yield, revealing the enormous potential of chloroplast biology in crop breeding.

Graphical abstract: Injectable composite comprising QX-314-loaded electrospun fiber and capsaicin-loaded thermosensitive hydrogel sequentially releasing drugs for prolonged and nociceptive-selective local analgesia.

Enhancing the capabilities of unmanned space exploration, such as satellite monitoring and space science missions, requires efficient and reliable nuclear power systems. A viable solution is found in the 1–10 kWe power level of space nuclear reactor power systems, offering advantages such as a manageable research and development process, and relatively low investment requirements. This paper introduces a conceptual design for a 5 kWe space nuclear reactor power system, outlining its components and characteristics. The study includes a thorough analysis of potential challenges, encompassing heat pipe failure accidents, re-entry scenarios, and weight estimation considerations. The results demonstrate that the proposed space nuclear reactor power system effectively meets the safety requirements. The total mass of the power system is estimated at approximately 1.5 tons, with a specific mass of around 300 kg/kWe. This research contributes valuable insights for the design of space nuclear reactor power systems operating within a similar power range.

Vision algorithms face challenges of limited visual presentation and unreliability in pedestrian activity assessment. In this article, we introduce LANDER, an interactive analysis system for visual exploration of pedestrian activity and uncertainty in surveillance videos. This visual analytics system focuses on three common categories of uncertainties in object tracking and action recognition. LANDER offers an overview visualization of activity and uncertainty, along with spatio-temporal exploration views closely associated with the scene. Expert evaluation and user study indicate that LANDER outperforms traditional video exploration in data presentation and analysis workflow. Specifically, compared to the baseline method, it excels in reducing retrieval time ($p< $ 0.01), enhancing uncertainty identification ($p< $ 0.05), and improving the user experience ($p< $ 0.05).

Modern industry and agriculture have exacerbated soil heavy metal pollution, with cadmium (Cd) being one of the most common, toxic, and widely distributed pollutants. Our aim is to evaluate the environmental impacts of Cd on the afforestation species and zonal forest soil of northeastern China. Changbai larch (Larix olgensisA. Henry) seedlings receiving 0 (Ck), 1, 5, 25, 50, 75, or 100 mg L− 1Cd treatment were incubated for 30, 40, or 50 days. Then, we investigated the growth and physiological characteristics of the seedlings, chemical properties of Haplic Cambisols under these seedlings and calculated the toxicity threshold. Partial Cd treatment significantly improved the relative conductivity, malondialdehyde, superoxide anion and hydrogen peroxide of seedlings and reduced leaf, stem and root dry mass at 30 days and root growth. Cd treatment reduced the soil pH value to a certain extent, generally improved the effectiveness of nitrogen, phosphorus and potassium nutrients, and significantly increased the content of available Cd. The EC10values of the aboveground parts were higher than those of the roots, and the EC10values of both reached a maximum at 50 days, reaching 2.07 and 1.15 mg L− 1, respectively. Partial Cd treatment had a significant impact on the growth physiology of seedlings and chemical properties of soil and varied with its concentration. Although the exact mechanism of the Cd treatment response is difficult to infer, this study clearly estimates the toxicity threshold of Cd-contaminated soil when the biomass of the plant is reduced by 10%.

Recently, some progress has been made in anomaly detection for data streams. However, these methods still exhibit deficiencies in effectively balancing computational efficiency and detection accuracy. In this paper, we propose a novel anomaly detection method called Dynamic Window Anomaly Detection based on Local Density of Vector dot Product (DWAD-LDVP) for data streams. DWAD-LDVP uses an adaptive dynamic sliding window mechanism with a verification model to balance the relationship between computational efficiency and detection accuracy. By converting local density values measured by the vector dot product into outlier scores, DWAD-LDVP can improve the accuracy of anomaly detection compared to SOTA methods. Furthermore, an incremental computation module is adopted to process data streams efficiently, thereby reducing unnecessary computational overhead. Simulation experiments on synthetic data and real-world datasets validate the robustness and superior performance of the proposed DWAD-LDVP method. Specifically, the experimental results indicate that the proposed DWAD-LDVP achieves competitive performance measured by accuracy, precision, recall, F1 score, and ROC AUC, confirming its effectiveness in anomaly detection tasks.

