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Background: Pelvic organ prolapse (POP) is a common condition worldwide, predominantly affecting middle-aged and elderly postmenopausal women. However, its pathogenesis, etiology, and associated risk factors remain unclear. This study aimed to analyze the distribution of POP severity stages, identify associated risk factors, and explore its underlying pathogenesis to develop strategies for early prevention and intervention. Methods: This retrospective study included all patients diagnosed with POP using the pelvic organ prolapse quantification (POP-Q) system at the outpatient clinic of West China Second Hospital, Sichuan University, between January 2015 and January 2019. A univariate Chi-square test and multivariate logistic regression analysis were performed to evaluate the relationships between the identified factors. Results: Among 9569 patients with anterior vaginal wall prolapse, the severity was distributed as follows: grade I (38.52%), grade II (60.70%), grade III (0.77%), and grade IV (0.01%). Among the 9495 patients with posterior vaginal wall prolapse, the severity was distributed as follows: grade I (90.37%), grade II (9.47%), and grade III (0.16%). Among the 932 patients with uterine prolapse, the severity was distributed as follows: grade I (85.84%), grade II (5.36%), grade III (7.62%), and grade IV (1.18%). Multivariate logistic regression analysis identified several significant risk factors associated with the progression of anterior vaginal wall prolapse, including advanced maternal age (AMA) (≥35 years), body mass index (BMI) (≥25 kg/m2), parity (≥2), and vaginal delivery (p < 0.05). For posterior vaginal wall prolapse, significant risk factors included AMA (≥35 years), BMI (≥25 kg/m2), manual labor, and vaginal delivery (p < 0.001). Additionally, AMA (≥35 years) was identified as a significant risk factor for the progression of uterine prolapse (p < 0.05). Conclusions: Grade II prolapse was most commonly observed in the anterior vaginal wall, whereas grade I prolapse predominantly occurred in the posterior vaginal wall. Key risk factors for POP include childbearing age, parity, BMI, and mode of delivery, with AMA identified as independent risk factors. Vaginal delivery specifically increases the likelihood of vaginal prolapse, while occupations involving prolonged physical labor correlate with a higher incidence of posterior vaginal wall prolapse. Based on the identified risk factors, targeted prevention and intervention measures are recommended to facilitate early identification and reduce the incidence of POP.

Blind deblurring of optical remote sensing images has been a longstanding challenge. In recent years, many learning-based deblurring algorithms have been greatly developed. However, these methods often suffer from losing image texture details and some artificial artifacts under low signal-to-noise ratio (SNR) conditions. To tackle this challenge, we introduce an innovative end-to-end noise-robust blind deblurring algorithm based on the diffusion model joined with a denoising module and a wavelet-enhanced conditional embedding mechanism. Experiments have verified the effectiveness of our method. Compared to the image blind deblurring algorithms based on the diffusion model, the proposed algorithm demonstrates better performance in terms of quantitative metrics peak signal-to-noise ratio and structural similarity index. Compared to all the comparative algorithms, the proposed algorithm exhibits significant advantages in quantitative metrics learned perceptual image patch similarity, Fréchet inception distance, and natural image quality evaluator and shows superior visual effects in restoring texture details in the restored images, especially in challenging low SNR conditions.

Pansharpening aims to reconstruct the high-resolution multispectral (HR-MS) image by fusing the low-resolution multispectral (LR-MS) image and the panchromatic (PAN) images. The effectiveness of pansharpening algorithms highly relies on the fusion mechanism of two images with different modality. In this article, we propose a novel progressive reconstruction network (PRNet) with adaptive frequency adjustment to address the spatial and frequency discrepancies between LR-MS and PAN images. Aiming to simultaneously capture contextual information and frequency characteristics, our PRNet fuses the LR-MS and PAN features in biscale frequency domains. Specifically, we use the wavelet transform to decompose the PAN features into biscale coefficients, dubbed as coarse-scale and fine-scale coefficients, respectively. Initially, the LR-MS features are fused into the coarse-scale coefficients first, and then the coarse-scale fused results are used to refine the fine-scale coefficients, leading to the progressive reconstruction mechanism. Additionally, to mitigate aliasing between LR-MS features and PAN coefficients across multiple frequency domains, we propose the adaptively frequency adjustment module to control the fusion weights and recalibrate the frequency responses of the reconstruct features. Experimental evaluations on reduced-resolution and full-resolution pansharpening datasets demonstrate that our method outperforms state-of-the-art pansharpening methods quantitatively and qualitatively, affirming its effectiveness. The code will be released soon.

Abstract Public sports venues serve as a crucial material medium for human athletic activities. Research on their spatial distribution holds profound implications for optimizing spatial layout and promoting human sports endeavors. This paper takes interest points of sports venues in China as research samples, employing GIS spatial analysis tools, mathematical statistics, and Geo-detector as research methods. It systematically investigates the spatial distribution characteristics and causes of sports venues in China, aiming to provide insights and references for the optimization of spatial arrangement, sustainable development, and relevant policy formulation of sports venues in the country. The results indicate that: (1) In terms of overall distribution characteristics, sports venues in China present a multi-centric agglomeration in geographical space. (2) From a regional distribution perspective, the spatial layout of these venues reveals a pattern of “more in the south, less in the north,” “dense in the southeast, sparse in the northwest,” and a coastal strip distribution. (3) Regarding spatial correlation, the hot and cold spot partitions of sports venues largely align with the “Hu-Line”, with their spatial distribution tending towards a positively correlated pattern of high–high clustering and low–low clustering. (4) There is evident heterogeneity in factors affecting sports venues’ spatial distribution. The distribution results from the interactive coupling of multiple factors, where the interaction between any two factors offers stronger explanatory power for the spatial layout of the venues.

