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Extraction of vectorized building instances from remote sensing images has made significant progress in recent years. The extraction of vectorized building maps enables rapid, large-scale updates to geospatial databases. Recently, the contour-based methods have achieved amazing success because their learning goals are more compatible with vectorized polygons. However, existing contour-based approaches achieve contour updates only by learning the local features at each vertex, which can lead to a contour smoothing problem. Besides, these methods achieve classification via features at the center point, which hinders the receptive field at the instance level. Our primary motivation is to overcome existing limitations introduced by local modeling during contour regression while simultaneously improving the instance classification performance. In this article, we utilize instance-level features to guide the contour learning process. An instance-aware contour regression (IACR) module is designed to update the local contour features via cross attention on the instance-level features. Furthermore, based on the IACR module, we propose a novel vectorized building extraction framework BuildingVec, which interacts between the contour regression branch and instance classification branch through a well-designed cascade architecture. We also build a vectorized building dataset with fine-grained categories, named UBCv2 (vec), to benefit the study of vectorized extraction for fine-grained buildings. Experiments on the UBCv2 (vec) dataset demonstrate that BuildingVec achieves state-of-the-art performance compared to both mask-based methods and contour-based methods. When compared to other vectorized extraction methods on the Crowd AI dataset, BuildingVec also achieves a state-of-the-art performance with an $\text{AP}_{50}$ of 93.1%.

Most state-of-the-art synthetic aperture radar (SAR) ship detection methods based on deep learning require large amounts of labeled data for network training. However, the annotation process requires significant manpower and resources especially for SAR images, since relevant background knowledge should be necessary for the annotators. Considering the available optical imagery datasets with labels, we propose an unsupervised oriented SAR ship detection method based on cross-modality distribution alignment, termed as CroMoDa. It consists of four components: 1) image-level feature alignment; 2) low-level feature despeckling; 3) cross-modality pseudo-label self-training; and 4) cross-modality object alignment. By aligning the multilevel feature distributions, modality-invariant features across the two imagery modalities can be learned. Considering speckle noise and other interferences in SAR images, the proposed loss term conducted on low-level features can enhance the object information to improve the detection accuracy. Moreover, the proposed pseudo-label self-training can better generate oriented SAR ship annotations than the other methods, which facilitates learning more discriminative features for SAR ship instances. With two optical–SAR dataset configurations, the proposed method is effectively evaluated by comparing to the other state of the arts, which demonstrates the great potential for SAR ship detection in real applications.

The metaverse signifies the amalgamation of virtual and tangible realms through human-computer interaction. The seamless integration of human, cyber, and environments within ubiquitous computing plays a pivotal role in fully harnessing the metaverse’s capabilities. Nevertheless, metaverse operating systems face substantial hurdles in terms of accessing ubiquitous resources, processing information while safeguarding privacy and security, and furnishing artificial intelligence capabilities to downstream applications. To tackle these challenges, this paper introduces the UbiMeta model, a specialized ubiquitous operating system designed specifically for the metaverse. It extends the capabilities of traditional ubiquitous operating systems and focuses on adapting downstream models and operational capacity to effectively function within the metaverse. UbiMeta comprises four layers: the Ubiquitous Resource Management Layer (URML), the Autonomous Information Mastery Layer (AIML), the General Intelligence Mechanism Layer (GIML), and the Metaverse Ecological Model Layer (MEML). The URML facilitates the seamless incorporation and management of various external devices and resources. It provides a framework for integrating and controlling these resources, including virtualization, abstraction, and reuse. The AIML is responsible for perceiving information and safeguarding privacy and security during storage and processing. The GIML leverages large-scale pre-trained deep-learning feature extractors to obtain effective features for processing information. The MEML focuses on constructing metaverse applications using the principles of Model-as-a-Service (MaaS) and the OODA loop (Observation, Orientation, Decision, Action). It leverages the vast amount of information collected by the URML and AIML layers to build a robust metaverse ecosystem. Furthermore, this study explores how UbiMeta enhances user experiences and fosters innovation in various metaverse domains. It highlights the potential of UbiMeta in revolutionizing medical healthcare, industrial practices, education, and agriculture within the metaverse.

Collaborative perception enhances onboard perceptual capability by integrating features from other platforms, effectively mitigating the compromised accuracy caused by a restricted observational range and vulnerability to interference. However, current implementations of collaborative perception overlook the prevalent issues of both limited and low-reliability communication, as well as misaligned observations in remote sensing. To address this problem, this article presents an innovative distributed collaborative perception network (DCP-Net) specifically designed for remote sensing applications. Firstly, a self-mutual information match module is proposed to identify collaboration opportunities and select suitable partners. This module prioritizes critical collaborative features and reduces redundant transmission for better adaptation to weak communication in remote sensing. Secondly, a related feature fusion module is devised to tackle the misalignment between local and collaborative features due to the multiangle observations, improving the quality of fused features for the downstream task. We conduct extensive experiments and visualization analyses using three semantic segmentation datasets, namely Potsdam, iSAID, and DFC23. The results demonstrate that DCP-Net outperforms the existing collaborative perception methods comprehensively, improving mIoU by 2.61% to 16.89% at the highest collaboration efficiency and achieving state-of-the-art performance.

This study proposes a Synthetic Aperture Radar (SAR) aircraft detection and recognition method combined with scattering perception to address the problem of target discreteness and false alarms caused by strong background interference in SAR images. The global information is enhanced through a context-guided feature pyramid module, which suppresses strong disturbances in complex images and improves the accuracy of detection and recognition. Additionally, scatter key points are used to locate targets, and a scatter-aware detection module is designed to realize the fine correction of the regression boxes to improve target localization accuracy. This study generates and presents a high-resolution SAR-AIRcraft-1.0 dataset to verify the effectiveness of the proposed method and promote the research on SAR aircraft detection and recognition. The images in this dataset are obtained from the satellite Gaofen-3, which contains 4,368 images and 16,463 aircraft instances, covering seven aircraft categories, namely A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and other. We apply the proposed method and common deep learning algorithms to the constructed dataset. The experimental results demonstrate the excellent effectiveness of our method combined with scattering perception. Furthermore, we establish benchmarks for the performance indicators of the dataset in different tasks such as SAR aircraft detection, recognition, and integrated detection and recognition.

