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The hydrological monitoring of flow data is important for flood prevention and modern river management. However, traditional contact methods are increasingly struggling to meet the requirements of simplicity, accuracy, and continuity. The video-based river discharge measurement is a technique to monitor flow velocity without contacting the water body by using the image-recognition algorithms, which has been verified to have the advantages of full coverage and full automation compared with the traditional contact technique. In order to provide a timely summary of the available results and to inform further research and applications, this paper reviews and synthesizes the literature on the general implementation routes of the video-based river discharge measurement technique and the principles and advances of today’s popular image-recognition algorithms for velocity detection. Then, it discusses the challenges of image-recognition algorithms in terms of image acquisition conditions, parameter uncertainties, and complex meteorological and water environments. It is concluded that the performance of this technique can be improved by enhancing the robustness and accuracy of video-based discharge measurement algorithms, minimizing weather effects, and improving computational efficiency. Finally, future development directions for further perfecting this technique are outlined.

Understanding the evolution of river morphology is crucial for comprehending changes in water resources and implementing development projects along rivers. This study proposes an integrated approach utilizing remote sensing image data combined with deep learning and visual interpretation algorithms to analyze continuous-type changes in river morphology. This research focuses on the lower reaches of the Minjiang River in China and comprehensively analyzes the river’s morphological evolution from 1986 to 2021. The results show that the proposed method of river water identification in this study demonstrates high accuracy and effectiveness, with an F1 score and Kappa coefficient greater than 0.96 and 0.91, respectively. The morphology of the river channel remains stable in the upstream and estuarine sections of the study region while undergoing substantial alterations in the middle section. Additionally, this study also identifies several factors that significantly impact the evolution of river morphology, including reservoir construction, river sediment mining, river training measures, geological conditions, and large flood events. The findings of this study can provide some insights into the management and conservation of water resources.

To improve the accuracy and reliability of precipitation estimation, numerous models based on machine learning technology have been developed for integrating data from multiple sources. However, little attention has been paid to extracting the spatiotemporal correlation patterns between satellite products and rain gauge observations during the merging process. This paper focuses on this issue by proposing an integrated framework to generate an accurate and reliable spatiotemporal estimation of precipitation. The proposed framework integrates Funk-Singular Value Decomposition (F-SVD) in the recommender system to achieve the accurate spatial distribution of precipitation based on the spatiotemporal interpolation of rain gauge observations and Convolutional Long Short-Term Memory (ConvLSTM) to merge precipitation data from interpolation results and satellite observation through exploiting the spatiotemporal correlation pattern between them. The framework (FS-ConvLSTM) is utilized to obtain hourly precipitation merging data with a resolution of 0.1° in Jianxi Basin, southeast of China, from both rain gauge data and Global Precipitation Measurement (GPM) from 2006 to 2018. The LSTM and Inverse Distance Weighting (IDW) are constructed for comparison purposes. The results demonstrate that the framework could not only provide more accurate precipitation distribution but also achieve better stability and reliability. Compared with other models, it performs better in variation process description and rainfall capture capability, and the root mean square error (RSME) and probability of detection (POD) are improved by 63.6% and 22.9% from the original GPM, respectively. In addition, the merged precipitation combines the strength of different data while mitigating their weaknesses and has good agreement with observed precipitation in terms of magnitude and spatial distribution. Consequently, the proposed framework provides a valuable tool to improve the accuracy of precipitation estimation, which can have important implications for water resource management and natural disaster preparedness.

Bioinspired molecular engineering strategies have emerged as powerful tools that significantly enhance the development of novel therapeutics, improving efficacy, specificity, and safety in disease treatment. Recent advancements have focused on identifying and utilizing disease‐associated biomarkers to optimize drug activity and address challenges inherent in traditional therapeutics, such as frequent drug administrations, poor patient adherence, and increased risk of adverse effects. In this review, we provide a comprehensive overview of the latest developments in bioinspired artificial systems (BAS) that use molecular engineering to tailor therapeutic responses to drugs in the presence of disease‐specific biomarkers. We examine the transition from open‐loop systems, which rely on external cues, to closed‐loop feedback systems capable of autonomous self‐regulation in response to disease‐associated biomarkers. We detail various BAS modalities designed to achieve biomarker‐driven therapy, including activatable prodrug molecules, smart drug delivery platforms, autonomous artificial cells, and synthetic receptor‐based cell therapies, elucidating their operational principles and practical in vivo applications. Finally, we discuss the current challenges and future perspectives in the advancement of BAS‐enabled technology and envision that ongoing advancements toward more programmable and customizable BAS‐based therapeutics will significantly enhance precision medicine. [ABSTRACT FROM AUTHOR]