Periodontitis is a frequent chronic inflammatory disease brought on by bacterial infection in the biofilm that forms on the teeth. It can cause the gingival recession and tooth loss, gravely jeopardizing the oral health of people. Current therapies still suffer from issues like bleeding, pain, injured gums, limited treatment effectiveness, a lengthy treatment cycle, and bacterial resistance when using a combination of surgical intervention and antibiotic therapy. Herein, a simple one-step cross-linking process was used to create glucose oxidase (GOD) loaded upon iron alginate (FeAlg/GOD), which has a strong anti-biofilm activity, good biocompatibility, and was successfully used to treat periodontitis caused by biofilms. The results demonstrated that in the presence of a high glucose concentration in the gingival crevicular fluid, FeAlg/GOD could catalyze the conversion of glucose into gluconic acid and hydrogen peroxide. The produced hydrogen peroxide could convert Fe3+ to Fe2+ when it got to the deep periodontal pockets. Following that, the Fenton reaction was activated, producing large amounts of hydroxyl radical (·OH). In conclusion, oral plaque biofilms were removed by the FeAlg/GOD. Additionally, successful in vivo treatment was accomplished in rat's experimental periodontitis models, as evidenced by a markedly reduced inflammatory response, decreased bone loss, and made healthier gum tissue. Overall, our biocompatible and antibacterial FeAlg/GOD study presents a novel strategy for effectively speeding periodontal tissue repair and eliminating oral pathogens to tackle the grave threat of biofilm-associated illnesses.

Abstract Because of similar astronomical background, Marine Isotope Stage (MIS) 11 is viewed as an analogue of the Holocene, but the evolution of seasonal climatic instability during MIS 11 has not been well investigated. Here we present a time series of land-snail eggs—a recently-developed proxy of seasonal cooling events—from the Chinese Loess Plateau (CLP) to investigate seasonal climatic instability during MIS 11 and adjacent glacials. Due to the impact of low temperatures on egg hatching, egg-abundance peaks document seasonal cooling events. A total of five egg-abundance peaks were recorded in the CLP during MIS 12, MIS 11 and MIS 10. Three peaks are strong and occur close to glacial inception or interglacial-to-glacial transition; two weaker peaks occur during MIS11. These peaks imply seasonal climatic instability intensifies mainly during glacial initiation or transition. All these events correspond to ice-sheet growth and the loss of ice-rafted debris at high northern latitudes. Moreover, they occurred at the minima of local spring insolation during the MIS 12 and MIS 10 glacials, but at the maxima during the MIS 11 interglacial. This may contribute to the difference in the intensity of seasonal cooling events between low-eccentricity glacials and interglacials. Our results provide new evidence for understanding low-eccentricity interglacial–glacial evolution.

The unique imaging modality of synthetic aperture radar (SAR) has posed significant challenges for object detection, making it more complex to acquire and interpret than optical images. Recently, numerous studies have proposed cross-domain adaptive methods based on convolutional neural networks (CNNs) to promote SAR object detection using optical data. However, existing cross-domain methods focus on image features, lack improvement on input data, and ignore the valuable supervision provided by few labeled SAR images. Therefore, we propose a semi-supervised cross-domain object detection framework that uses optical data and few SAR data to achieve knowledge transfer for SAR object detection. Our method focuses on the data processing aspects to gradually reduce the domain shift at the image, instance, and feature levels. First, we propose a data augmentation method of image mixing and instance swapping to generate a mixed domain that is more similar to the SAR domain. This method fully utilizes few SAR annotation information to reduce domain shift at image and instance levels. Second, at the feature level, we propose an adaptive optimization strategy to filter out mixed domain samples that significantly deviate from the SAR feature distribution to train feature extractor. In addition, we employ Vision Transformer (ViT) as feature extractor to handle the global feature extraction of mixed images. We propose a detection head based on normalized Wasserstein distance (NWD) to enhance objects with smaller effective regions in SAR images. The effectiveness of our proposed method is evaluated on public SAR ship and oil tank datasets.

Extracting buildings in urban scenes from remote sensing images is crucial for the construction of digital cities, urban monitoring, urban planning, and autonomous driving. Traditional methods generally rely on shadow detection or stereo matching from multi-view high-resolution remote sensing images, which is cost-intensive. Recently, machine learning has provided solutions for the estimation of building heights from remote sensing images, but challenges remain due to the limited observation angles and image quality. The inherent lack of information in a single modality greatly limits the extraction precision. This article proposes an advanced method using multi-source remote sensing images for urban building height estimation, which is characterized by multi-level cross-fusion, the multi-task joint learning of footprint extraction and height estimation, and semantic information to refine the height estimation results. The complementary and effective features of synthetic aperture radar (SAR) and electro-optical (EO) images are transferred through multi-level cross-fusion. We use the semantic information of the footprint extraction branch to refine the height estimation results, enhancing the height results from coarse to fine. Finally, We evaluate our model on the SpaceNet 6 dataset and achieve 0.3849 and 0.7231 in the height estimation metric δ1 and footprint extraction metric Dice, respectively, which indicate effective improvements in the results compared to other methods.