Cutaneous T‐cell lymphoma (CTCL) encompasses two main subtypes: mycosis fungoides and Sezary syndrome. Global response rates for the systemic treatment of mycosis fungoides and Sezary syndrome are approximately 30%, and none of these treatments are thought to be curative. C–C chemokine receptor type 4 (CCR4) and CD25 are encouraging targets for the treatment of CTCL and are individually targeted by mogamulizumab and denileukin diftitox, respectively. We developed a novel CCR4‐IL2 bispecific immunotoxin (CCR4‐IL2 IT) targeting both CCR4 and CD25. CCR4‐IL2 IT demonstrated superior efficacy against CCR4+CD25+CD30+ CTCL in an immunodeficient NSG mouse tumor model. Investigative New Drug‐enabling studies of CCR4–IL2 IT are ongoing, including Good Manufacturing Practice production and toxicology studies. In this study, we compared the in vivo efficacy of CCR4‐IL2 IT versus the US Food and Drug Administration–approved drug, brentuximab, using an immunodeficient mouse CTCL model. We demonstrated that CCR4–IL2 IT was significantly more effective in prolonging survival than brentuximab, and combination treatment of CCR4–IL2 IT and brentuximab was more effective than brentuximab or CCR4–IL2 IT alone in an immunodeficient NSG mouse CTCL model. Thus, CCR4–IL2 IT is a promising novel therapeutic drug candidate for CTCL treatment.

The formation and stability of soil aggregates affect plant growth, carbon sequestration, and many other physiological and biogeochemical processes. Aggregates may be destabilized by nitrogen (N) deposition due to decreased inputs of binding materials; however, the legacy effects of which are unknown. An increase in labile carbon (C) input could mitigate the negative impacts of N addition on soil aggregate stability through the improvement of soil physical, chemical and biological conditions. Using a field experiment with the addition of NH4NO3 at multiple levels in a meadow steppe, we terminated the addition of N at the sixth year and shifted to applying labile C in the form of sucrose at three levels (C-0, C-200, and C-2000 g C m−2 y−1) to soil for two years. Then we examined the aggregate size distribution and the associated soil properties. The high historical N addition rates decreased the proportion of macroaggregates (>2000 μm) and increased microaggregates (

In recent years, legged robots have been more and more widely used on non-structured terrain, and their foot structure has an important impact on the robot’s motion performance and stability. The structural characteristics of the yak foot sole with a high outer edge and low middle, which has excellent soil fixation ability and is an excellent bionic prototype, can improve the friction between the foot and the ground. At the same time, the foot hooves can effectively alleviate the larger impact load when contacting with the ground, which is an excellent anti-slip buffer mechanism. The bionic foot end design was carried out based on the morphology of the yak sole; the bionic foot design was carried out based on the biological anatomy observation of yak foot skeletal muscles. The virtual models of the bionic foot end and the bionic foot were established and simulated using Solidworks 2022 and Abaqus 2023, and the anti-slip performance on different ground surfaces and the influence of each parameter of the bionic foot on the cushioning effect were investigated. The results show that (1) the curved shape of the yak sole has a good anti-slip performance on both soil ground and rocky ground, and the anti-slip performance is better on soil ground; (2) the curved shape of the yak sole has a larger maximum static friction than the traditional foot, and the anti-slip performance is stronger under the same pressure conditions; (3) the finger pillow–hoof ball structure of the bionic foot has the greatest influence on the buffering effect, and the buffering effect of the bionic foot is best when the tip of the bionic foot touches the ground first.

In order to assess the heavy metal pollution features, ecological dangers, and health risk status posed to human beings by soils in the Ankang Basin, a study was conducted. This involved the collection of 38 surface soil samples, followed by the determination of elemental levels of arsenic, mercury, copper, cadmium, lead, chromium, nickel, and zinc. The concentrations of arsenic, mercury, copper, cadmium, lead, chromium, nickel, and zinc were quantified through the collection of 38 surface soil samples. The data obtained from the study was subjected to analysis and evaluation utilizing various academic methodologies, including the geo-accumulation index method, potential ecological risk assessment method, human health risk assessment model, and Monte Carlo simulation method. The findings indicated that the concentrations of the eight heavy metals in the soil above the background levels, with only Cadmium (Cd) marginally surpassing the threshold set for controlling soil pollution risks. The ground accumulation index revealed a higher degree of soil pollution with mercury, cadmium, copper, and zinc components. According to the possible ecological risk index, the presence of mercury and cadmium elements poses significant ecological hazards. The geographical distribution analysis suggests that these risks mostly stem from the combined impacts of human activities and the topographical and geomorphological characteristics of the river valley. The findings of the human health risk assessment indicated that the non-carcinogenic risk fell within acceptable limits. Additionally, it was observed that the carcinogenic risk associated with arsenic, mercury, cadmium, and nickel was comparatively greater for children as compared to adults. The results of the Monte Carlo simulations indicate that the non-carcinogenic hazards have a negligible effect on human health. However, it was seen that arsenic and nickel have a greater likelihood of presenting a substantial carcinogenic risk to humans, particularly in relation to the pediatric population, hence exerting a more pronounced impact on their health. In general, it is observed that conventional deterministic risk assessments tend to overstate the potential health risks associated with a given situation. Conversely, the utilization of Monte Carlo simulations has been found to effectively mitigate uncertainties in health risk assessments. It has been observed that children exhibit a higher vulnerability to both carcinogenic and non-carcinogenic health impacts resulting from exposure to heavy metals present in soil, in comparison to adults. It is recommended that residents prioritize the surveillance of soil heavy metals in relation to potential impacts on human health.

