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Real-time sensing of nitric oxide (NO) in physiological environments is critically important in monitoring neurotransmission, inflammatory responses, cardiovascular systems, etc. Conventional approaches for NO detection relying on indirect colorimetric measurement or built with rigid and permanent materials cannot provide continuous monitoring and/or require additional surgical retrieval of the implants, which comes with increased risks and hospital cost. Herein, we report a flexible, biologically degradable and wirelessly operated electrochemical sensor for real-time NO detection with a low detection limit (3.97 nmol), a wide sensing range (0.01–100 μM), and desirable anti-interference characteristics. The device successfully captures NO evolution in cultured cells and organs, with results comparable to those obtained from the standard Griess assay. Incorporated with a wireless circuit, the sensor platform achieves continuous sensing of NO levels in living mammals for several days. The work may provide essential diagnostic and therapeutic information for health assessment, treatment optimization and postsurgical monitoring. Real-time continuous sensing of biological analytes is of importance in a range of biomedical applications. Here, the authors report on a flexible and physically transient sensor for the detection of nitric oxide and demonstrate applications in nitric oxide sensing in organs ex vivo and in vivo.

Peptide coacervates self‐assembling via liquid‐liquid phase separation are appealing intracellular delivery vehicles of macromolecular therapeutics (proteins, DNA, mRNA) owing to their non‐cytotoxicity, high encapsulation capacity, and efficient cellular uptake. However, the mechanisms by which these viscoelastic droplets cross the cellular membranes remain unknown. Here, using multimodal imaging, data analytics, and biochemical inhibition assays, we identify the key steps by which droplets enter the cell. We find that the uptake follows a non‐canonical pathway and instead integrates essential features of macropinocytosis and phagocytosis, namely active remodeling of the actin cytoskeleton and appearance of filopodia‐like protrusions. Experiments using giant unilamellar vesicles show that the coacervates attach to the bounding membrane in a charge‐ and cholesterol‐dependent manner but do not breach the lipid bilayer barrier. Cell uptake in the presence of small molecule inhibitors – interfering with actin and tubulin polymerization – confirm the active role of cytoskeleton remodeling, most prominently evident in electron microscopy imaging. These findings suggest a peculiar internalization mechanism for viscoelastic, glassy coacervate droplets combining features of non‐specific uptake of fluids by macropinocytosis and particulate uptake of phagocytosis. The broad implications of this study will enable to enhance the efficacy and utility of coacervate‐based strategies for intracellular delivery of macromolecular therapeutics. Phase‐separating peptide coacervates are promising carriers to deliver large macromolecular therapeutic inside cells, including proteins, genes, mRNA and gene editing machineries, with promising perspective for a broad range of therapeutic treatments. In this study, viscoelastic coacervate microdroplets are shown to be uptaken by cells by a non‐canonical pathway integrating features of macropinocytosis and phagocytosis.

Peptide Coacervates for Intracellular Therapeutic Delivery Peptide coacervate microdroplets (CMs) are promising vectors to deliver a broad range of macromolecular therapeutics inside cells, but the mechanism by which CMs enter cells has remained unknown to date. In article number 2402652, Ali Miserez and co‐workers demonstrate that CMs enter the cell through a mechanism reminiscent of macropinocytosis and phagocytosis. The cover image is a scanning electron microscope (SEM) image of CMs captured by filipodia protrusions of HeLa cells, which is the first step in the internalization process of CMs within cells. The image was false‐colored for visualization purposes.

The effect of glucose as a signaling molecule on induction of aliphatic glucosinolate biosynthesis was reported in our former study. Here, we further investigated the regulatory mechanism of indolic glucosinolate biosynthesis by glucose in Arabidopsis . Glucose exerted a positive influence on indolic glucosinolate biosynthesis, which was demonstrated by induced accumulation of indolic glucosinolates and enhanced expression of related genes upon glucose treatment. Genetic analysis revealed that MYB34 and MYB51 were crucial in maintaining the basal indolic glucosinolate accumulation, with MYB34 being pivotal in response to glucose signaling. The increased accumulation of indolic glucosinolates and mRNA levels of MYB34 , MYB51 and MYB122 caused by glucose were inhibited in the gin2-1 mutant, suggesting an important role of HXK1 in glucose-mediated induction of indolic glucosinolate biosynthesis. In contrast to what was known on the function of ABI5 in glucose-mediated aliphatic glucosinolate biosynthesis, ABI5 was not required for glucose-induced indolic glucosinolate accumulation. In addition, our results also indicated that glucose-induced glucosinolate accumulation was due to enhanced sulfur assimilation instead of directed sulfur partitioning into glucosinolate biosynthesis. Thus, our data provide new insights into molecular mechanisms underlying glucose-regulated glucosinolate biosynthesis.

In order to enable urban economic development, the use of the right value and asset value of rural collective construction land (RCCL) is increasingly becoming apparent and this market is experiencing rapid development. However, the arrangement of the governance structure of rural shareholding cooperatives (RSCs) can seriously affect the efficiency of collective construction land market transactions, since the governance of RSCs is related to the interests of farmers. Protecting the rights and interests of farmers while improving the governance efficiency of RSCs is a considerable challenge worldwide. To better deal with this challenge, this study used a field survey in Nanhai District, Guangdong Province, China, to estimate how the governance structure of RSCs affect the efficiency of RCCL market transactions. Tobit models were constructed, and the results show that (1) most of the governance functions of RSCs were not separate from the administrative management of the village committees, which leads to low efficiency of RSCs’ governance; (2) leaders of rural collective economic organizations played a key role in governance efficiency; (3) from the perspective of collective land property rights, most village shareholders did not have decision-making power or supervisory authority in the RCCL transfers because they could not complete access to transaction information. Furthermore, most villagers felt that the amount of income distributed was unreasonable, and the rights and interests of farmers and village shareholders were not guaranteed by the RSCs. Therefore, we suggest that the Chinese authorities should strengthen their current efforts to construct a more open and fair governance structure of the RSCs and thus improve their market transaction efficiency. Our work provides some insights into ways to improve the governance structure and market transaction efficiency of RSCs, which can further contribute to the development of the RCCL market in other areas of China and worldwide.

Graph Neural Networks (GNNs) have been broadly applied in many urban applications upon formulating a city as an urban graph whose nodes are urban objects like regions or points of interest. Recently, a few enhanced GNN architectures have been developed to tackle heterophily graphs where connected nodes are dissimilar. However, urban graphs usually can be observed to possess a unique spatial heterophily property; that is, the dissimilarity of neighbors at different spatial distances can exhibit great diversity. This property has not been explored, while it often exists. To this end, in this paper, we propose a metric, named Spatial Diversity Score, to quantitatively measure the spatial heterophily and show how it can influence the performance of GNNs. Indeed, our experimental investigation clearly shows that existing heterophilic GNNs are still deficient in handling the urban graph with high spatial diversity score. This, in turn, may degrade their effectiveness in urban applications. Along this line, we propose a Spatial Heterophily Aware Graph Neural Network (SHGNN), to tackle the spatial diversity of heterophily of urban graphs. Based on the key observation that spatially close neighbors on the urban graph present a more similar mode of difference to the central node, we first design a rotation-scaling spatial aggregation module, whose core idea is to properly group the spatially close neighbors and separately process each group with less diversity inside. Then, a heterophily-sensitive spatial interaction module is designed to adaptively capture the commonality and diverse dissimilarity in different spatial groups. Extensive experiments on three real-world urban datasets demonstrate the superiority of our SHGNN over several its competitors.

Urban villages (UVs) refer to the underdeveloped informal settlement falling behind the rapid urbanization in a city. Since there are high levels of social inequality and social risks in these UVs, it is critical for city managers to discover all UVs for making appropriate renovation policies. Existing approaches to detecting UVs are labor-intensive or have not fully addressed the unique challenges in UV detection such as the scarcity of labeled UVs and the diverse urban patterns in different regions. To this end, we first build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area. Finally, we conduct extensive experiments in three cities to demonstrate the effectiveness of our approach.

The novel coronavirus disease (COVID-19) has crushed daily routines and is still rampaging through the world. Existing solution for nonpharmaceutical interventions usually needs to timely and precisely select a subset of residential urban areas for containment or even quarantine, where the spatial distribution of confirmed cases has been considered as a key criterion for the subset selection. While such containment measure has successfully stopped or slowed down the spread of COVID-19 in some countries, it is criticized for being inefficient or ineffective, as the statistics of confirmed cases are usually time-delayed and coarse-grained. To tackle the issues, we propose C-Watcher, a novel data-driven framework that aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. In terms of design, C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn \"city-invariant\" representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks, where the results demonstrate the efficiency and effectiveness of C-Watcher for early detection of high-risk neighborhoods from a large number of cities.