Monocular depth estimation using a single remote sensing image has emerged as a focal point in both remote sensing and computer vision research, proving crucial in tasks such as 3D reconstruction and target instance segmentation. Monocular depth estimation does not require multiple views as references, leading to significant improvements in both time and efficiency. Due to the complexity, occlusion, and uneven depth distribution of remote sensing images, there are currently few monocular depth estimation methods for remote sensing images. This paper proposes an approach to remote sensing monocular depth estimation that integrates an attention mechanism while considering global and local feature information. Leveraging a single remote sensing image as input, the method outputs end-to-end depth estimation for the corresponding area. In the encoder, the proposed method employs a dense neural network (DenseNet) feature extraction module with efficient channel attention (ECA), enhancing the capture of local information and details in remote sensing images. In the decoder stage, this paper proposes a dense atrous spatial pyramid pooling (DenseASPP) module with channel and spatial attention modules, effectively mitigating information loss and strengthening the relationship between the target’s position and the background in the image. Additionally, weighted global guidance plane modules are introduced to fuse comprehensive features from different scales and receptive fields, finally predicting monocular depth for remote sensing images. Extensive experiments on the publicly available WHU-OMVS dataset demonstrate that our method yields better depth results in both qualitative and quantitative metrics.

Scientists have been puzzled by the disparity between climate simulations of the past 12,000 years and geological records. Dong et al. reconstructed past annual and seasonal temperatures from land snail records to examine the potential seasonal bias.

With the expansion of Synthetic Aperture Radar (SAR) applications and the development of SAR data acquisition technology, multiangle SAR datasets of various typical targets need to be constructed. Presently, a comprehensive multiangle SAR image dataset for aircraft targets is still lacking. This study explores a method of dataset construction based on the acquisition of actual data and intelligent simulation. Multiangle SAR images of aircraft targets are collected through flight tests, and the interpolation simulations of SAR images of specific angles are realized based on scattering analysis and self-attention generative adversarial network, which provide a new solution for dataset construction and expansion. Finally, under the assumption that some data are missing, the similarities between the simulated and actual images are evaluated using six evaluation indices, which verify the effectiveness of the proposed method.

Diabetic periodontitis is a major complication of diabetes, which has a deep involvement in teeth loss and more serious systematic diseases, including Alzheimer's disease, atherosclerosis and cancers. Diabetic periodontitis is difficult to treat because of recalcitrant infection and hyperglycemia-induced tissue dysfunction. Current treatments fail to completely eliminate infection due to the diffusion-reaction inhibition of biofilm, and ignore the tissue dysfunction. Here, we design a glucose-driven transformable complex, composed of calcium alginate (CaAlg) hydrogel shell and Zeolitic imidazolate framework-8 (ZIF-8) core encapsulating Glucose oxidase (GOx)/Catalase (CAT) and Minocycline (MINO), named as CaAlg@MINO/GOx/CAT/ZIF-8 (CMGCZ). The reaction product of glucose-scavenging, gluconic acid, could dissolve ZIF-8 core and transform CMGCZ from inflexible to flexible, facilitating the complex to overcome the diffusion-reaction inhibition of biofilm. Meanwhile, reduced glucose concentration could ameliorate the pyroptosis of macrophages to decrease the secretion of pro-inflammatory factors, thereby reducing inflamm-aging to alleviate periodontal dysfunction.

Drought stress (DS) is a potential abiotic stress that is substantially reducing crop productivity across the globe. Likewise, salinity stress (SS) is another serious abiotic stress that is also a major threat to global crop productivity. The rapid climate change increased the intensity of both stresses which pose a serious threat to global food security; therefore, it is urgently needed to tackle both stresses to ensure better crop production. Globally, different measures are being used to improve crop productivity under stress conditions. Among these measures, biochar (BC) has been widely used to improve soil health and promote crop yield under stress conditions. The application of BC improves soil organic matter, soil structure, soil aggregate stability, water and nutrient holding capacity, and the activity of both beneficial microbes and fungi, which leads to an appreciable increase in tolerance to both damaging and abiotic stresses. BC biochar protects membrane stability, improves water uptake, maintains nutrient homeostasis, and reduces reactive oxygen species production (ROS) through enhanced antioxidant activities, thereby substantially improving tolerance to both stresses. Moreover, BC-mediated improvements in soil properties also substantially improve photosynthetic activity, chlorophyll synthesis, gene expression, the activity of stress-responsive proteins, and maintain the osmolytes and hormonal balance, which in turn improve tolerance against osmotic and ionic stresses. In conclusion, BC could be a promising amendment to bring tolerance against both drought and salinity stresses. Therefore, in the present review, we have discussed various mechanisms through which BC improves drought and salt tolerance. This review will help readers to learn more about the role of biochar in causing drought and salinity stress in plants, and it will also provide new suggestions on how this current knowledge about biochar can be used to develop drought and salinity tolerance.