Few-shot learning, which aims to learn the concept of novel category from extremely limited labeled samples, has received intense interests in remote sensing image scene classification. Most of the existing methods inherit the philosophy of prototype learning and tackle classification as the prototype-based metric matching problem. Despite the achievement that has been obtained so far, the problems of interclass metric misalignment and intraclass variations have become two main challenges that obstacle the performance. In this article, a novel transductive learning framework with conditional metric embedding is proposed to remedy these problems. First, a conditional metric embedding mechanism is introduced to perform anisotropic embedding for each pair of the support category and query instance. This design provides the model with flexible scalability to accommodate the metric biases across classes. Second, a transductive prototype learning strategy is presented to enhance the robustness of the prototype against intraclass variations. The unlabeled query instances are transformed into pseudo instances equipped with credibility coefficients and then leveraged to calibrate the prototype estimation bias in low-data regimes. Third, a long-term consistency regularization is designed, which continuously memorizes the historical prototypes to generate additional supervision of interclass separation in the global label space. Benefiting from this design, the discriminability of the prototypes obtains obvious improvement. Finally, extensive experiments are conducted on three public benchmark remote sensing datasets. The experimental results demonstrate the validity and the superiority of the proposed method in low-shot conditions.

The sample scarcity issue is still challenged for SAR images interpretation. The number of geospatial targets related images is constrained of the SAR images interpretation ability of data acquisition, sample labeling, and the lack of target coverage. Our SAR-ATR method is demonstrated based on scattering information and meta-learning. First, the discrete distribution of the spatial structure of different types of aircraft is quite different in SAR images. An associated scattering classifier is designed to guide the network to learn more discriminative intra-class and inter-class feature descriptions. Our proposed classifier facilitates the modeling of discrete degree of the aircraft target quantitatively and balance the weights of sample pairs dynamically through the differentiated analysis of different target discrete distributions. In addition, an adaptive feature refinement module is designed to optimize the network cohesion for the key parts of the aircraft and reduce the interference of background noise. The proposed method integrates the target scattering distribution properties to the network learning process. On 5-way 1-shot emerging categorized recognition task involved only few samples, our experimental results demonstrate that the recognition accuracy of this method is 59.90%, which is 3.85% higher than the benchmark. After reducing the amount of training data by half, the proposed method is still competitive on the new category of few-shot recognition tasks.

Over the recent years, high-resolution Synthetic-Aperture Radar (SAR) images have been widely applied for intelligent interpretation of urban mapping, change detection, etc. Different from optical images, the acquisition approach and object geometry of SAR images have limited the interpretation performances of the existing deep-learning methods. This paper proposes a novel building footprint generation method for high-resolution SAR images. This method is based on supervised contrastive learning regularization, which aims to increase the similarities between intra-class pixels and diversities of interclass pixels. This increase will make the deep learning models focus on distinguishing building and nonbuilding pixels in latent space, and improve the classification accuracy. Based on public SpaceNet6 data, the proposed method can improve the segmentation performance by 1% compared to the other state-of-the-art methods. This improvement validates the effectiveness of the proposed method on real data. This method can be used for building segmentation in urban areas with complex scene background. Moreover, the proposed method can be extended for other types of land-cover segmentation using SAR images.

Abstract Background Bletilla striata is the main medicine of many skin whitening classic formulas in traditional Chinese medicine (TCM) and is widely used in cosmetic industry recently. However, its active ingredients are still unclear and its fibrous roots are not used effectively. The aim of the present study is to discover and identify its potential anti-melanogenic active constituents by zebrafish model and molecular docking. Methods The antioxidant activities were evaluated by 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity, 2,2′-azino-bis-(3-ethylbenthiazoline-6-sulphonic acid) (ABTS) radical scavenging activity and ferric reducing antioxidant power (FRAP) assay. The anti-melanogenic activity was assessed by tyrosinase inhibitory activity in vitro and melanin inhibitory in zebrafish. The chemical profiles were performed by ultra-high-performance liquid chromatography combined with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS). Meanwhile, the potential anti-melanogenic active constituents were temporary identified by molecular docking. Results The 95% ethanol extract of B. striata fibrous roots (EFB) possessed the strongest DPPH, ABTS, FRAP and tyrosinase inhibitory activities, with IC50 5.94 mg/L, 11.69 mg/L, 6.92 mmol FeSO4/g, and 58.92 mg/L, respectively. In addition, EFB and 95% ethanol extract of B. striata tuber (ETB) significantly reduced the melanin synthesis of zebrafish embryos in a dose-dependent manner. 39 chemical compositions, including 24 stilbenoids were tentatively identified from EFB and ETB. Molecular docking indicated that there were 83 (including 60 stilbenoids) and 85 (including 70 stilbenoids) compounds exhibited stronger binding affinities toward tyrosinase and adenylate cyclase. Conclusion The present findings supported the rationale for the use of EFB and ETB as natural skin-whitening agents in pharmaceutical and cosmetic industries.

Ship detection and classification in synthetic aperture radar (SAR) images play a vital role for wide applications. Due to the unique SAR imaging mechanism, ship detection and classification tasks have faced numerous challenges, such as land interference, image defocus, and noise. Many detectors and classifiers have been presented to handle these problems. However, the general deep learning-based detectors and classifiers lack the combination of SAR characteristics, which leads to poor performance. Compared with optical images, SAR images lack the texture information of ships, which brings great difficulties to the recognition task. To address the above issues, a novel deep learning-based ship detection and classification network combined with scattering characteristics is proposed in this article. First, to accurately locate ships in large-scale SAR images, this article designs a strong scattering point aware network (SPAN) by capturing the strong scattering points that existed in the ship area. SPAN recognizes the ship category according to their distribution characteristics. Second, to compensate for the feature loss caused by the down-sampling operation, this article designs a more suitable resolution recovery module to replace the bilinear interpolation method. Third, a region of interest automatic generation module is proposed to fully utilize the axis-align feature of oriented proposal boxes and the sufficient information of horizontal proposal boxes. Furthermore, the classification encoder module extracts the distribution feature of scattering points to classify SAR ships. Finally, the comprehensive experiments in the large-scale dataset for ship detection and classification in SAR images (LDSD) demonstrate the superior performance of the proposed method.

