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It is of great significance to apply the object detection methods to automatically detect boulders from planetary images and analyze their distribution. This contributes to the selection of candidate landing sites and the understanding of the geological processes. This paper improves the state-of-the-art object detection method of YOLOv5 with attention mechanism and designs a pyramid based approach to detect boulders from planetary images. A new feature fusion layer has been designed to capture more shallow features of the small boulders. The attention modules implemented by combining the convolutional block attention module (CBAM) and efficient channel attention network (ECA-Net) are also added into YOLOv5 to highlight the information that contribute to boulder detection. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset which is widely used for object detection evaluations and the boulder dataset that we constructed from the images of Bennu asteroid, the evaluation results have shown that the improvements have increased the performance of YOLOv5 by 3.4% in precision. With the improved YOLOv5 detection method, the pyramid based approach extracts several layers of images with different resolutions from the large planetary images and detects boulders of different scales from different layers. We have also applied the proposed approach to detect the boulders on Bennu asteroid. The distribution of the boulders on Bennu asteroid has been analyzed and presented.

Background MicroRNAs (miRNAs) play important roles in the growth and metastasis of colon cancer. It is known that one set of miRNAs are dysregulated in colon cancer cells, but the mechanism of their role in cancer development is still largely unknown. Our study focuses on the role of miR-378 in colon cancer cells. Methods Human colon cancer tissues and adjacent non-tumor tissues were collected from patients diagnosed in pathological examinations. In addition, human colon cancer cell lines LoVo, CaCo2, SW1116, SW480 and HCT-116, and a normal colonic mucosa cell line NCM460 were included. Quantitative RT-PCR was used to detect the miR-378 level in the clinical tissues and cell lines. In SW480 and HCT-116, miR-378 was artificially overexpressed or suppressed. Cell viability and proliferation were measured using MTT and colony formation assays, and apoptosis was detected via annexin V-PI staining and flow cytometry analysis. The transwell technique was applied to detect the migration and invasion of the colon cancer cells, and their epithelial–mesenchymal transition (EMT) was evaluated by detecting EMT-associated markers using Western blotting. Bioinformatics methods were used to predict the potential targets of miR-378, and luciferase reporter assays were performed to conform the direct binding between miR-378 and its target mRNA. The activity of the Wnt/β-catenin pathway was evaluated by detecting the key factors through Western blotting. Results We found that miR-378 expression was low in colon cancer tissues and cell lines. Overexpression of miR-378 not only inhibits the proliferation of colon cancer cells in vitro by inducing apoptosis, but also inhibits migration and invasion by inhibiting the EMT of colon cancer cells. SDAD1 is a direct target gene of miR-378, and knockdown of SDAD1 suppresses the proliferation, migration and invasion of colon cancer cells. We also confirmed that miR-378 alleviated the malignant phenotypes of colon cancer cells by inhibiting the Wnt/β-catenin pathway. Conclusion miR-378 inhibits the proliferation, migration and invasion of colon cancer cells by targeting SDAD1, defining miR-378 as a potential target for the diagnosis and treatment of colon cancer.

The combination of multiple imaging methods has made an indelible contribution to the diagnosis, surgical navigation, treatment, and prognostic evaluation of various diseases. Due to the unique advantages of luminogens with aggregation-induced emission (AIE), their progress has been significant in the field of organic fluorescent contrast agents. Herein, this manuscript summarizes the recent advancements in AIE molecules as contrast agents for optical image-based dual/multi-modal imaging. We particularly focus on the exceptional properties of each material and the corresponding application in the diagnosis and treatment of diseases.

Posterior reversible encephalopathy syndrome is characterized by abnormal white matter findings and neurological symptoms. However, it is clinically rare and can easily be misdiagnosed. Herein, we describe a case of posterior reversible encephalopathy syndrome in an adult patient with acute post-streptococcal glomerulonephritis, confirmed by renal pathology and magnetic resonance imaging, and provide diagnostic suggestions for clinicians.

Despite the absence of severe bleeding, low platelet counts and a high international normalized ratio may result in a small amount of oozing, which prolongs the ER procedure duration as hemostasis must be secured. [...]the difficulty and duration of the procedure are effectively decreased. The lesion could not be excised by using EVL and may result in ischemia, necrosis, and ulceration. [...]the full specimen could not be provided for further histological examination, and the exact extent and infiltration depth of the lesion could not be determined. Similar to MBM, patients treated with EVL are also at risk of residual disease, which may lead to local recurrence. [...]EVL should not be recommended for the treatment of EEC in cirrhotic patients.

To establish the reference range of normal fetal ductus venosus pulsatility index (DV PI) and ductus venosus (DV) blood flow velocity at 11-14 weeks of gestation. Fetal ductus venosus Doppler flow was measured in singleton pregnancies attending our hospital for early pregnancy nuchal translucency (NT) screening between June 2021 and May 2022. All fetuses were followed up for pregnancy outcome using the following inclusion criteria: Singleton pregnancy; no maternal underlying diseases such as diabetes, hypertension, rheumatism, or other pregnancy complications; fetal crown-rump length (CRL) of 45 to 84 mm; normal NT screening ultrasound; no absent or reversed ductus venosus a-wave; no fetal structural abnormalities; no chromosomal abnormalities during follow-up; and good pregnancy outcome. DV PI, peak ventricular systolic velocity (S-wave), atrial systolic flow velocity (a-wave) and time-averaged maximum velocity (TAMXV) were recorded. The ductus venosus Doppler parameters of 224 fetuses which met the inclusion criteria were analysed. DV PI P5 and P95 ranged from 1.0007 and 1.3415 for a CRL of 45 mm to 0.9734 and 1.2115 for a CRL of 84 mm, indicating a statistically significant correlation with CRL. DV S-wave, a-wave, and TAMXV all increased as CRL increased, demonstrating a statistically significant correlation with CRL values. A reference range of normal fetal ductus venosus Doppler spectral parameters at 11-14 weeks was established to provide a basis for further research into the clinical value of normal and abnormal DV PI values in relation to adverse pregnancy outcomes.

In computed tomography (CT) images, the presence of metal artifacts leads to contaminated object structures. Theoretically, eliminating metal artifacts in the sinogram domain can correct projection deviation and provide reconstructed images that are more real. Contemporary methods that use deep networks for completing metal-damaged sinogram data are limited to discontinuity at the boundaries of traces, which, however, lead to secondary artifacts. This study modifies the traditional U-net and adds two sinogram feature losses of projection images—namely, continuity and consistency of projection data at each angle, improving the accuracy of the complemented sinogram data. Masking the metal traces also ensures the stability and reliability of the unaffected data during metal artifacts reduction. The projection and reconstruction results and various evaluation metrics reveal that the proposed method can accurately repair missing data and reduce metal artifacts in reconstructed CT images.

The two peaks characteristic of yellow and blue light in the spectrum of dual-wavelength white light emitting diodes (LEDs) introduce distinctive features to the interference signal of white light scanning interferometry (WLSI). The distinctive features are defined as discontinuities, so that the fringe contrast function cannot be modeled as a single Gaussian function, and causes the interferogram to have uneven distribution of fringes of different orders in the scanning interferometer. This phenomenon leads to the low accuracy of the zero-order fringe position in the envelope calculation, which affects the repeatability and accuracy of the interferometry. This paper proposes a new surface recovery algorithm based on the Hilbert phase envelope and adjacent reference points calculation, which can effectively overcome the influence of the discontinuous signal of dual-wavelength LED white light interference on the three-dimensional reconstruction of WLSI measurements. The reliability of the algorithm is verified by experiments, and the measurement accuracy of LED WLSI system is evaluated.

A parallel high-resolution underwater target detection network is proposed to address the problems of complex underwater scenes and limited target feature extraction capability. First, a high-resolution network (HRNet), a lighter high-resolution human posture estimation network, is used to improve the target feature representation and effectively reduce the semantic information lost in the image during sampling. Then, the attention module (A-CBAM) is improved to capture complex feature distributions by modeling the two-dimensional space in the activation function stage through the introduction of the flexible rectified linear units (FReLU) activation function to achieve pixel-level spatial information modeling capability. Feature enhancement in the spatial and channel dimensions is performed to improve understanding of fuzzy targets and small target objects and to better capture irregular and detailed object layouts. Finally, a receptive field augmentation module (RFAM) is constructed to obtain sufficient semantic information and rich detail information to further enhance the robustness and discrimination of features and improve the detection capability of the model for multi-scale underwater targets. Experimental results show that the method achieves 81.17%, 77.02%, and 82.9% mean average precision (mAP) on three publicly available datasets, specifically underwater robot professional contest (URPC2020, URPC2018) and pattern analysis, statistical modeling, and computational learning visual object classes (PASCAL VOC2007), respectively, demonstrating the effectiveness of the proposed network.