In SAR defocused ship images, the defocusing phenomenon of some ship targets is space-variant along the distance. In this context, a novel autofocus algorithm combining the adaptive momentum estimation optimizer and space-variant minimum entropy criteria is proposed to address these defocused ship targets. The algorithm can directly process complex images and compensate for any order phase errors. The effectiveness of the proposed method is proved by the experimental results on the simulation data and GF-3 data. Moreover, the entropy and contrast of the refocused image have been improved, and the focusing speed of the algorithm has been greatly enhanced.

Abstract Background The status of vaginal microbiota in persistent high-risk human papilloma virus (HR-HPV) infection is unclear. The present work aimed to identify the vaginal microbiota of persistent HPV infection and explore the possible underlying microbiota factors. Methods A total of 100 women were recruited in this study, of which 28 presented HR-HPV persistent infection (P group), 30 showed clearance of any subtype of HR-HPV (C group), and 42 had no history of any HR-HPV infection (NC group). The vaginal microbiota and the community structure of the three groups were compared based on the 16S rRNA sequencing of the V3–V4 region. The microbiota diversity and differential analysis were carried out to detect the potential factors associated with HR-HPV infection. Results P and C groups showed an increase of Firmicutes and Actinobacteriota but a decrease in Proteobacteria compared to the NC group. The Chao1 index indicated that the microbial richness of the NC group was greater than C group (P

Remote sensing images play a crucial role in remote sensing target detection and 3D remote sensing modeling, and the enhancement of resolution holds significant application implications. The task of remote sensing target detection requires a substantial amount of high-resolution remote sensing images, while 3D reconstruction tasks generate denser models from diverse view perspectives. However, high-resolution remote sensing images are often limited due to their high acquisition costs, a scarcity of acquisition views, and restricted view perspective variations, which pose challenges for remote sensing tasks. In this paper, we propose an advanced method for a high-resolution remote sensing novel view synthesis by integrating attention mechanisms with neural radiance fields to address the scarcity of high-resolution remote sensing images. To enhance the relationships between sampled points and rays and to improve the 3D implicit model representation capability of the network, we introduce a point attention module and batch attention module into the proposed framework. Additionally, a frequency-weighted position encoding strategy is proposed to determine the significance of each frequency for position encoding. The proposed method is evaluated on the LEVIR-NVS dataset and demonstrates superior performance in quality assessment metrics and visual effects compared to baseline NeRF (Neural Radiance Fields) and ImMPI (Implicit Multi-plane Images). Overall, this work presents a promising approach for a remote sensing novel view synthesis by leveraging attention mechanisms and frequency-weighted position encoding.

Cinnamomum plants are rich in natural essential oils, which are widely used as materials in the fragrance, insecticidal, antibacterial agent, pharmaceutical, and food industries; however, few studies have investigated the essential oil components of Cinnamomum bodinieri. Therefore, this study investigated the diversity of essential oils from the leaves of 885 individual C. bodinieri plants across 32 populations in five provinces. Essential oils were extracted by hydrodistillation, and then qualitative and quantitative analyses of the compounds were performed by GC-MS and GC-FID. A total of 87 chemical constituents were identified in the essential oils, including 33 monoterpenes, 48 sesquiterpenes, and six other compounds. The average oil yield was 0.75%, and individual oil yields ranged from 0.01% to 4.28%. A total of 16 chemotypes were classified according to variations in the essential oil chemical constituents of C. bodinieri, among which the camphor-type, citral-type, and eucalyptol-type were dominant. Moreover, the borneol-type, cymol-type, elemol-type, methylisoeugenol-type, and selina-6-en-4-ol-type were reported in C. bodinieri for the first time. The yield and principal components of the essential oils were mainly affected by altitude, temperature, and sunshine duration, among which altitude had the most significant effect; thus, low-altitude areas are more suitable for the synthesis and accumulation of essential oils. Based on the different characteristics of the essential oils in the leaves of C. bodinieri, several excellent populations and individuals were identified in this study. Moreover, the findings provide a foundation for breeding superior varieties and studying essential oil biosynthesis mechanisms in the future.

The unreasonable economic development model of human beings has caused the environmental pollution problem to become increasingly serious. In order to achieve a positive relationship and interaction between environmental regulation, research and development (R&D) investment, and green technology innovation, and effectively solve the "strange circle" problem between high-quality economic development and environmental pollution in China and even the world, this paper takes the panel data of industrial enterprises above designated size in Chinese mainland 31 provinces from 2009 to 2019 as a research sample. The comprehensive index of R&D investment and green technology innovation was established by the entropy method, and the panel vector autoregressive (PVAR) model was constructed from the dynamic endogenous perspective, and the dynamic interaction and regional heterogeneity between environmental regulation, R&D investment, and green technology innovation were empirically analyzed by using impulse response function and variance decomposition. We obtain the following findings: (1) Environmental regulation has a two-way interaction relationship with R&D investment and green technology innovation, and R&D investment has a promotion effect on the "green degree" of technological innovation, but its role is still weak and has lagging characteristics. (2) There is significant regional heterogeneity in the dynamic responses of the eastern, central and western parts of China. (3) In the long run, environmental regulation has a "negative crowding out effect" on R&D investment in the central region, and the phenomenon of "central collapse" still exists but will gradually weaken. Environmental regulation has a "positive innovation compensation effect" on green technology innovation. Green technology innovation and R&D investment have an obvious "Pareto improvement" effect on environmental regulation, especially in the eastern region. The conclusions of this study help to clarify the dynamic interaction between environmental regulation, R&D investment, and green technology innovation, further improve environmental regulatory policies and green technology innovation R&D decision-making, and provide an effective way to achieve green and sustainable development in China and other parts of the world.