Polarimetric synthetic aperture radar (PolSAR) terrain segmentation is a fundamental research topic in PolSAR image interpretation. Recently, many studies have been investigated to handle this task. However, the existing data for PolSAR terrain segmentation have relatively limited scale and their scene complexity is relatively simple. These issues greatly restrict the development of algorithms. Therefore, there is a strong requirement for establishing a large-scale data set for terrain segmentation in complex-scene PolSAR images. In this paper, we present a benchmark data set containing a PolSAR amplitude image with a 9082×9805-pixel region and 2000 image patches with a size of 512×512 for PolSAR terrain segmentation, which is called AIR-PolSAR-Seg. We collect the PolSAR image with a resolution of 8 m from the GaoFen-3 satellite, and it is equipped with pixel-wise annotation which covers six categories. Compared with the previous data resources, AIR-PolSAR-Seg preserves some specific properties. First, AIR-PolSAR-Seg owns a large-size PolSAR image and provides a large quantity of image patches. It offers the research community a complete data resource with adequate training examples and reliable validation results. Second, AIR-PolSAR-Seg is established upon a PolSAR image with high scene complexity. This characteristic motivates robust and advanced segmentation approaches to facilitate complex-scene PolSAR image analysis. Based on AIR-PolSAR-Seg, three tasks are introduced: multi-category segmentation, water body segmentation, and building segmentation. Moreover, a performance analysis of traditional approaches and deep learning-based approaches are conducted, which can be regarded as baselines and provide references for future research.

CD3-epsilon(CD3e) immunotoxins (IT), a promising precision reagent for various clinical conditions requiring effective depletion of T cells, often shows limited treatment efficacy for largely unknown reasons. Tissue-resident T cells that persist in peripheral tissues have been shown to play pivotal roles in local and systemic immunity, as well as transplant rejection, autoimmunity and cancers. The impact of CD3e-IT treatment on these local cells, however, remains poorly understood. Here, using a new murine testing model, we demonstrate a substantial enrichment of tissue-resident Foxp3+ Tregs following CD3e-IT treatment. Differential surface expression of CD3e among T-cell subsets appears to be a main driver of Treg enrichment in CD3e-IT treatment. The surviving Tregs in CD3e-IT-treated mice were mostly the CD3edimCD62Llo effector phenotype, but the levels of this phenotype markedly varied among different lymphoid and nonlymphoid organs. We also found notable variations in surface CD3e levels among tissue-resident T cells of different organs, and these variations drive CD3e-IT to uniquely reshape T-cell compositions in local organs. The functions of organs and anatomic locations (lymph nodes) also affected the efficacy of CD3e-IT. The multi-organ pharmacodynamics of CD3e-IT and potential treatment resistance mechanisms identified in this study may generate new opportunities to further improve this promising treatment.

Abstract Background Soil microbial communities cope with an imbalanced supply of resources by adjusting their element acquisition and utilization strategies. Although soil pH has long been considered an essential driver of microbial growth and community composition, little is known about how soil acidification affects microbial acquisition and utilization of carbon (C) and nitrogen (N). To close the knowledge gap, we simulated soil acidification and created a pH gradient by adding eight levels of elemental sulfur (S) to the soil in a meadow steppe. Results We found that S-induced soil acidification strongly enhanced the ratio of fungi to bacteria (F:B) and microbial biomass C to N (MBC:MBN) and subsequently decreased the C:N imbalance between microbial biomass and their resources. The linear decrease in the C:N imbalance with decreasing soil pH implied a conversion from N limitation to C limitation. To cope with enhanced C versus N limitation, soil microbial communities regulated the relative production of enzymes by increasing the ratio of β-glucosidase (BG, C-acquiring enzyme) to leucine aminopeptidase (LAP, N-acquiring enzyme), even though both enzymatic activities decreased with S addition. Structural equation modeling (SEM) suggested that higher C limitation and C:N-acquiring enzyme stimulated microbial carbon-use efficiency (CUE), which counteracted the negative effect of metal stress (i.e., aluminum and manganese) under soil acidification. Conclusions Overall, these results highlight the importance of stoichiometric controls in microbial adaption to soil acidification, which may help predict soil microbial responses to future acid deposition.

Epidermal growth factor receptor (EGFR) is often overexpressed in head and neck squamous cell carcinoma (HNSCC) and represents a top candidate for targeted HNSCC therapy. However, the clinical effectiveness of current Food and Drug Administration (FDA)‐approved drugs targeting EGFR is moderate, and the overall survival rate for HNSCC patients remains low. Therefore, more effective treatments are urgently needed. In this study, we generated a novel diphtheria toxin‐based bivalent human epidermal growth factor fusion toxin (bi‐EGF‐IT) to treat EGFR‐expressing HNSCC. Bi‐EGF‐IT was tested for in vitro binding affinity, cytotoxicity, and specificity using 14 human EGFR‐expressing HNSCC cell lines and three human EGFR‐negative cancer cell lines. Bi‐EGF‐IT had increased binding affinity for EGFR‐expressing HNSCC compared with the monovalent version (mono‐EGF‐IT), and both versions specifically depleted EGFR‐positive HNSCC, but not EGFR‐negative cell lines, in vitro. Bi‐EGF‐IT exhibited a comparable potency to that of the FDA‐approved EGFR inhibitor, erlotinib, for inhibiting HNSCC tumor growth in vivo using both subcutaneous and orthotopic HNSCC xenograft mouse models. When tested in an experimental metastasis model, survival was significantly longer in the bi‐EGF‐IT treatment group than the erlotinib treatment group, with a significantly reduced number of metastases compared with mono‐EGF‐IT. In addition, in vivo off‐target toxicities were significantly reduced in the bi‐EGF‐IT treatment group compared with the mono‐EGF‐IT group. These results demonstrate that bi‐EGF‐IT is more effective and markedly less toxic at inhibiting primary HNSCC tumor growth and metastasis than mono‐EGF‐IT and erlotinib. Thus, the novel bi‐EGF‐IT is a promising drug candidate for further development.

