Search

Objective·To explore the hearing phenotype of Timm8a-/- mice and the function of translocase of inner mitochondrial membrane 8A (Timm8a) gene in inner ear by constructing Timm8a gene knockout mice.Methods·Timm8a-/- mice were designed and constructed. PCR and Western blotting were used to verify whether the construction was successful. The body size and weight of Timm8a-/- mice and wild type (WT) mice aged one-month were observed and compared. Immunofluorescence staining was used to observe the expression and distribution of TIMM8A protein in the cochlea of WT mice. Auditory brainstem response (ABR) was used to compare the hearing threshold of Timm8a-/- mice and WT mice. Toluidine blue staining was performed to observe the morphology of organ of Corti (OC), spiral ganglion neuron (SGN) and stria vascularis (SV) in the inner ear of the two kinds of mice. Transmission electron microscope was used to observe the ultrastructure of mitochondria in inner ear hair cells (HCs), SGN cells and SV cells of the two kinds of mice, and the proportion of abnormal mitochondria was counted.Results·The results of PCR showed that Timm8a gene had been successfully knocked out, and the results of Western blotting showed that there was no TIMM8A proteins in the cochlea, brain, heart and skeletal muscle tissues. The both results indicated that Timm8a-/- mice were successfully constructed. The results of immunofluorescence staining showed that TIMM8A was abundantly expressed in the cochlea of WT mice, and was highly expressed in the OC, SGN and SV. Compared with WT mice, Timm8a-/- mice aged one-month developed slower, had lighter body weight and significantly higher hearing threshold (all P

Cultivating the ability to solve complex and contradictory problems is one of the important goals of engineering education. This paper introduces extenics into the training of cultivating college students' problem-solving skills, streamlines the process of solving contradictory problems, and develops a systematic and patterned extenics model for cultivating college students' problem-solving abilities. This study verified the validity of the methodology through comparative case studies and experiments. The training model of contradictory problems extraction and resolution has implications for pedagogical innovations in other specialized courses. [ABSTRACT FROM AUTHOR]

As a new vegetation monitoring index, the KNDVI has certain advantages in characterizing the evolutionary process of regional desertification. However, there are few reports on desertification monitoring based on KNDVI and feature space models. In this study, seven feature parameters, including the kernel normalized difference vegetation index (KNDVI) and Albedo, were introduced to construct different models for desertification remote-sensing monitoring. The optimal desertification remote-sensing monitoring index model was determined with the measured data; then, the spatiotemporal evolution pattern of desertification in Gulang County from 2013 to 2023 was analyzed and revealed. The main conclusions were as follows: (1) Compared with the NDVI and MSAVI, the KNDVI showed more advantages in the characterization of the desertification evolution process. (2) The point–line pattern KNDVI-Albedo remote-sensing index model had the highest monitoring accuracy, reaching 94.93%, while the point–line pattern NDVI-TGSI remote-sensing monitoring index had the lowest accuracy of 54.38%. (3) From 2013 to 2023, the overall desertification situation in Gulang County showed a trend of improvement with a pattern of "firstly aggravation and then alleviation." Additionally, the gravity center of desertification in Gulang County first shifted to the southeast and then to the northeast, indicating that the northeast's aggravating rate of desertification was higher than in the southwest during the period. (4) From 2013 to 2023, the area of stable desertification in Gulang County was the largest, followed by the slightly weakened zone, and the most significant transition area was that of extreme desertification to severe desertification. The research results provide important decision support for the precise monitoring and governance of regional desertification. [ABSTRACT FROM AUTHOR]

Rapidly stitching unmanned aerial vehicle (UAV) imagery to produce high-resolution fast-stitch maps is key to UAV emergency mapping. However, common problems such as gaps and ghosting in image stitching remain challenging and directly affect the visual interpretation value of the imagery product. Inspired by the data characteristics of high-precision satellite images with rich access and geographic coordinates, a seamless stitching method is proposed for emergency response without the support of ground control points (CGPs) and global navigation satellite systems (GNSS). This method aims to eliminate stitching traces and solve the problem of stitching error accumulation. Firstly, satellite images are introduced to support image alignment and geographic coordinate acquisition simultaneously using matching relationships. Then a dynamic contour point set is constructed to locate the stitching region and adaptively extract the fused region of interest (FROI). Finally, the gradient weight cost map of the FROI image is computed and the Laplacian pyramid fusion rule is improved to achieve seamless production of the fast-stitch image map with geolocation information. Experimental results indicate that the method is well adapted to two representative sets of UAV images. Compared with the Laplacian pyramid fusion algorithm, the peak signal-to-noise ratio (PSNR) of the image stitching results can be improved by 31.73% on average, and the mutual information (MI) can be improved by 19.98% on average. With no reliance on CGPs or GNSS support, fast-stitch image maps are more robust in harsh environments, making them ideal for emergency mapping and security applications. [ABSTRACT FROM AUTHOR]

Project Final Report prepared for CIEE and California Energy Commission

Three-dimensional (3D) scene reconstruction plays an important role in digital cities, virtual reality, and simultaneous localization and mapping (SLAM). In contrast to perspective images, a single panoramic image can contain the complete scene information because of the wide field of view. The extraction and matching of image feature points is a critical and difficult part of 3D scene reconstruction using panoramic images. We attempted to solve this problem using convolutional neural networks (CNNs). Compared with traditional feature extraction and matching algorithms, the SuperPoint (SP) and SuperGlue (SG) algorithms have advantages for handling images with distortions. However, the rich content of panoramic images leads to a significant disadvantage of these algorithms with regard to time loss. To address this problem, we introduce the Improved Cube Projection Model: First, the panoramic image is projected into split-frame perspective images with significant overlap in six directions. Second, the SP and SG algorithms are used to process the six split-frame images in parallel for feature extraction and matching. Finally, matching points are mapped back to the panoramic image through coordinate inverse mapping. Experimental results in multiple environments indicated that the algorithm can not only guarantee the number of feature points extracted and the accuracy of feature point extraction but can also significantly reduce the computation time compared to other commonly used algorithms. [ABSTRACT FROM AUTHOR]

Background: Mental stress-induced neurotransmitters can affect the immune system in various ways. Therefore, a better understanding of the role of neurotransmitters in the tumour immune microenvironment is expected to promote the development of novel anti-tumour therapies. Methods: In this study, we analysed the plasma levels of neurotransmitters in anti-programmed cell death protein 1 (PD-1) monoclonal antibody (mAb)-resistance patients and sensitive patients, to identify significantly different neurotransmitters. Subsequently, animal experiments and experiments in vitro were used to reveal the specific mechanism of norepinephrine's (NE) effect on immunotherapy. Results: The plasma NE levels were higher in anti-PD-1 mAb-resistance patients, which may be the main cause of anti-PD-1 mAb resistance. Then, from the perspective of the immunosuppressive microenvironment to explore the specific mechanism of NE-induced anti-PD-1 mAb resistance, we found that NE can affect the secretion of C-X-C Motif Chemokine Ligand 9 (CXCL9) and adenosine (ADO) in tumour cells, thereby inhibiting chemotaxis and function of CD8+ T cells. Notably, the WNT7A/β-catenin signalling pathway plays a crucial role in this progression. Conclusion: NE can affect the secretion of CXCL9 and ADO in tumour cells, thereby inhibiting chemotaxis and the function of CD8+ T cells and inducing anti-PD-1 mAb resistance in lung adenocarcinoma (LUAD). [ABSTRACT FROM AUTHOR]

Heterogeneous images acquired from various platforms and sensors provide complementary information. However, to use that information in applications such as image fusion and change detection, accurate image matching is essential to further process and analyze these heterogeneous images, especially if they have significant differences in radiation and geometric characteristics. Therefore, matching heterogeneous remote sensing images is challenging. To address this issue, we propose a feature point matching method named Cross and Self Attentional Matcher (CSAM) based on Attention mechanisms (algorithms) that have been extensively used in various computer vision-based applications. Specifically, CSAM alternatively uses self-Attention and cross-Attention on the two matching images to exploit feature point location and context information. Then, the feature descriptor is further aggregated to assist CSAM in creating matching point pairs while removing the false matching points. To further improve the training efficiency of CSAM, this paper establishes a new training dataset of heterogeneous images, including 1,000,000 generated image pairs. Extensive experiments indicate that CSAM outperforms the existing feature extraction and matching methods, including SIFT, RIFT, CFOG, NNDR, FSC, GMS, OA-Net, and Superglue, attaining an average precision and processing time of 81.29% and 0.13 s. In addition to higher matching performance and computational efficiency, CSAM has better generalization ability for multimodal image matching and registration tasks. [ABSTRACT FROM AUTHOR]