Ischemic stroke is a major condition that remains extremely problematic to treat. A cerebral reperfusion injury becomes apparent after an ischemic accident when reoxygenation of the afflicted area produces pathological side effects that are different than those induced by the initial oxygen and nutrient deprivation insult. Pyroptosis is a form of lytic programmed cell death that is distinct from apoptosis, which is initiated by inflammasomes and depends on the activation of Caspase-1. Then, Caspase-1 mobilizes the N-domain of gasdermin D (GSDMD), resulting in the release of cytokines, such as interleukin-1β (IL-1β) and interleukin-18 (IL-18). X-box binding protein l (XBP-1) is activated under endoplasmic reticulum (ER) stress to form an important transcription factor XBP-1 splicing (XBP-1s). The cerebral ischemia/reperfusion (CI/R) causes cytotoxicity, which correlates with the activation of splicing XBP-1 mRNA and NLRP3 (NOD-, LRR-, and pyrin domain-containing 3) inflammasomes, along with increases in the expression and secretion of proinflammatory cytokines and upregulation of pyroptosis-related genes in HT22 cells and in the middle cerebral artery occlusion (MCAO) rat model. However, whether XBP-1 plays a role in regulating pyroptosis involved in CI/R is still unknown. Our present study showed that behavior deficits, cerebral ischemic lesions, and neuronal death resulted from CI/R. CI/R increased the mRNA level of XBP-1s, NLRP3, IL-1β, and IL-18 and the expressions of XBP-1s, NLRP3, Caspase-1, GSDMD-N, IL-1β, and IL-18. We further repeated this process in HT22 cells and C8-B4 cells and found that OGD/R decreased cell viability and increased LDH release, XBP-1s, NLRP3, Caspase-1, GSDMD-N, IL-1β, IL-18, and especially the ratio of pyroptosis, which were reversed by Z-YVAD-FMK and downregulated XBP-1. Our results suggest that downregulated XBP-1 inhibited pyroptosis through the classical NLRP3/Caspase-1/GSDMD pathway to protect the neurons.

Background: The vulvar lichen simplex chronicus (VLSC) is a common condition in gynecologic clinics. Though VLSC is not life-threatening, it usually causes pruritus, soreness and dispareunia, which cause general discomfort. The exact etiology of VLSC is unclear. This study was performed to explore the vaginal microbiota of VLSC and to identify the possible microbial factors in attacks. Methods: Ninety women were recruited. 45 patients with VLSC and 45 women without vulvar symptom were identified as lichen simplex chronicus (LSC) and H groups respectively. The vaginal microbiota of the two arms were compared by the V3-V4 region of 16S rRNA sequencing. Results: The LSC group had less alpha diversity than H group (p < 0.05) and the beta diversity of LSC group was also distinct from the H group. Linear discriminant analysis effect size (LEfSE) analysis indicated that genus Sneathia and family Leptotrichiaceae were discriminant taxa in LSC group. Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis found that microbial genes related to the signal transduction, metabolism of terpenoids and polyketides, transporters, nervous system, energy metabolism and others were different in the LSC and H groups. Conclusions: VLSC was associated with dysbioss of vaginal microbiota profiles compared with healthy control cases.

Autophagy is closely related to the growth and drug resistance of cancer cells, and autophagy related 4B (ATG4B) performs a crucial role in the process of autophagy. The long non-coding RNA (lncRNA) colorectal neoplasia differentially expressed (CRNDE) promotes the progression of hepatocellular carcinoma (HCC), but it is unclear whether the tumor-promoting effect of CRNDE is associated with the regulation of ATG4B and autophagy. Herein, we for the first time demonstrated that CRNDE triggered autophagy via upregulating ATG4B in HCC cells. Mechanistically, CRNDE enhanced the stability of ATG4B mRNA by sequestrating miR-543, leading to the elevation of ATG4B and autophagy in HCC cells. Moreover, sorafenib induced CRNDE and ATG4B as well as autophagy in HCC cells. Knockdown of CRNDE sensitized HCC cells to sorafenib in vitro and in vivo. Collectively, these results reveal that CRNDE drives ATG4B-mediated autophagy, which attenuates the sensitivity of sorafenib in HCC cells, suggesting that the pathway CRNDE/ATG4B/autophagy may be a novel target to develop sensitizing measures of sorafenib in HCC treatment.