The rapid development of deep learning brings effective solutions for remote sensing image interpretation. Training deep neural network models usually require a large number of manually labeled samples. However, there is a limitation to obtain sufficient labeled samples in remote sensing field to satisfy the data requirement. Therefore, it is of great significance to conduct the research on few-shot learning for remote sensing image interpretation. First, this article provides a bibliometric analysis of the existing works for remote sensing interpretation related to few-shot learning. Second, two categories of few-shot learning methods, i.e., the data-augmentation-based and the prior-knowledge-based, are introduced for the interpretation of remote sensing images. Then, three typical remote sensing interpretation applications are listed, including scene classification, semantic segmentation, and object detection, together with the corresponding public datasets and the evaluation criteria. Finally, the research status is summarized, and some possible research directions are provided. This article gives a reference for scholars working on few-shot learning research in the remote sensing field.

The building extraction from synthetic aperture radar (SAR) images has always been a challenging research topic. Recently, the deep convolution neural network brings excellent improvements in SAR segmentation. The fully convolutional network and other variants are widely transferred to the SAR studies because of their high precision in optical images. They are still limited by their processing in terms of the geometric distortion of buildings, the variability of building structures, and scattering interference between adjacent targets in the SAR images. In this article, a unified framework called selective spatial pyramid dilated (SSPD) network is proposed for the fine building segmentation in SAR images. First, we propose a novel encoder–decoder structure for the fine building feature reconstruction. The enhanced encoder and the dual-stage decoder, composed of the CBM and the SSPD module, extract and recover the crucial multiscale information better. Second, we design the multilayer SSPD module based on the selective spatial attention. The multiscale building information with different attention on multiple branches is combined, optimized, and adaptively selected for adaptive filtering and extracting features of complex multiscale building targets in SAR images. Third, according to the building features and SAR imaging mechanism, a new loss function called L-shape weighting loss (LWloss) is proposed to heighten the attention on the L-shape footprint characteristics of the buildings and reduce the missing detection of line buildings. Besides, LWloss can also alleviate the class imbalance problem in the optimization stage. Finally, the experiments on a large-scene SAR image dataset demonstrate the effectiveness of the proposed method and verify its superiority over other approaches, such as the region-based Markov random field, U-net, and DeepLabv3+.

To accurately extract various ground objects from polarimetric synthetic aperture radar (PolSAR) images is a challenging research topic. The deep convolutional neural networks are widely used in SAR segmentation due to their remarkable performance in optical remote sensing images. However, they are still limited by the geometric deformation of the objects, the strong scattering interference among adjacent objects, and the difficulty in distinguishing similar things. A large part of the reason lies in the insufficient utilization and the damage of feature mining for PolSAR data. In this article, focusing on the scattering matrix and the polarimetric coherency matrix, a polarimetric space reconstruction network is proposed. First, to maintain the relatively initial spatial constraints and the complete polarimetric information, the inputs are arranged by a spatial amplification coding method for the polarimetric coherency and scattering matrices. Second, a statistics enhancement module based on scattering characteristics is proposed to mine the differential expression among multiple scattering and polarimetric components, which supplements the local feature representation of convolutional operators. Third, the designed dual self-attention mechanism can capture the amplitude and phase relations of matrix element context adaptively. The proposed method keeps the spatiality of each scattering vector and fully explores the complementary information involved in the fully polarimetric data. Moreover, it accomplishes the accurate land cover classification for PolSAR images, especially the traditional confusing categories, such as water and roads. The experimental results on the E-SAR, AIRSAR, Gaofen-3, and RADARSAT-2 datasets show that the proposed method is effective and promising for PolSAR image segmentation.

Ship detection in synthetic aperture radar (SAR) images is a significant and challenging task. Recently, deep convolutional neural networks have been applied to solve the detection problem and made a great breakthrough. Previous works mostly rely on the manually designed anchor boxes to search for the region of interests, which is less flexible and suffers from a heavy computational load. Moreover, these detectors have limited performance in large-scale and complex scenes due to the strong interference of inshore background and the variability of object imaging characteristics. In this article, a novel ship detection method based on the scattering-keypoint-guided network is proposed to remedy these problems. First, an anchor-free network is built to eliminate the effect of anchor boxes, in which a more robust representation scheme is designed for the arbitrary oriented objects. Second, a context-aware feature selection module is introduced to dynamically learn both local and context features. In this process, the semantic information of objects can be enhanced while suppressing the background interference. Third, according to the SAR imaging mechanism, a set of scattering keypoints is defined to describe the local scattering regions and reflect the discriminative structural characteristics of ships. Based on this conception, a novel feature adaption method is proposed with the purpose of dealing with the imaging variability issue. Furthermore, to demonstrate the effectiveness of the proposed improvements, we build the Gaofen-3 ship detection dataset. Meanwhile, the public SAR ship detection dataset is introduced to verify the robustness and generalization ability of the detector. Experimental results on these two datasets show that the proposed method achieves the state-of-the-art performance.

Building footprint segmentation from high-resolution remote sensing (RS) images plays a vital role in urban planning, disaster response, and population density estimation. Convolutional neural networks (CNNs) have been recently used as a workhorse for effectively generating building footprints. However, to completely exploit the prediction power of CNNs, large-scale pixel-level annotations are required. Most state-of-the-art methods based on CNNs are focused on the design of network architectures for improving the predictions of building footprints with full annotations, while few works have been done on building footprint segmentation with limited annotations. In this article, we propose a novel semisupervised learning method for building footprint segmentation, which can effectively predict building footprints based on the network trained with few annotations (e.g., only $\text{0.0324 {km}}^2$ out of $\text{2.25-{km}}^2$ area is labeled). The proposed method is based on investigating the contrast between the building and background pixels in latent space and the consistency of predictions obtained from the CNN models when the input RS images are perturbed. Thus, we term the proposed semisupervised learning framework of building footprint segmentation as PiCoCo, which is based on the enforcement of Pixelwise Contrast and Consistency during the learning phase. Our experiments, conducted on two benchmark building segmentation datasets, validate the effectiveness of our proposed framework as compared to several state-of-the-art building footprint extraction and semisupervised semantic segmentation methods.