Depth estimation plays a pivotal role in various applications, including autonomous driving and robot navigation. In contrast to depth estimation using multiple images, such as stereo depth perception, inferring depth relations from a monocular camera is notably more challenging yet highly valuable. Traditionally, convolutional neural networks (CNNs) with residual structures have been extensively employed for this task, but they inherently constrain the model’s feature extraction capabilities. Inspired by HorNet, in this article, we propose a novel self-supervised monocular depth estimation framework based on high-order spatial interactions, referred to as the Hor-Depth. Furthermore, the Hor-Depth improves feature fusion efficiency in the depth network decoder by incorporating the attentional feature fusion (AFF) module based on first-order spatial interaction, leading to more refined predicted disparity maps. To address issues of loss fluctuations and training instability, we introduce a progressive scale-weight adjustment strategy-based loss function. This strategy applies varying constraints to the model at different training stages, effectively reducing training fluctuations and mitigating outliers that significantly deviate from the predicted values. The proposed approach demonstrates exceptional performance in self-supervised monocular depth estimation, surpassing certain stereo supervised or monocular supervised methods, as evidenced by its impressive results on the KITTI Eigen split benchmark.

External fingerprints (EFs) based only on epidermal information are vulnerable to spoofing attacks and non-ideal skin conditions. To solve such shortcomings, internal fingerprints (IFs) collected using optical coherence tomography (OCT) have been proposed and widely researched. However, the development of IF is limited by the lack of in-depth researches on the IF and the EF-IF interoperability, which is partially caused by the lack of public OCT database. The obvious gap in the applications of EF and IF recognition motivated us to design and publish a comprehensive fingerprint database containing both traditional EFs and OCT IFs, denoted as ZJUT-EIFD. To the best of our knowledge, ZJUT-EIFD is the first public database that combines OCT and total internal reflection (TIR) via synchronous acquisition, with 399 different fingers from 60 subjects. In this article, the composition of the database, the quality of EFs and IFs, and the verification performance of different types of fingerprints were detailed. In addition, potential application directions of ZJUT-EIFD were demonstrated. ZJUT-EIFD can serve benchmarks and interoperability tests for EF-IF research, which will promote the research and development of EF and IF.

The structural upgrading of the sports industry is the only way to achieve high-quality development of the sports industry. By constructing a theoretical analysis framework of "Technology Organization Market" (TOE), and using the dynamic QCA method to analyze panel data of 22 provinces in China from 2016 to 2021, the study explored the effect of each dimension on the structural upgrading of local sports industry in China, and examines the differences in the interpretation of each group state across different regions. Individual factors do not constitute a necessary condition for the structural upgrading of the local sports industry; There are four pathways that can drive the structural upgrading of the local sports industry, which are summarized as the "policy-technology" driven model, the "government-market" driven model and the "government-market-technology" linkage model; In the time dimension, the explanatory power of three pathways for the structural upgrading of the local sports industry has been declining since 2017; In the spatial dimension, the explanatory power of one pathway shows significant geographical differences. Based on this, we put forward the development strategies for upgrading the structure of local sports industry: improve the effectiveness of industrial policy implementation, increase the investment in scientific research; give full play to the government's economic means, focusing on the empowerment of digital technology; improve the mode of industry-industry integration and cooperation. deepening the development of digital innovation linkage; improve the degree of coordination and suitability of the "resources-factors-policy", and strengthen the advantages of the regional allocation of resources. [ABSTRACT FROM AUTHOR]

Acute liver failure (ALF) is a critical life-threatening disease that occurs due to a rapid loss in hepatocyte functions. Hepatocyte transplantation holds great potential for ALF treatment, as it rapidly supports liver biofunctions and enhances liver regeneration. However, hepatocyte transplantation is still limited by renewable and ongoing cell sources. In addition, intravenously injected hepatocytes are primarily trapped in the lungs and have limited efficacy because of the rapid clearance in vivo. Here, we designed a Y-shaped DNA nanostructure to deliver microRNA-122 (Y-miR122), which could induce the hepatic differentiation and maturation of human mesenchymal stem cells. mRNA sequencing analysis revealed that the Y-miR122 promoted important hepatic biofunctions of the induced hepatocyte-like cells including fat and lipid metabolism, drug metabolism, and liver development. To further improve hepatocyte transplantation efficiency and therapeutic effects in ALF treatment, we fabricated protective microgels for the delivery of Y-miR122-induced hepatocyte-like cells based on droplet microfluidic technology. When cocultured with human umbilical vein endothelial cells in microgels, the hepatocyte-like cells exhibited an increase in hepatocyte-associated functions, including albumin secretion and cytochrome P450 activity. Notably, upon transplantation into the ALF mouse model, the multiple cell-laden microgels effectively induced the restoration of liver function and enhanced liver regeneration. Overall, this study presents an efficient approach from the generation of hepatocyte-like cells to hepatocyte transplantation in ALF therapy.