Due to synthetic aperture radar (SAR) imaging geometry and scattering characteristics, there are some low coherence areas in interferometric SAR (InSAR) data, such as shadow and water, where the interferometric phase is discontinuous. Therefore, it is difficult to prevent the phase error spreading in the process of phase unwrapping. In this article, the characteristics of the interferometric phase in the shadow and water areas are theoretically analyzed, respectively. According to the analysis, instead of the usual integral path setting strategy, a novel unwrapping method based on the phase compensation is proposed to avoid the unwrapping error. Besides, a fast implementation method of the quality-guided unwrapping algorithm based on the heap sort is proposed to improve the efficiency. The experiment by real airborne InSAR data validates the effectiveness of the proposed method.

Alzheimer’s disease (AD) is a common neurodegenerative disorder that places a heavy burden on patients and society. Hippocampal neuronal loss is a hallmark of AD progression. Therefore, understanding the mechanism underlying hippocampal neuronal death would be of great importance for the diagnosis and treatment of AD. This study aimed to explore the molecular mechanism via which nuclear factor kappa β (NF-κB) promotes hippocampal neuronal oxidative stress and pyroptosis in AD. We collected serum samples from 101 healthy elderly people and 112 patients with AD at the Affiliated Hospital of Kunming University of Science and Technology between January 2017 and January 2020. Commercially available human hippocampal neurons (HHNs) were used to establish an AD model (AD-HHN) following Aβ25–35 treatment. The mRNA expression levels of NF-κB and pyroptosis markers [NLR family pyrin domain-containing 3, caspase-1, interleukin (IL)-1β, and interleukin-18] mRNA and the expression level of miR-146a-5p in the serum samples of patients with AD and AD-HHNs were determined by quantitative reverse transcription polymerase chain reaction. Oxidative stress indices (reactive oxygen species, malondialdehyde, nicotinamide adenine dinucleotide phosphate, superoxide dismutase, glutathione, and catalase) were measured by Enzyme-Linked Immunosorbent Assay (ELISA). The expression of proteins [NF-κB, TP53-induced glycolysis and apoptosis regulator (TIGAR), and pyroptosis markers] was tested by western blotting. The relationship between miR-146a-5p and TIGAR was investigated using a dual luciferase reporter gene assay. We found that NF-κB and miR-146a-5p were highly expressed, while TIGAR was low expressed in patients with AD and AD-HHNs. In addition, there was a significant positive correlation between the expression levels of NF-κB and miR-146a-5p, but a negative correlation between NF-κB mRNA and TIGAR mRNA in patients with AD, as well as miR-146a-5p and TIGAR mRNA in patients with AD. In AD-HNNs, miR-146a-5p targeted and downregulated the expression of TIGAR. Knockdown of NF-κB or overexpression of TIGAR markedly attenuated oxidative stress and pyroptosis in AD-HHNs, while concurrent overexpression of miR-146a-5p inhibited these effects. In conclusion, NF-κB-induced upregulation of miR-146a-5p promoted oxidative stress and pyroptosis in AD-HNNs by targeting TIGAR.

Blind image deblurring is a long-standing challenge in remote sensing image restoration tasks. It aims to recover a latent sharp image from a blurry image while the blur kernel is unknown. To solve this problem, many image priors-based algorithms and learning-based algorithms have been proposed. However, most of these methods are based on a single blurry image. Due to the lack of high frequency information, the images restored by these algorithms still have some deficiencies in edge and texture details. In this work, we propose a novel deep learning model named Reference-Based Multi-Level Features Fusion Deblurring Network (Ref-MFFDN), which registers the reference image and the blurry image in the multi-level feature space and transfers the high-quality textures from registered reference features to assist image deblurring. Comparative experiments on the testing set prove that our Ref-MFFDN outperforms many state-of-the-art single image deblurring approaches in both quantitative evaluation and visual results, which indicates the effectiveness of using reference images in remote sensing image deblurring tasks. More ablation experiments demonstrates the robustness of Ref-MFFDN to the input image size, the effectiveness of multi-level features fusion network (MFFN) and the effect of different feature levels in multi-feature extractor (MFE) on algorithm performance.

Few-shot learning has achieved great success in computer vision. However, when applied to Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR), it tends to demonstrate a bad performance due to the ignorance of the differences between SAR images and optical ones. What is more, the same transformation on both images may cause different results, even some unexpected noise. In this paper, we propose an improved Prototypical Network (PN) based on Spatial Transformation, also known as ST-PN. Cascaded after the last convolutional layer, a spatial transformer module implements a feature-wise alignment rather than a pixel-wise one, so more semantic information can be exploited. In addition, there is always a huge divergence even for the same target when it comes to pixel-wise alignment. Moreover, it reduces computational cost with fewer parameters of the deeper layer. Here, a rotation transformation is used to reduce the discrepancies caused by different observation angles of the same class. Thefinal comparison of four extra losses indicates that a single cross-entropy loss is good enough to calculate the loss of distances. Our work achieves state-of-the-art performance on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset.

