Sea ice, as an important component of the Earth's ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has ...important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model's generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness determined using Sentinel-1 data.
Periostin (POSTN) is a multifunctional extracellular component that regulates cell-matrix interactions and cell-cell crosstalk. POSTN deletion significantly decreases leukemia burden in mice; ...however, the underlying mechanisms by which POSTN promotes B cell acute lymphoblastic leukemia (B-ALL) progression remain largely unknown. Here, we demonstrate that bone marrow (BM)-derived mesenchymal stromal cells (BM-MSCs) express higher levels of POSTN when co-cultured with B-ALL cells in vitro and in vivo. POSTN deficiency in BM-MSCs significantly decreases CCL2 expression in co-cultured B-ALL cells in vitro and in vivo. Moreover, POSTN treatment increases expression of CCL2 in B-ALL cells by activating the integrin-ILK-NF-κB pathway. Conversely, CCL2 treatment upregulates expression of POSTN in BM-MSCs via STAT3 activation. Furthermore, there is a positive correlation between POSTN expression and CCL2 level in the BM of mice and patients with B-ALL. These findings suggest that B-ALL cell-derived CCL2 contributes to the increased leukemia burden promoted by BM-MSC-derived POSTN.
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•B-ALL cells increase periostin level in co-cultured BM-MSCs in vitro and in vivo•BM-MSC-derived periostin promotes B-ALL cell proliferation via NF-κB-CCL2 pathway•B-ALL cell-derived CCL2 increases periostin level in BM-MSCs via STAT3 activation•Periostin is positively correlated with CCL2 in BM of mice and patients with B-ALL
Ma et al. show that BM-MSC-derived periostin promotes leukemia progression by activating the integrin-ILK-NF-κB-CCL2 pathway in leukemia cells and that leukemia cell-derived CCL2 increases periostin expression in BM-MSCs by activating STAT3. This work identifies critical crosstalk between leukemia cells and stromal cells via periostin and CCL2 in B-ALL progression.
Low-cost camera calibration is vital in air and underwater photogrammetric applications. However, various lens distortions and the underwater environment influence are difficult to be covered by a ...universal distortion compensation model, and the residual distortions may still remain after conventional calibration. In this paper, we propose a combined physical and mathematical camera calibration method for low-cost cameras, which can adapt to both in-air and underwater environments. The commonly used physical distortion models are integrated to describe the image distortions. The combination is a high-order polynomial, which can be considered as basis functions to successively approximate the image deformation from the point of view of mathematical approximation. The calibration process is repeated until certain criteria are met and the distortions are reduced to a minimum. At the end, several sets of distortion parameters and stable camera interior orientation (IO) parameters act as the final camera calibration results. The Canon and GoPro in-air calibration experiments show that GoPro owns distortions seven times larger than Canon. Most Canon distortions have been described with the Australis model, while most decentering distortions for GoPro still exist. Using the proposed method, all the Canon and GoPro distortions are decreased to close to 0 after four calibrations. Meanwhile, the stable camera IO parameters are obtained. The GoPro Hero 5 Black underwater calibration indicates that four sets of distortion parameters and stable camera IO parameters are obtained after four calibrations. The camera calibration results show a difference between the underwater environment and air owing to the refractive and asymmetric environment effects. In summary, the proposed method improves the accuracy compared with the conventional method, which could be a flexible way to calibrate low-cost cameras for high accurate in-air and underwater measurement and 3D modeling applications.
Ovarian cancer is a gynecological tumor with an incidence rate lower than those of other gynecological tumor types and the second-highest death rate. CC chemokine 2 (CCL2) is a multifunctional factor ...associated with the progression of numerous cancers. However, the effect of CCL2 on ovarian cancer progression is unclear. Here, we found that exogenous CCL2 and the overexpression of CCL2 promoted the proliferation and metastasis of ovarian cancer cells. On the other hand, CCL2 knockdown via CRISPR/Cas9 inhibited ovarian cancer cell proliferation, migration, and invasion. The present study demonstrated that mitogen-activated protein three kinase 19 (MAP3K19) was the key CCL2 target for regulating ovarian cancer progression through transcriptome sequencing. Additionally, MAP3K19 knockout inhibited ovarian cancer cell proliferation, migration, and invasion. Furthermore, CCL2 increased MAP3K19 expression by activating the mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) pathway. The present study showed the correlation between CCL2 and ovarian cancer, suggesting that CCL2 may be a novel target for ovarian cancer therapy.
The application potential of very high resolution (VHR) remote sensing imagery has been boosted by recent developments in the data acquisition and processing ability of aerial photogrammetry. ...However, shadows in images contribute to problems such as incomplete spectral information, lower intensity brightness, and fuzzy boundaries, which seriously affect the efficiency of the image interpretation. In this paper, to address these issues, a simple and automatic method of shadow detection is presented. The proposed method combines the advantages of the property-based and geometric-based methods to automatically detect the shadowed areas in VHR imagery. A geometric model of the scene and the solar position are used to delineate the shadowed and non-shadowed areas in the VHR image. A matting method is then applied to the image to refine the shadow mask. Different types of shadowed aerial orthoimages were used to verify the effectiveness of the proposed shadow detection method, and the results were compared with the results obtained by two state-of-the-art methods. The overall accuracy of the proposed method on the three tests was around 90%, confirming the effectiveness and robustness of the new method for detecting fine shadows, without any human input. The proposed method also performs better in detecting shadows in areas with water than the other two methods.
Aiming at the common problems, such as noise pollution, low contrast, and color distortion in underwater images, and the characteristics of holothurian recognition, such as morphological ambiguity, ...high similarity with the background, and coexistence of special ecological scenes, this paper proposes an underwater holothurian target-detection algorithm (FA-CenterNet), based on improved CenterNet and scene feature fusion. First, to reduce the model’s occupancy of embedded device resources, we use EfficientNet-B3 as the backbone network to reduce the model’s Params and FLOPs. At the same time, EfficientNet-B3 increases the depth and width of the model, which improves the accuracy of the model. Then, we design an effective FPT (feature pyramid transformer) combination module to fully focus and mine the information on holothurian ecological scenarios of different scales and spaces (e.g., holothurian spines, reefs, and waterweeds are often present in the same scenario as holothurians). The co-existing scene information can be used as auxiliary features to detect holothurians, which can improve the detection ability of fuzzy and small-sized holothurians. Finally, we add the AFF module to realize the deep fusion of the shallow-detail and high-level semantic features of holothurians. The results show that the method presented in this paper yields better results on the 2020 CURPC underwater target-detection image dataset with an AP50 of 83.43%, Params of 15.90 M, and FLOPs of 25.12 G compared to other methods. In the underwater holothurian-detection task, this method improves the accuracy of detecting holothurians with fuzzy features, a small size, and dense scene. It also achieves a good balance between detection accuracy, Params, and FLOPs, and is suitable for underwater holothurian detection in most situations.
The early detection of major depression in elderly individuals who are at risk of developing the disease is of prime importance when it comes to the prevention of geriatric depression. We used ...resting-state functional magnetic resonance imaging (fMRI) to examine changes in regional homogeneity (ReHo) of spontaneous activity in late-life subthreshold depression (StD), and we evaluated the sensitivity/specificity performance of these changes. Nineteen elderly individuals with StD and 18 elderly controls underwent a resting-state fMRI scan. The ReHo approach was employed to examine whether StD was related to alterations in resting-state neural activity, in the form of abnormal regional synchronization. Receiver operating characteristic curve analysis and the Fisher stepwise discriminant analysis were used to evaluate the sensitivity/specificity characteristics of the ReHo index in discriminating between the StD subjects and normal controls. The results demonstrated that, compared to controls, StD subjects display lower ReHo in the right orbitofrontal cortex (OFC), left dorsolateral prefrontal cortex (DLPFC), left postcentral gyrus (PCG), and left middle frontal and inferior temporal gyri, as well as higher ReHo in the bilateral insula and right DLPFC. The left PCG and the right DLPFC, OFC, and posterior insula, together reported a predictive accuracy of 91.9%. These results suggest that the regional activity coherence was changed in the resting brain of StD subjects, and that these alterations may serve as potential markers for the early detection of StD in late-life depression.
Coronavirus disease 2019 (COVID-19) is an epidemic respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can cause infections in millions of individuals, who ...can develop lung injury, organ failure, and subsequent death. As the first line of host defense, the innate immune system is involved in initiating the immune response to SARS-CoV-2 infection and the hyperinflammatory phenotype of COVID-19. However, the interplay between SARS-CoV-2 and host innate immunity is not yet well understood. It had become known that the cGAS-STING pathway is involved in the detection of cytosolic DNA, which elicits an innate immune response involving a robust type I interferon response against viral and bacterial infections. Nevertheless, several lines of evidence indicate that SARS-CoV-2, a single-stranded positive-sense RNA virus, triggered the cGAS-STING signaling pathway. Therefore, understanding the molecular and cellular details of cGAS-STING signaling upon SARS-CoV-2 infection is of considerable biomedical importance. In this review, we discuss the role of cGAS-STING signaling in SARS-CoV-2 infection and summarize the potential therapeutics of STING agonists as virus vaccine adjuvants.
In the coastal areas of China, the eutrophication of seawater leads to the continuous occurrence of red tide, which has caused great damage to Marine fisheries and aquatic resources. Therefore, the ...detection and prediction of red tide have important research significance. The rapid development of optical remote sensing technology and deep-learning technology provides technical means for realizing large-scale and high-precision red tide detection. However, the difficulty of the accurate detection of red tide edges with complex boundaries limits the further improvement of red tide detection accuracy. In view of the above problems, this paper takes GOCI data in the East China Sea as an example and proposes an improved U-Net red tide detection method. In the improved U-Net method, NDVI was introduced to enhance the characteristic information of the red tide to improve the separability between the red tide and seawater. At the same time, the ECA channel attention mechanism was introduced to give different weights according to the influence of different bands on red tide detection, and the spectral characteristics of different channels were fully mined to further extract red tide characteristics. A shallow feature extraction module based on Atrous Spatial Pyramid Convolution (ASPC) was designed to improve the U-Net model. The red tide feature information in a multi-scale context was fused under multiple sampling rates to enhance the model’s ability to extract features at different scales. The problem of limited accuracy improvement in red tide edge detection with complex boundaries is solved via the fusion of deep and shallow features and multi-scale spatial features. Compared with other methods, the method proposed in this paper achieves better results and can detect red tide edges with complex boundaries, and the accuracy, precision, recall, and F1-score are 95.90%, 97.15%, 91.53%, and 0.94, respectively. In addition, the red tide detection experiments in other regions with relatively concentrated distribution also prove that the method has good applicability.
Accurate habitat prediction is important to improve fishing efficiency. Most of the current habitat-prediction studies use the single-source datasets and the sequence model based on single-source ...datasets, which, to a certain extent, limits the further improvement of prediction accuracy. In this paper, we propose a habitat-prediction method based on the multi-source heterogeneous remote-sensing data fusion, using product-level remote-sensing data and L1B-level original remote-sensing data. We designed a heterogeneous data feature extraction model based on a Convolution Neural Network (CNN) and Long and Short-Term Memory network (LSTM), and we designed a decision-fusion model based on multi-source heterogeneous data feature extraction. In the habitat prediction for the Northwest Pacific Saury, the mean R2 of the model reaches 0.9901 and the RMSE decreases to 0.01588 in the model validation experiment. It is significantly better than the results of other models, with the single datasets as input. Moreover, the model performs well in the generalization experiment because we limited the prediction error to less than 8%. Compared with the single-source sequence network model in the existing literature, the proposed method in this paper solves the problem of ineffective fusion caused by the differences in the structure and size of heterogeneous data through multilevel feature fusion and decision fusion, and it deeply explores the features of remote-sensing fishery data with different data structures and sizes. It can effectively improve the accuracy of fishery prediction, proving the feasibility and advancement of using multi-source remote-sensing data for habitat prediction. It also provides new methods and ideas for future research in the field of habitat prediction.
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