The majority of clinically diagnosed cutaneous T‐cell lymphomas (CTCL) highly express the cell‐surface markers CC chemokine receptor 4 (CCR4) and/or CD25. Recently, we have developed diphtheria toxin‐based recombinant Ontak®‐like human IL2 fusion toxin (IL2 fusion toxin) and anti‐human CCR4 immunotoxin (CCR4 IT). In this study, we first compared the efficacy of the CCR4 IT vs IL2 fusion toxin for targeting human CD25+CCR4+ CTCL. We demonstrated that CCR4 IT was more effective than IL2 fusion toxin. We further constructed an IL2‐CCR4 bispecific IT. The bispecific IT was significantly more effective than either IL2 fusion toxin or CCR4 IT alone. The bispecific IT is a promising novel targeted therapeutic drug candidate for the treatment of refractory and recurrent human CD25+ and/or CCR4+ CTCL.

While most monocular structure-from-motion frameworks rely on sparse keypoints, it has long been acknowledged that lines represent an alternative, higher-order feature with high accuracy, repeatability, and abundant availability in man-made environments. Its exclusive use, however, is severely complicated by its inability to resolve the common bootstrapping scenario of two-view geometry. Even with stereo cameras, a one-dimensional disparity space, as well as ill-posed triangulations of horizontal lines make the realization of purely line-based tracking pipelines difficult. The present paper successfully leverages the redundancy in camera matrices to alleviate this shortcoming. We present a novel stereo trifocal tensor solver and extend it to the case of two camera matrix view-points. Our experiments demonstrate superior behavior with respect to both 2D-2D and 3D-3D alternatives. We furthermore outline the camera matrix's ability to continuously and robustly bootstrap visual motion estimation pipelines via integration into a robust, purely line-based visual odometry pipeline. The result leads to state-of-the-art tracking accuracy comparable to what is achieved by point-based stereo or even dense depth camera alternatives.

Applications of mineral nitrogen (N) and phosphorus (P) fertilizer to grasslands become more frequently in recent decade, aiming to improve the grass production. Nonetheless, the ecological impacts of N and P fertilizer on ecosystem components, especially the belowground soil microbes, are not well acknowledged in grasslands ecosystem. We investigated the responses of plant traits, soil properties, bacterial community composition and potential functions to 5-year N and P fertilization in a mid-high latitude meadow grassland in Inner Mongolia, China. Bacterial species richness and diversity significantly (P

The study aimed to assess the gait adjustment techniques of limbs on different slopes and investigate the relationship between forelimb and hindlimb kinetics and the center of mass (COM) during the uphill movement of a specific Boer goat using a pressure-sensitive walkway (PSW). During the uphill and downhill movements at a comfortable walking speed, we measured the ground reaction force (GRF) of the forelimbs and hindlimbs on the slope, the change in the included angle of the propulsive force direction of the forelimbs and hindlimbs, and the impulse relationship between GRF and propulsive force. According to the study, since the forelimbs of the goat were nearer the COM, they were primarily adjusted during the movement on the slope. By lowering the initial included angle of the propulsive force and the angle variation range, the forelimbs and hindlimbs could walk steadily. The forelimbs and hindlimbs exhibited completely different adjustment strategies during uphill and downhill movements. In particular, the forelimbs performed braking and the hindlimbs performed driving. In addition, we discovered that the goat altered its adjustment strategy when climbing the steep slope. All findings of this study indicate the need to understand the gait adjustment mode of the Boer goat during movement on the slope to thoroughly comprehend the driving strategy of quadrupeds with the ability to walk on specialized terrains.

Proteoglycans are glycosylated proteins that play a number of structural and signalling functions. Here, Corti, Wang et al. show that the N-terminal sequence of proteoglycan Syndecan-2 selectively increases 6-O sulfation of its heparan sulfate chains, and that this promotes formation of a ternary Sdc2/VEGFA/VEGFR2 complex leading to increased angiogenesis.

Campylobacter jejuni CsrA is an mRNA-binding, post-transcriptional regulator that controls many metabolic- and virulence-related characteristics of this important pathogen. In contrast to E. coli CsrA, whose activity is modulated by binding to small non-coding RNAs (sRNAs), C. jejuni CsrA activity is controlled by binding to the CsrA antagonist FliW. In this study, we identified the FliW binding site on CsrA. Deletion of the C-terminus of C. jejuni CsrA, which is extended relative to sRNA-binding CsrA proteins, abrogated FliW binding. Bacterial two-hybrid experiments were used to assess the interaction of FliW with wild-type CsrA and mutants thereof, in which every amino acid was individually mutated. Two CsrA mutations (V51A and N55A) resulted in a significant decrease in FliW binding. The V51A and N55A mutants also showed a decrease in CsrA-FliW complex formation, as assessed by size-exclusion chromatography and surface plasmon resonance. These residues were highly conserved in bacterial species containing CsrA orthologs whose activities are predicted to be regulated by FliW. The location of FliW binding was immediately adjacent to the two RNA-binding sites of the CsrA homodimer, suggesting the model that FliW binding to CsrA modulates its ability to bind to its mRNA targets either by steric hindrance, electrostatic repulsion, or by altering the overall structure of the RNA-binding sites.

Anti-CD3-epsilon (CD3e) monoclonal antibodies (mAbs) and CD3e immunotoxins (ITs) are promising targeted therapy options for various T-cell disorders. Despite significant advances in mAb and IT engineering, vascular leakage syndrome (VLS) remains a major dose-limiting toxicity for ITs and has been poorly characterized for recent “engineered” mAbs. This study undertakes a direct comparison of non-mitogenic CD3e-mAb (145-2C11 with Fc-silentTM murine IgG1: S-CD3e-mAb) and a new murine-version CD3e-IT (saporin–streptavidin (sZAP) conjugated with S-CD3e-mAb: S-CD3e-IT) and identifies their distinct toxicity profiles in mice. As expected, the two agents showed different modes of action on T cells, with S-CD3e-mAb inducing nearly complete modulation of CD3e on the cell surface, while S-CD3e-IT depleted the cells. S-CD3e-IT significantly increased the infiltration of polymorphonuclear leukocytes (PMNs) into the tissue parenchyma of the spleen and lungs, a sign of increased vascular permeability. By contrast, S-CD3e-mAbs-treated mice showed no notable signs of vascular leakage. Treatment with control ITs (sZAP conjugated with Fc-silent isotype antibodies) induced significant vascular leakage without causing T-cell deaths. These results demonstrate that the toxin portion of S-CD3e-IT, not the CD3e-binding portion (S-CD3e-mAb), is the main driver of vascular leakage, thus clarifying the molecular target for improving safety profiles in CD3e-IT therapy.