Few-shot synthetic aperture radar automatic target recognition (SAR-ATR) aims to recognize the targets of the images (query images) based on a few annotated images (support images). Such a task requires modeling the relationship between the query and support images. In this paper, we propose the instance-aware transformer (IAT) model. The IAT exploits the power of all instances by constructing the attention map based on the similarities between the query feature and all support features. The query feature aggregates the support features based on the attention values. To align the features of the query and support images in IAT, the shared cross-transformer keep all the projections in the module shared across all features. Instance cosine distance is used in training to minimize the distance between the query feature and the support features. In testing, to fuse the support features of the same class into the class representation, Euclidean (Cosine) Loss is used to calculate the query-class distances. Experiments on the two proposed few-shot SAR-ATR test sets based on MSTAR demonstrate the superiority of the proposed method.

SAR tomography (TomoSAR) is an important technology for three-dimensional (3D) reconstruction of buildings through multiple coherent SAR images. In order to obtain sufficient signal-to-noise ratio (SNR), typical TomoSAR applications often require dozens of scenes of SAR images. However, limited by time and cost, the available SAR images are often only 3–5 scenes in practice, which makes the traditional TomoSAR technique unable to produce satisfactory SNR and elevation resolution. To tackle this problem, the conditional generative adversarial network (CGAN) is proposed to improve the TomoSAR 3D reconstruction by learning the prior information of building. Moreover, the number of tracks required can be reduced to three. Firstly, a TomoSAR 3D super-resolution dataset is constructed using high-quality data from the airborne array and low-quality data obtained from a small amount of tracks sampled from all observations. Then, the CGAN model is trained to estimate the corresponding high-quality result from the low-quality input. Airborne data experiments prove that the reconstruction results are improved in areas with and without overlap, both qualitatively and quantitatively. Furthermore, the network pretrained on the airborne dataset is directly used to process the spaceborne dataset without any tuning, and generates satisfactory results, proving the effectiveness and robustness of our method. The comparative experiment with nonlocal algorithm also shows that the proposed method has better height estimation and higher time efficiency.

The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods.

Object localization is an important application of remote sensing images and the basis of information extraction. The acquired accuracy is the key factor to improve the accuracy of object structure information inversion. The floating roof oil tank is a typical cylindrical artificial object, and its top cover fluctuates up and down with the change in oil storage. Taking the oil tank as an example, this study explores the localization by combining the traditional feature parameter method and convolutional neural networks (CNNs). In this study, an improved fast radial symmetry transform (FRST) algorithm called fast gradient modulus radial symmetry transform (FGMRST) is proposed and an approach based on FGMRST combined with CNN is proposed. It effectively adds the priori of circle features to the calculation process. Compared with only using CNN, it achieves higher precision localization with fewer network layers. The experimental results based on SkySat data show that the method can effectively improve the calculation accuracy and efficiency of the same order of magnitude network, and by increasing the network depth, the accuracy still has a significant improvement.

Ischemic postconditioning (PC) is proved to efficiently protect diabetic patients with acute myocardial infarction from ischemia-reperfusion injury. We aimed to explore the protective roles of ischemic PC on diabetic retinopathy in tree shrews with diabetic cerebral ischemia. A diabetic tree shrew model was established through high-fat diet feeding combined with streptozotocin (STZ) injection, while cortical thrombotic cerebral ischemia was induced photochemically. Tree shrews were divided into the normal control group, sham operation group, diabetes mellitus group, diabetes mellitus+cerebral ischemia group, and diabetes mellitus+cerebral ischemia+PC group (in which the tree shrews with diabetic cerebral ischemia were treated with ischemic PC). H&E staining was used to examine the pathological changes in the retina, and immunohistochemistry was performed to determine the retinal expression of VEGF (vascular endothelial growth factor). The modeling resulted in 77% tree shrews with diabetes. Ischemic PC reduced the blood glucose levels in the tree shrews with diabetic cerebral ischemia. Tree shrews with diabetes had thinned retina with disordered structures, and these pathological changes were aggravated after cerebral ischemia. The retinopathy was alleviated after ischemic PC. Retina expression of VEGF was mainly distributed in the ganglion cell layer in tree shrews. Diabetes and cerebral ischemia increased retinal VEGF expression in a step-wise manner, while additional ischemic PC reduced retinal VEGF expression. Therefore, ischemic PC effectively alleviates retinopathy in tree shrews with diabetic cerebral ischemia, and this effect is associated with reduced retinal VEGF expression.

In this work, a novel approach for the simulation of synthetic aperture radar (SAR) images is proposed. Also, this approach aims to interpret the mechanisms of the dominated scattering centres in the SAR image. An attributed model based on typical structures is used to characterise the 3D geometry of the targets. The physical optics and the geometry optics are used to calculate the main scattering mechanisms of the specified typical structures. Also, based on scattering properties and the imaging process, the simulation approach is set up. The experiments based on the Terra-SAR data of an airplane are implemented to test the validity of the simulation. The work presented would be useful for SAR automatic target recognition.

