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Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS images. Based on these observations, this paper proposes a Mask2Former with an improved query (IQ2Former) for this task. The fundamental motivation behind the IQ2Former is to enhance the capability of the query of Mask2Former by exploiting the characteristics of RS images well. First, we propose the Query Scenario Module (QSM), which aims to learn and group the queries from feature maps, allowing the selection of distinct scenarios such as the urban and rural areas, building clusters, and parking lots. Second, we design the query position module (QPM), which is developed to assign the image position information to each query without increasing the number of parameters, thereby enhancing the model’s sensitivity to small targets in complex scenarios. Finally, we propose the query attention module (QAM), which is constructed to leverage the characteristics of query attention to extract valuable features from the preceding queries. Being positioned between the duplicated transformer decoder layers, QAM ensures the comprehensive utilization of the supervisory information and the exploitation of those fine-grained details. Architecturally, the QSM, QPM, and QAM as well as an end-to-end model are assembled to achieve high-quality semantic segmentation. In comparison to the classical or state-of-the-art models (FCN, PSPNet, DeepLabV3+, OCRNet, UPerNet, MaskFormer, Mask2Former), IQ2Former has demonstrated exceptional performance across three publicly challenging remote-sensing image datasets, 83.59 mIoU on the Vaihingen dataset, 87.89 mIoU on Potsdam dataset, and 56.31 mIoU on LoveDA dataset. Additionally, overall accuracy, ablation experiment, and visualization segmentation results all indicate IQ2Former validity.

Semantic segmentation of remote-sensing (RS) images is one of the most fundamental tasks in the understanding of a remote-sensing scene. However, high-resolution RS images contain plentiful detailed information about ground objects, which scatter everywhere spatially and have variable sizes, styles, and visual appearances. Due to the high similarity between classes and diversity within classes, it is challenging to obtain satisfactory and accurate semantic segmentation results. This paper proposes a Dynamic High-Resolution Network (DyHRNet) to solve this problem. Our proposed network takes HRNet as a super-architecture, aiming to leverage the important connections and channels by further investigating the parallel streams at different resolution representations of the original HRNet. The learning task is conducted under the framework of a neural architecture search (NAS) and channel-wise attention module. Specifically, the Accelerated Proximal Gradient (APG) algorithm is introduced to iteratively solve the sparse regularization subproblem from the perspective of neural architecture search. In this way, valuable connections are selected for cross-resolution feature fusion. In addition, a channel-wise attention module is designed to weight the channel contributions for feature aggregation. Finally, DyHRNet fully realizes the dynamic advantages of data adaptability by combining the APG algorithm and channel-wise attention module simultaneously. Compared with nine classical or state-of-the-art models (FCN, UNet, PSPNet, DeepLabV3+, OCRNet, SETR, SegFormer, HRNet+FCN, and HRNet+OCR), DyHRNet has shown high performance on three public challenging RS image datasets (Vaihingen, Potsdam, and LoveDA). Furthermore, the visual segmentation results, the learned structures, the iteration process analysis, and the ablation study all demonstrate the effectiveness of our proposed model.

Semantic segmentation in high-resolution remote-sensing (RS) images is a fundamental task for RS-based urban understanding and planning. However, various types of artificial objects in urban areas make this task quite challenging. Recently, the use of Deep Convolutional Neural Networks (DCNNs) with multiscale information fusion has demonstrated great potential in enhancing performance. Technically, however, existing fusions are usually implemented by summing or concatenating feature maps in a straightforward way. Seldom do works consider the spatial importance for global-to-local context-information aggregation. This paper proposes a Learnable-Gated CNN (L-GCNN) to address this issue. Methodologically, the Taylor expression of the information-entropy function is first parameterized to design the gate function, which is employed to generate pixelwise weights for coarse-to-fine refinement in the L-GCNN. Accordingly, a Parameterized Gate Module (PGM) was designed to achieve this goal. Then, the single PGM and its densely connected extension were embedded into different levels of the encoder in the L-GCNN to help identify the discriminative feature maps at different scales. With the above designs, the L-GCNN is finally organized as a self-cascaded end-to-end architecture that is able to sequentially aggregate context information for fine segmentation. The proposed model was evaluated on two public challenging benchmarks, the ISPRS 2Dsemantic segmentation challenge Potsdam dataset and the Massachusetts building dataset. The experiment results demonstrate that the proposed method exhibited significant improvement compared with several related segmentation networks, including the FCN, SegNet, RefineNet, PSPNet, DeepLab and GSN.For example, on the Potsdam dataset, our method achieved a 93.65% F 1 score and 88.06% I o U score for the segmentation of tiny cars in high-resolution RS images. As a conclusion, the proposed model showed potential for object segmentation from the RS images of buildings, impervious surfaces, low vegetation, trees and cars in urban settings, which largely varies in size and have confusing appearances.

The manipulation of microstructures within modern micro- and nanomaterials stands as a prevalent practice with extensive applications across diverse fields. The deliberate control of material microstructures empowers the fine-tuning of their distinctive physical and chemical properties, catering to specific requirements in various applications. This dissertation mainly explores the strategic utilization of materials endowed with controlled microstructures, particularly investigating their significance and applications in the field of electrochemistry and obscurants.Finding ways to reduce reactor volume while increasing product output for electroorganic reactions would facilitate the broader adoption of such reactions for the production of chemicals in a commercial setting. The goal of the electrochemistry research is to investigate how the use of flow with different electrode structures impacts the productivity (i.e., the rate of product generation) of a TEMPO-mediated azidooxygenation reaction. Comparison of a flow and batch process with carbon paper (CP) demonstrated a 3.8-fold higher productivity for the flow reactor. Three custom carbon electrodes, sintered carbon paper (S-CP), carbon nanofiber (CNF), and composite carbon microfiber-nanofiber (MNC), were studied in the flow reactor to evaluate how changing the electrode structure affected productivity. Under the optimum conditions these electrodes achieved productivities 5.4, 6.5 and 7.8 times higher than the average batch reactor, respectively. Recycling the outlet from the flow reactor with the MNC electrode back into the inlet achieved an 81% yield in 36 minutes, while the batch reactor obtained a 75% yield in 5 hours. These findings demonstrate that the productivity of electroorganic reactions can be substantially improved through the use of novel flow-through electrodes. Further exploration on other type of electroorganic reaction with 3-D porous electrode, like electrochemical cross-electrophile coupling (XEC), got an extensively lower yield in the flow cell with different configurations, which was due to the pass of chemicals through membrane in divided cell and low residence time in undivided cell. Due to the time and funding limited, we did not dig deeper into this project.The ultimate goal of the obscurants work is to create an engineered aerosol that acts as one-way smoke, i.e., it creates an asymmetric vision environment in which the ability to image objects depends on the viewing direction. To this end I developed a rapid, one-pot synthesis of copper-based microclubs that consist of a Cu2O octahedron attached to a Cu2O@Cu shaft. Millions of synthesized particles were analyzed in minutes with a FlowCam to provide a robust statistical analysis of their geometry, and rapidly elucidate the roles of the reaction constituents on the particle shape and yield. By utilizing Bayesian Optimization, the parameter space of the reaction conditions was fully explored, reducing the mean square error (MSE) between predicted and actual yield by 125 times after 14 iterations and achieving 64% yield of microclub production in 20 mL scale. With the slight modification on the optimized conditions, 67% yield was achieved under 2 L scale synthesis of microclub. The combination of asymmetry in both shape and composition introduces a 30% difference in scattering of light propagating parallel to the microclub axis from opposing directions. This work represents a first step toward the creation of an asymmetric imaging environment with an aerosol consisting of acoustically aligned microclubs.

Bacterial infections, especially those caused by drug-resistant bacteria, have seriously threatened human life and health. There is urgent to develop new antibacterial agents to reduce the problem of antibiotics. Biomedical materials with good antimicrobial properties have been widely used in antibacterial applications. Among them, hydrogels have become the focus of research in the field of biomedical materials due to their unique three-dimensional network structure, high hydrophilicity, and good biocompatibility. In this review, the latest research progresses about hydrogels in recent years were summarized, mainly including the preparation methods of hydrogels and their antibacterial applications. According to their different antibacterial mechanisms, several representative antibacterial hydrogels were introduced, such as antibiotics loaded hydrogels, antibiotic-free hydrogels including metal-based hydrogels, antibacterial peptide and antibacterial polymers, stimuli-responsive smart hydrogels, and light-mediated hydrogels. In addition, we also discussed the applications and challenges of antibacterial hydrogels in biomedicine, which are expected to provide new directions and ideas for the application of hydrogels in clinical antibacterial therapy.

Optineurin is an important autophagy receptor involved in several selective autophagy processes, during which its function is regulated by TBK1. Mutations of optineurin and TBK1 are both associated with neurodegenerative diseases. However, the mechanistic basis underlying the specific interaction between optineurin and TBK1 is still elusive. Here we determine the crystal structures of optineurin/TBK1 complex and the related NAP1/TBK1 complex, uncovering the detailed molecular mechanism governing the optineurin and TBK1 interaction, and revealing a general binding mode between TBK1 and its associated adaptor proteins. In addition, we demonstrate that the glaucoma-associated optineurin E50K mutation not only enhances the interaction between optineurin and TBK1 but also alters the oligomeric state of optineurin, and the ALS-related TBK1 E696K mutation specifically disrupts the optineurin/TBK1 complex formation but has little effect on the NAP1/TBK1 complex. Thus, our study provides mechanistic insights into those currently known disease-causing optineurin and TBK1 mutations found in patients. Mutations in optineurin that cause defects in the interaction with TBK1 are associated with neurodegenerative diseases. Here, the authors report the structure of this complex, and outline a general binding mode for these proteins.