Recently, we have developed a diphtheria toxin‐based recombinant anti‐human CCR4 immunotoxin for targeting CCR4+ tumors and Tregs. In this study, we further optimized the dosing schedule for improved CCR4+ Treg depletion. We have demonstrated that up to a 90% depletion was achieved and the depletion extended to approximately 2 weeks in the peripheral blood and more than 48 days in the lymph node at 25 μg·kg−1, BID for 8 consecutive days in cynomolgus monkeys. Expansion was observed including monocytes and NK cells. Antibody against the CCR4 immunotoxin was detected after approximately 2 weeks, affecting further depletion efficacy for multiple course treatment.

CC chemokine receptor 4 (CCR4) has attracted much attention as a promising therapeutic drug target for CCR4+ tumor cells and Tregs. CCR4 is expressed on some tumor cells such as T‐cell acute lymphoblastic leukemia (ALL), adult T‐cell leukemia/lymphoma (ATLL), adult peripheral T cell lymphoma (PTCL) and cutaneous T cell lymphoma (CTCL). CCR4 is also expressed on majority of Tregs, mainly effector Tregs. In this study we have successfully developed three versions of diphtheria‐toxin based anti‐human CCR4 immunotoxins (monovalent, bivalent and single‐chain fold‐back diabody). Binding analysis by flow cytometry showed that all three versions of the anti‐human CCR4 immunotoxins bound to the human CCR4+ tumor cell line as well as CCR4+ human PBMC. The bivalent isoform bound stronger than its monovalent counterpart and the single‐chain foldback diabody isoform was the strongest among the three versions. In vitro efficacy analysis demonstrated that the bivalent isoform was 20 fold more potent in inhibiting cellular proliferation and protein synthesis in human CCR4+ tumor cells compared to the monovalent anti‐human CCR4 immunotoxin. The single‐chain fold‐back diabody isoform was 10 fold more potent than its bivalent counterpart and 200 fold more potent than its monovalent counterpart. The in vivo efficacy was assessed using a human CCR4+ tumor‐bearing mouse model. The immunotoxin significantly prolonged the survival of tumor‐bearing NOD/SCID IL‐2 receptor γ−/− (NSG) mice injected with human CCR4+ acute lymphoblastic leukemia cells compared with the control group. This novel anti‐human CCR4 immunotoxin is a promising drug candidate for targeting human CCR4+ tumor cells and Tregs in vivo.

The elevational pattern of soil microbial diversity along mountain slopes has received considerable interest over the last decade. An increasing amount of taxonomic data on soil microbial community composition along elevation gradients have been collected, however the trophic patterns and environmental drivers of elevational changes remain largely unclear. Here, we examined the distribution patterns of major soil bacterial and fungal taxa along the northern slope of Changbai Mountain, Northeast China, at five typical vegetation types located between 740 and 2,691 m above sea level. Elevational patterns of the relative abundance of specific microbial taxa could be partially explained by the oligotrophic-copiotrophic theory. Specifically, two dark-coniferous forests, located at mid-elevation sites, were considered to be oligotrophic habitats, with relatively higher soil C/N ratio and NH4+-N concentrations. As expected, oligotrophic microbial taxa, belonging to the bacterial phyla Acidobacteria and Gemmatimonadetes, and fungal phylum Basidiomycota, were predominant in the two dark-coniferous forests, exhibiting a mid-elevation maximum pattern. In contrast, the broad leaf-Korean pine mixed forest located at the foot of the mountain, Betula ermanii-dominated forest located below the tree line, and alpine tundra at the highest elevation were considered more copiotrophic habitats, characterized by higher substrate-induced-respiration rates and NO3--N concentrations. Microbial taxa considered to be so called copiotrophic members, such as bacterial phyla Proteobacteria and Actinobacteria, and fungal phylum Ascomycota, were relatively abundant in these locations, resulting in a mid-elevation minimum pattern. At finer taxonomic levels, the two most abundant proteobacterial classes, alpha- and beta-Proteobacteria, along with Acidobacteria Gp1, 2, 3, 15, and the Basidiomycotal class of Tremellomycetes were classified with the copiotrophic group. Gamma- and delta-Proteobacteria, Acidobacteria Gp4, 6, 7, 16, and Basidiomycotal class of Agaricomycetes were classified as oligotrophic taxa. This work uses the oligotrophic-copiotrophic theory to explain the elevational distribution pattern of the relative abundance of specific microbial taxa, confirming some of the existing trophic classifications of microbial taxa and expanding on the theory to include a broader range of taxonomic levels.

The original version of this Article contained errors in Figures 1, 3 and 4. In panels b and d of Figure 1, the labels ‘Sdc4-/-’ were inadvertently replaced by ‘Sdc4+/+‘. In panels c and d of Figure 3, the labels ‘Sdc4-/-’ were replaced by ‘Sdc2-/-’. In panel f of Figure 3, the labels ‘FGF2’ were replaced by ‘VEGFA165’. In panel e of Figure 6, a ‘Sdc2-/-‘ label was inadvertently included. This has now been corrected in the PDF and HTML versions of the Article.