Inflammatory biomarkers are closely related to infectious diseases. However, traditional clinical tests of laboratory inspection are unable to achieve rapid and accurate detection of these biomarkers on-site due to shortcomings such as complex experimental operation, expensive equipment, and long test time. Herein, we proposed a lateral flow assay (LFA) strip based on surface-enhanced Raman scattering (SERS) nanotags (SERS-LFA strips) for the simultaneous and quantitative detection of dual infection biomarkers, serum amyloid A (SAA) and C-reactive protein (CRP), respectively. In practice, mesoporous silica (mSiO2)-coated Au nanoparticles (Au NPs) were used as the SERS substrate. Mercaptobenzoic acid (MBA) was embedded in the internal gap between Au NPs and the mSiO2 shell to prepare AuMBA@mSiO2 NPs, onto which SAA and CRP antibodies were modified to prepare two AuMBA@mSiO2 SERS nanotags. The Raman intensities of the test and control lines were simultaneously identified for the qualitative detection of SAA and CRP, with limits of detection (LODs) as low as 0.1 and 0.05 ng/mL for SAA and CRP, respectively. Finally, aiming at point-of-care testing (POCT) applications, we used a smartphone-based portable Raman spectrometer to quantitatively analyze the SERS-LFA strips. The Raman signal could still be accurately detected when the concentration of SAA and CRP was 10 ng/mL, which is lower than the LOD required in clinical practice for most diseases. Therefore, taking into account its simple operation and short analysis time, by using a portable Raman spectrometer which can be equipped with a 5G cloud-based healthcare management system, the current strategy based on SERS-LFA provides the potential for the quick and on-site diagnosis of infectious diseases such as sepsis, which is of great significance for medical guidance on the treatment of widely spread infection-related diseases in remote areas that lack well-developed medical resources.

Convolutional Neural Network (CNN) models are widely used in supervised Polarimetric Synthetic Aperture Radar (PolSAR) image classification. They are powerful tools to capture the non-linear dependency between adjacent pixels and outperform traditional methods on various benchmarks. On the contrary, research works investigating unsupervised PolSAR classification are quite rare, because most CNN models need to be trained with labeled data. In this paper, we propose a completely unsupervised model by fusing the Convolutional Autoencoder (CAE) with Vector Quantization (VQ). An auxiliary Gaussian smoothing loss is adopted for better semantic consistency in the output classification map. Qualitative and quantitative experiments are carried out on satellite and airborne full polarization data (RadarSat2/E-SAR, AIRSAR). The proposed model achieves 91.87%, 83.58% and 96.93% overall accuracy (OA) on the three datasets, which are much higher than the traditional H/alpha-Wishart method, and it exhibits better visual quality as well.

Synthetic aperture radar tomography (TomoSAR) is an important 3D mapping method. Traditional TomoSAR requires a large number of observation orbits however, it is hard to meet the requirement of massive orbits. While on the one hand, this is due to funding constraints, on the other hand, because the target scene is changing over time and each observation orbit consumes lots of time, the number of orbits can be fewer as required within a narrow time window. When the number of observation orbits is insufficient, the signal-to-noise ratio (SNR), peak-to-sidelobe ratio (PSR), and resolution of 3D reconstruction results will decline severely, which seriously limits the practical application of TomoSAR. In order to solve this problem, we propose to use a deep learning network to improve the resolution and SNR of 3D reconstruction results under the condition of very few observation orbits by learning the prior distribution of targets. We use all available orbits to reconstruct a high resolution target, while only very few (around 3) orbits to reconstruct a low resolution input. The low-res and high-res 3D voxel-grid pairs are used to train a 3D super-resolution (SR) CNN (convolutional neural network) model, just like ordinary 2D image SR tasks. Experiments on the Civilian Vehicle Radar dataset show that the proposed deep learning algorithm can effectively improve the reconstruction both in quality and in quantity. In addition, the model also shows good generalization performance for targets not shown in the training set.

In this work, 1vol.%, 3vol.% and 5vol.% TiBw/Ti60 composites were fabricated by spark plasma sintering (SPS) at 1150 °C with Ti60 powder and B powder. Scanning electron microscopy (SEM) confirmed that micro-nano enhancement TiBw can be obtained by SPS and the network structure were formed in the 3vol.% TiBw/Ti60 composites. The tensile properties of composites with different volume fraction at 600 °C show that 3vol.% TiBw/Ti60 composites have optimum comprehensive mechanical properties. The microstructure of thermal deformation of the 3vol.% TiBw/Ti60 composites shows that the matrix grains recrystallized at 1050 °C and the grains were refined. The tensile strength and elongation of the composites increased at first and then decreased with the increase of thermal deformation temperature. Highest values for flexural strength (∼752 MPa) and elongation (∼7.03%) were attained at 1050 °C, respectively. The strengthening mechanism can be mainly attributed to the directional arrangement of reinforcements and the grain refinement of the matrix.

Despite developed long time ago, plaque assay is still the gold standard for viral titer quantification in modern virology. The standard crystal violet-based plaque assay relies on virus’ ability to induce cytopathic effect (CPE) which limits the assay to lytic viruses. Alternative viral quantification assays such as 50% tissue culture infectious assay (TCID50) and genetic material quantification by Q-PCR provide a different way of viral quantification with their own shortcoming. In here, we modified the fluorescent focus assay and developed an antibody-based immunoplaque assay which provides a reliable and reproducible viral quantification independent of CPE. Our assay not only allows accurate determination of viral titer, but also provides information on viral kinetics, genetic stability and purity of the virus population.