Improvement of cold tolerance at the booting stage (CTB) in rice is a key strategy for cultivation in high-altitude and high-latitude regions. Here, we identify CTB3 gene, encoding a calmodulin-binding transcriptional activator that positively regulates cold tolerance at the booting stage in japonica rice. Two indels (57-bp and 284-bp) in the CTB3 promoter confer a differential transcriptional response to cold between the japonica and indica subspecies. OsTCP19 suppresses CTB3 expression by binding to these indels, negatively regulating cold tolerance. CTB3 activates the expression of TREHALOSE-6-PHOSPHATE PHOSPHATASE1 ( OsTPP1 ), reducing trehalose 6-phosphate (Tre6P) levels, which increases sugar accumulation in panicles and improves cold tolerance. Additionally, favorable alleles of OsTCP19 and CTB3 are selected in japonica rice for cold adaptation. These findings highlight the important role of CTB3 in cold adaptation and its potential for improving cold tolerance in rice breeding. Cold tolerance at the booting stage is critical for the expansion of rice cultivation area. Here, the authors report that a CAMTA family transcription factor encoding gene CTB3 positively regulates japonica rice booting stage cold tolerance and its upstream and downstream interactors to fulfill the functionality.

With the increasing frequency of extreme rainfall events due to climate change, enhancing flood resilience has become a critical focus for urban researchers and planners. However, existing studies rarely evaluate the relationship between flood resilience and damage at fine geospatial scales, limiting the development of site-specific enhancement strategies. This study employs a baseline-adaptive resilience framework that integrates machine learning techniques with multidimensional social media data analytics. The methodology implements a three-phase analytical approach—stepwise clustering analysis model, hierarchical partitioning analysis, and coupling coordination degree analysis—to identify the resilience determinants influencing both physical and psychological flood damage. This framework enables assessment of coordination between resilience dimensions and empirical disaster impacts at high spatial resolution. The analysis highlights the dominant role of fire station accessibility in enhancing resilience, alongside other critical factors such as medical facility accessibility, road network structure, population density, and public service provision. Our findings reveal significant spatial differentiation in flood risk patterns, with higher-risk areas concentrated in suburban regions, new urban districts, and densely populated city centers. By identifying dominant resilience factors and uncovering spatial heterogeneity, this study provides a valuable tool for policymakers to identify flood risk areas, while at the same time advancing knowledge within the broader framework of flood resilience research.

Macrophages are critical effector cells of the innate immune system. The presence of microbes or the stimulation by inflammatory factors triggers the metabolic reprogramming of macrophages or macrophage polarization into two phenotypes: the classically activated macrophages (M1) displaying a pro-inflammatory phenotype and the alternatively activated macrophages (M2) having anti-inflammatory functions. The imbalance between the two phenotypes has been linked with various pathological states, such as fibrosis, hepatitis, colitis, and tumor progression. An avenue of potential therapeutic strategies based on macrophage polarization has emerged. Therefore, it is essential to understand the mechanisms of macrophage polarization. In this review, we focus on the macrophage polarization process and discuss the stimuli-dependent conversion into M1 and M2 phenotypes. We also present the metabolic patterns supporting their specific functions. The factors and signaling cascades involved in intra-class switching are also detailed. Finally, the role of macrophage polarization in disease progression is discussed.

The whistler-mode wave is an electromagnetic wave that commonly occurs in space plasma and has been extensively studied, especially within the Earth's magnetosphere. They have also been reported in the near-Mars space, such as Martian upstream solar wind, crustal magnetic field, ionopause, and the magnetic reconnection ion diffusion region. However, the generation of whistler-mode waves in the Martian magnetotail current sheet is still unclear. Based on observations made by Mars Atmosphere and Volatile Evolution spacecraft, we report whistler-mode waves observed within a train of proton-scale magnetic dips during a Martian magnetotail current sheet crossing. The linear growth rate analyses demonstrate that the whistler-mode waves are locally generated within the magnetic dips. Unlike in Earth's plasma environment, the train of magnetic dips in the Martian plasma sheet is attributed to electron mirror-mode instability. Our finding suggests that the mirror-mode structure in the Martian magnetotail can be an important source region for generating whistler-mode waves. This provides a new insight into how whistler-mode waves are generated in unmagnetized planets.