Proteins capable of switching between distinct active states in response to biochemical cues are ideal for sensing and controlling biological processes. Activatable CRISPR‐Cas systems are significant in precise genetic manipulation and sensitive molecular diagnostics, yet directly controlling Cas protein function remains challenging. Herein, we explore anti‐CRISPR (Acr) proteins as modules to create synthetic Cas protein switches (CasPSs) based on computational chemistry‐directed rational protein interface engineering. Guided by molecular fingerprint analysis, electrostatic potential mapping, and binding free energy calculations, we rationally engineer the molecular interaction interface between Cas12a and its cognate Acr proteins (AcrVA4 and AcrVA5) to generate a series of orthogonal protease‐responsive CasPSs. These CasPSs enable the conversion of specific proteolytic events into activation of Cas12a function with high switching ratios (up to 34.3‐fold). These advancements enable specific proteolysis‐inducible genome editing in mammalian cells and sensitive detection of viral protease activities during virus infection. This work provides a promising strategy for developing CRISPR‐Cas tools for controllable gene manipulation and regulation and clinical diagnostics. [ABSTRACT FROM AUTHOR]

Flood forecasting is one of the most significant tools for reducing flood risk and avoiding the loss of life To solve the problem of low resolution and the short lead time of the traditional urban flood forecasting method, this work develops a novel high-accuracy and long lead time model through coupling the atmospheric and hydrodynamic models. The GRAPE_MESO model is applied as an atmospheric model for predicting rainstorms. To improve reliability, a reconstructed method is put forward to correct predicted rainstorm data. The reconstructed predicted rainstorm is then used as input data for the hydrodynamic flood model. Finally, the urban flood inundation process was forecasted by the coupled atmospheric and flood model. Though applying the coupled model at Fengxi New Town (China), the performance is evaluated for realistic urban flood forecasting. The results show that the coupled modeling system can predict the urban flood inundation process with high-resolution and a long lead time.

Previous studies regarding social interactions commonly adopt research methods that investigate causal relationships between variables. The existing approaches often utilize variables derived from general contexts, aiming to apply them universally across diverse situations. However, social interactions, including the usage of social media, are intricately woven within the immediate social context. The interpretability of these generalized variables has been attenuated by the dynamic and transient nature of social contexts; these variables have diverse impacts on social interactions. Consequently, researchers have been diligently seeking new variables relevant to specific social contexts in order to complement the existing generalized ones. However, the ever-changing nature of social contexts poses a challenge, impeding researchers from exhaustively defining all variables that influence social interactions. To address this complexity, this study proposes social network analysis as a suitable research method capable of capturing the ever-evolving dynamics of social interactions, including social media usage. Furthermore, this study puts forth hypotheses that specifically explore the role of individual social networks in social media research, with the aim of stimulating future investigations that center on the interactive and dynamic nature of social media usage. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) have emerged as promising diagnostic biomarkers and therapeutic targets in various diseases. However, there is currently a lack of molecular strategies that can effectively use disease‐associated extracellular miRNAs as input signals to drive therapeutic functions. Herein, we present a modular and programmable miRNA‐responsive chimeric DNA receptor (miRNA‐CDR) capable of biomarker‐driven therapy. By grafting a miRNA‐responsive DNA nanodevice on a natural membrane receptor via aptamer anchoring, miRNA‐CDR can sense extracellular miRNA levels and autonomously induce dimerization‐mediated receptor activation via the complementary‐mediated strand displacement reaction‐induced dynamic DNA assembly. The sequence programmability of miRNA‐CDR allows it to sense and respond to a user‐defined miRNA with tunable sensitivity. Moreover, the miRNA‐CDR is versatile and customizable to reprogram desirable signaling output via adapting a designated receptor, such as MET and FGFR1. Using a mouse model of drug‐induced acute liver injury (DILI), we demonstrate the functionality of a designer miRNA‐CDR in rewiring the recognition of the DILI‐elevated miR‐122 to promote MET signaling of hepatocytes for biomarker‐driven in situ repair and liver function restoration. Our synthetic miRNA‐CDR platform provides a novel molecular device enabling biomarker‐driven therapeutic cellular response, potentially paving the way for improving the precision of cell therapy in regenerative medicine. [ABSTRACT FROM AUTHOR]