Aircraft recognition in remote sensing images has long been a meaningful topic. Most related methods treat entire images as a whole and do not concentrate on the features of parts. In fact, a variety of aircraft types have small interclass variance, and the main evidence for classifying subcategories is related to some discriminative object parts. In this paper, we introduce the idea of fine-grained visual classification (FGVC) and attempt to make full use of the features from discriminative object parts. First, multiple class activation mapping (MultiCAM) is proposed to extract the discriminative parts of aircrafts of different categories. Second, we present a mask filter (MF) strategy to enhance the discriminative object parts and filter the interference of the background from original images. Third, a selective connected feature fusion method is proposed to fuse the features extracted from both networks, focusing on the original images and the results of MF, respectively. Compared with the single prediction category in class activation mapping (CAM), MultiCAM makes full use of the predictions of all categories to overcome the wrong discriminative parts produced by a wrong single prediction category. Additionally, the designed MF preserves the object scale information and helps the network to concentrate on the object itself rather than the interfering background. Experiments on a challenging dataset prove that our method can achieve state-of-the-art performance.

A deeper understanding of the mechanical behavior of ultra-fine (UF) and nanocrystalline (NC) grained metals is necessary with the growing interest in using UF and NC grained metals for structural applications. The cyclic deformation response and behavior of UF and NC grained metals is one aspect that has been gaining momentum as a major research topic for the past ten years. Severe Plastic Deformation (SPD) materials are often in the spotlight for cyclic deformation studies as they are usually in the form of bulk work pieces and have UF and NC grains. Some well known techniques in the category of SPD processing are High Pressure Torsion (HPT), Equal Channel Angular Pressing (ECAP), and Accumulative Roll-Bonding (ARB). In this report, the literature on the cyclic deformation response and behavior of SPDed metals will be reviewed. The cyclic response of such materials is found to range from cyclic hardening to cyclic softening depending on various factors. Specifically, for SPDed UF grained metals, their behavior has often been associated with the observation of grain coarsening during cycling. Consequently, the many factors that affect the cyclic deformation response of SPDed metals can be summarized into three major aspects: (1) the microstructure stability; (2) the limitation of the cyclic lifespan; and lastly (3) the imposed plastic strain amplitude.

A new multiple access scheme, Waveform Division Multiple Access (WDMA) based on the orthogonal wavelet function, is presented. After studying the correlation properties of different categories of single wavelet functions, the one with the best correlation property will be chosen as the foundation for combined waveform. In the communication system, each user is assigned to different combined orthogonal waveform. Demonstrated by simulation, combined waveform is more suitable than single wavelet function to be a communication medium in WDMA system. Due to the excellent orthogonality, the bit error rate (BER) of multiuser with combined waveforms is so close to that of single user in a synchronous system. That is to say, the multiple access interference (MAI) is almost eliminated. Furthermore, even in an asynchronous system without multiuser detection after matched filters, the result is still pretty ideal and satisfactory by using the third combination mode that will be mentioned in the study.

The decoration of the lipid A headgroups of the lipooligosaccharide (LOS) by the LOS phosphoethanolamine (PEA) transferase (LptA) in Neisseria spp. is central for resistance to polymyxin. The structure of the globular domain of LptA shows that the protein has five disulphide bonds, indicating that it is a potential substrate of the protein oxidation pathway in the bacterial periplasm. When neisserial LptA was expressed in Escherichia coli in the presence of the oxidoreductase, EcDsbA, polymyxin resistance increased 30-fold. LptA decorated one position of the E. coli lipid A headgroups with PEA. In the absence of the EcDsbA, LptA was degraded in E. coli. Neisseria spp. express three oxidoreductases, DsbA1, DsbA2 and DsbA3, each of which appear to donate disulphide bonds to different targets. Inactivation of each oxidoreductase in N. meningitidis enhanced sensitivity to polymyxin with combinatorial mutants displaying an additive increase in sensitivity to polymyxin, indicating that the oxidoreductases were required for multiple pathways leading to polymyxin resistance. Correlates were sought between polymyxin sensitivity, LptA stability or activity and the presence of each of the neisserial oxidoreductases. Only meningococcal mutants lacking DsbA3 had a measurable decrease in the amount of PEA decoration on lipid A headgroups implying that LptA stability was supported by the presence of DsbA3 but did not require DsbA1/2 even though these oxidoreductases could oxidise the protein. This is the first indication that DsbA3 acts as an oxidoreductase in vivo and that multiple oxidoreductases may be involved in oxidising the one target in N. meningitidis. In conclusion, LptA is stabilised by disulphide bonds within the protein. This effect was more pronounced when neisserial LptA was expressed in E. coli than in N. meningitidis and may reflect that other factors in the neisserial periplasm have a role in LptA stability.

With a short linear array configured in the cross-track direction, downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain the 3-D image of an imaging scene. To improve the cross-track resolution, sparse recovery methods have been investigated in recent years. In the compressive sensing (CS) framework, the reconstruction performance depends on the property of measurement matrix. This paper concerns the technique to optimize the measurement matrix and deal with the mismatch problem of measurement matrix caused by the off-grid scatterers. In the model of cross-track reconstruction, the measurement matrix is mainly affected by the configuration of antenna phase centers (APC), thus, two mutual coherence based criteria are proposed to optimize the configuration of APCs. On the other hand, to compensate the mismatch problem of the measurement matrix, the sparse Bayesian inference based method is introduced into the cross-track reconstruction by jointly estimate the scatterers and the off-grid error. Experiments demonstrate the performance of the proposed APCs’ configuration schemes and the proposed cross-track reconstruction method.

To detect and estimate ground slowly moving targets in airborne single-channel synthetic aperture radar (SAR), a road-aided ground moving target indication (GMTI) algorithm is proposed in this paper. First, the road area is extracted from a focused SAR image based on radar vision. Second, after stationary clutter suppression in the range-Doppler domain, a moving target is detected and located in the image domain via the watershed method. The target’s position on the road as well as its radial velocity can be determined according to the target’s offset distance and traffic rules. Furthermore, the target’s azimuth velocity is estimated based on the road slope obtained via polynomial fitting. Compared with the traditional algorithms, the proposed method can effectively cope with slowly moving targets partly submerged in a stationary clutter spectrum. In addition, the proposed method can be easily extended to a multi-channel system to further improve the performance of clutter suppression and motion estimation. Finally, the results of numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm.