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Coding technology provides new ideas for spatial resolution enhancement of coherent Doppler wind lidar (CDWL). To improve the performance of coding CDWL for ultra-fine-wind field detection, the crosstalk between neighboring laser pulses is analyzed in theory. The strong backscattered signal from aerosols in near field region will interfere with the weak atmospheric signal, making the accuracy of Doppler shift estimation deteriorate seriously. Considering the formation mechanism of crosstalk, a solution based on adaptive field of view (FOV) modulation is proposed to suppress the crosstalk which is validated by numerical simulation and experiment. Dynamic range of the backscatter intensity is controlled from 10 dB to 2 dB within the distance of 50 m to 300 m, thus the crosstalk is accordingly suppressed.

Background: The present work was conducted to screen the potential biomarkers affecting nasopharyngeal carcinoma (NPC) progression through RNA-sequencing (RNA-seq), bioinformatic analysis and functional experiments. Materials and Methods: Six normal samples and five NPC clinical samples were collected for RNA-seq analysis. The expression levels in both groups were determined through student’s t-test. We identified genes of P < 0.01 as the differentially expressed genes (DEGs). In addition, gene set enrichment analysis (GSEA) was conducted. Afterwards, STRING V10 database was employed to extract protein interactions among the DEGs. Later, we established a protein-protein interaction (PPI) network, and used the Cytoscape software for network visualization. qRT-PCR was conducted to verify hub genes from clinical samples. Then, the function of CXCL10 in cell proliferation, apoptosis, invasion and migration was evaluated. Results: A total of 2024 DEGs were identified, among which, 1449 were down-regulated and 575 were up-regulated. The PPI was constructed, and the hub genes including Insulin Like Growth Factor 1 (IGF1), C-X-C Motif Chemokine Ligand 10 (CXCL10), Interleukin 13 (IL13), Intercellular Adhesion Molecule 1 (ICAM1), G Protein Subunit Gamma Transducin 1 (GNGT1), Matrix Metallopeptidase 1 (MMP1), Neurexin 1 (NRXN1) and Matrix Metallopeptidase 3 (MMP3) were obtained. The expression levels of CXCL10, IGF1, MMP3, MMP1, ICAM1, and IL-13 were significantly up-regulated in tumor tissues. High expression levels of CXCL10, MMP3 and ICAM1 predicted poor prognosis of NPC patients. CXCL10 silencing suppressed NPC cell proliferation and migration. Conclusions: CXCL10 may serve as a potential key gene affecting NPC genesis and progression.

Background: Nasopharyngeal carcinoma (NPC) is a malignant head and neck tumor arising in the nasopharynx. MicroRNAs (miRNAs) are elucidated to exert tumor-suppressing function in human cancers. Numerous studies have manifested that miR-30a-5p serves as an anti-oncogene in various cancers. Objective: To research the biological function and molecular mechanism of miR-30a-5p in NPC. Methods: The morphology of NPC tissues was revealed by H&E staining. Transwell and wound healing assays were applied to investigate the effects of miR-30a-5p on NPC cell migration. The binding interaction between miR-30a-5p and nucleobindin 2 (NUCB2) was identified by luciferase reporter assay. Xenograft nude mice were used to detect the influence of miR-30a-5p on NPC tumor growth. Results: MiR-30a-5p was downregulated in NPC tissues and cells. The overexpression ofmiR-30a-5p inhibited proliferation, migration and invasion abilities of NPC cells. Moreover, NUCB2 was revealed to be a downstream target gene of miR-30a-5p, and knockdown of NUCB2 repressed the malignant behaviors of NPC cells and tumor growth. Additionally, rescue experiments revealed that miR-30a-5p suppressed the proliferation, migration and invasion of NPC cells via targeting NUCB2 in vitro. Meanwhile, in vivo assays depicted that NUCB2 overexpression rescued the effects induced by miR-30a-5p upregulation on tumor growth. Conclusion: MiR-30a-5p modulates NPC progression by targeting NUCB2. These findings lay a foundation for exploring the clinical treatment of NPC.

Stratospheric aerosols play an important role in the atmospheric chemical and radiative balance. To detect the stratospheric aerosol layer, a 1064 nm lidar with high resolution and large dynamic range is developed using a superconducting nanowire single-photon detector (SNSPD). Measurements are typically performed at 1064 nm for its sensitivity to aerosol, whereas detectors are limited by low efficiency and high dark count rate (DCR). SNSPDs are characterized by high efficiency in the infrared wavelength domain, as well as low noise and dead time, which can significantly enhance the signal quality. However, it is still challenging to build an SNSPD with both large active area and high count rate. To improve the maximal count rate (MCR) so as to avoid saturation in the near range, a 16-pixel interleaved SNSPD array and a multichannel data acquisition system are developed. As a reference, a synchronous system working at 532 nm is applied. In a continuous comparison experiment, backscatter ratio profiles are retrieved with resolutions of 90 m/3 min, and the 1064 nm system shows better performance, which is sensitive to aerosols and immune to the contamination of the ozone absorption and density of molecule change in the lower stratosphere.

Geometric deep learning has sparked a rising interest in computer graphics to perform shape understanding tasks, such as shape classification and semantic segmentation. When the input is a polygonal surface, one has to suffer from the irregular mesh structure. Motivated by the geometric spectral theory, we introduce Laplacian2Mesh, a novel and flexible convolutional neural network (CNN) framework for coping with irregular triangle meshes (vertices may have any valence). By mapping the input mesh surface to the multi-dimensional Laplacian-Beltrami space, Laplacian2Mesh enables one to perform shape analysis tasks directly using the mature CNNs, without the need to deal with the irregular connectivity of the mesh structure. We further define a mesh pooling operation such that the receptive field of the network can be expanded while retaining the original vertex set as well as the connections between them. Besides, we introduce a channel-wise self-attention block to learn the individual importance of feature ingredients. Laplacian2Mesh not only decouples the geometry from the irregular connectivity of the mesh structure but also better captures the global features that are central to shape classification and segmentation. Extensive tests on various datasets demonstrate the effectiveness and efficiency of Laplacian2Mesh, particularly in terms of the capability of being vulnerable to noise to fulfill various learning tasks., Accepted by IEEE Transactions on Visualization and Computer Graphics (TVCG)

Objective: To study the value of magnetic resonance imaging (MRI) in the diagnosis of nasopharyngeal carcinoma with pericranial infiltration. Methods: 86 patients with nasopharyngeal carcinoma were selected as the research objected and examined by MRI and clinical examination, and the results of the two methods were compared. Results: In MRI examination, the positive rate of cranial nerve infiltration in nasopharyngeal carcinoma was 23.26%, which was significantly higher than 8.13% in clinical diagnosis. The coincidence rate between clinical diagnosis and MRI diagnosis was 35.00%. Conclusion: Nuclear MRI has important application value in the diagnosis of cranial nerve infiltration of nasopharyngeal carcinoma, which is worthy of clinical promotion.

Iron (Fe) is an essential component for marine ecosystems, and it is related to the growth of phytoplankton communities and environmental evolution in coastal area. However, the effect of aquaculture activities on sediment Fe levels is not well studied. Fe levels and grain sizes are determined in two cores (respectively Core C in the culture area and Core A in the control area) in Sishili Bay to reveal the influence of cultivation on sediment Fe levels over an extended period. The sediment Fe levels are distinguished in the upper sections (culture period) but equal in the lower sections (non-culture period) of the two cores. The core C has the same Fe levels as Core A before 1950s (non-culture period). However, the sediment Fe levels of Core C increased during 1950s–1970s (the algae culture period) and decreased after the 1970s (shellfish culture period) compared with Core A, indicating the algae and shellfish culture impose opposite effects on sediment Fe levels. Similarly, sediment grain sizes are observed to be finer during the algae culture period but coarser during the shellfish culture period, and the variation of sediment grain sizes because of culture activities is the important factor affecting sediment Fe levels. The slowing down of ocean current due to algae culture causes finer particles and higher Fe levels in sediment. However, during the shellfish culture period, bio-deposition and re-suspension play major roles in coarsening sediment particles and decreasing sediment Fe levels.

Spectroscopy is a well-established nonintrusive tool that has played an important role in identifying and quantifying substances, from quantum descriptions to chemical and biomedical diagnostics. Challenges exist in accurate spectrum analysis in free space, which hinders us from understanding the composition of multiple gases and the chemical processes in the atmosphere. A photon-counting distributed free-space spectroscopy is proposed and demonstrated using lidar technique, incorporating a comb-referenced frequency-scanning laser and a superconducting nanowire single-photon detector. It is suitable for remote spectrum analysis with a range resolution over a wide band. As an example, a continuous field experiment is carried out over 72 h to obtain the spectra of carbon dioxide (CO2) and semi-heavy water (HDO, isotopic water vapor) in 6 km, with a range resolution of 60 m and a time resolution of 10 min. Compared to the methods that obtain only column-integrated spectra over kilometer-scale, the range resolution is improved by 2–3 orders of magnitude in this work. The CO2 and HDO concentrations are retrieved from the spectra acquired with uncertainties as low as ±1.2% and ±14.3%, respectively. This method holds much promise for increasing knowledge of atmospheric environment and chemistry researches, especially in terms of the evolution of complex molecular spectra in open areas., A photon-counting distributed free-space spectroscopy is proposed and demonstrated using lidar technique. It is suitable for remote spectrum analysis with range resolution over wide band.

We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field representing the coarse 3D shape and the other capturing the details. Given an input image, our network, coined D2IM-Net, encodes it into global and local features which are respectively fed into two decoders. The base decoder uses the global features to reconstruct a coarse implicit field, while the detail decoder reconstructs, from the local features, two displacement maps, defined over the front and back sides of the captured object. The final 3D reconstruction is a fusion between the base shape and the displacement maps, with three losses enforcing the recovery of coarse shape, overall structure, and surface details via a novel Laplacian term.

BACKGROUND The long noncoding RNA (lncRNA) HOTTIP is involved in gastric cancer tumorigenesis, papillary thyroid carcinoma, colorectal cancer, lung adenocarcinoma, and hepatocellular carcinoma, but it is unclear how HOTTIP exerts roles in nasopharyngeal carcinoma (NPC). The present study investigated HOTTIP function during NPC development. MATERIAL AND METHODS HOTTIP levels in cancer specimens and cell lines were analyzed using qRT-PCR. HOTTIP function in NPC was determined by Cell Counting Kit-8 (CCK8) and Transwell assay. RESULTS HOTTIP expression was increased in NPC tissues. Higher levels of HOTTIP are correlated with lower survival in NPC patients. HOTTIP silencing suppressed the proliferation, cell cycle, migration, and invasion of NPC cells. HOTTIP served as a sponge for miR-4301. miR-4301 expression was significantly inhibited by HOTTIP in NPC cells. miR-4301 overexpression dramatically inhibited NPC cell proliferation, migration, and invasion. CONCLUSIONS This study showed that HOTTIP acts as an oncogene in NPC by sponging miR-4301.

We introduce a novel framework for using natural language to generate and edit 3D indoor scenes, harnessing scene semantics and text-scene grounding knowledge learned from large annotated 3D scene databases. The advantage of natural language editing interfaces is strongest when performing semantic operations at the sub-scene level, acting on groups of objects. We learn how to manipulate these sub-scenes by analyzing existing 3D scenes. We perform edits by first parsing a natural language command from the user and transforming it into a semantic scene graph that is used to retrieve corresponding sub-scenes from the databases that match the command. We then augment this retrieved sub-scene by incorporating other objects that may be implied by the scene context. Finally, a new 3D scene is synthesized by aligning the augmented sub-scene with the user's current scene, where new objects are spliced into the environment, possibly triggering appropriate adjustments to the existing scene arrangement. A suggestive modeling interface with multiple interpretations of user commands is used to alleviate ambiguities in natural language. We conduct studies comparing our approach against both prior text-to-scene work and artist-made scenes and find that our method significantly outperforms prior work and is comparable to handmade scenes even when complex and varied natural sentences are used.

We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout metric via neural graph matching, using an attention-based GMN designed under a triplet network setting. To train our network, we utilize weak labels obtained by pixel-wise Intersection-over-Union (IoUs) to define the triplet loss. Importantly, LayoutGMN is built with a structural bias which can effectively compensate for the lack of structure awareness in IoUs. We demonstrate this on two prominent forms of layouts, viz., floorplans and UI designs, via retrieval experiments on large-scale datasets. In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution. In addition, LayoutGMN is the first deep model to offer both metric learning of structural layout similarity and structural matching between layout elements.

Shape dissimilarity is a fundamental problem with many applications such as shape exploration, retrieval, and classification. Given a collection of shapes, existing methods develop a consistent global metric to compare and organize shapes. The global nature of the involved shape descriptors implies that overall shape appearance is compared. These methods work well to distinguish shapes from different categories, but often fail for fine-grained classes within the same category. In this paper, we develop a dissimilarity metric for fine-grained classes by fusing together multiple distinctive metrics for different classes. The fused metric measures the dissimilarities among inter-class shapes by observing their unique traits. We demonstrate the advantage of using our approach in several applications.

A multi-frequency differential absorption lidar incorporating a tunable laser and an optical frequency comb is demonstrated for precise spectrum analysis of atmospheric gas. The single frequency tunable laser is stabilized by locking to the optical frequency comb, with a standard deviation of 0.5 MHz. To achieve a high signal-to-noise ratio, a multi-mode superconducting nanowire single-photon detector with an active-area diameter of 50 µm, a quantum efficiency of 31.5%, and dark noise of 100 counts per second is implemented, which enables to avoid the need for high energy lasers. In the experiment, the range-resolved spectrum of atmospheric mixture gases (CO2 and HDO) in a region of 1572.2 - 1572.45 nm is obtained. Results show different partially overlapped absorption of two gases in different seasons, with a stronger influence of HDO on CO2 in summer than in winter. The interactions are taken into account by separating the mixture absorption spectrum (one CO2 line and two HDO lines) with triple-peak Voigt fitting. The retrieved concentrations over 6 km with a range resolution of 120 m and a time resolution of 10 min are compared with in-situ sensors. The uncertainties of the retrieved concentrations are as low as 6.5 µmol/mol (ppm) and 1×10−3 g/kg for CO2 and HDO, respectively.

In this paper, we propose a sparse surface representation for arbitrary surface models (point clouds, mesh models, continuous surface models, etc.). We approximate the input surface model with radial basis functions (RBF) whose centers are located on the medial axis of the input object surface. The sparsity of the RBF representation is achieved by solving an L 1 optimization problem. Experimental results demonstrate that our method needs much less number of parameters to represent the input surface model with good accuracy. The sparse representation is useful in various applications, such as saving memory space in storing the surface models, and saving time in transmission of the surface models on the internet.

Purpose This study aimed to investigate the expression and biological function of miR-141-3p in nasopharyngeal carcinoma (NPC) via targeting neoplasm metastasis 1 (NME1). Materials and methods The expression of miR-141-3p and NME1 in 5-8F, C666-1, CNE-1, CNE-2, 6-10B and NP69 nasopharyngeal epithelial cells were detected using real-time Polymerase Chain Reaction (real-time PCR) and western blot, respectively. Cell proliferation was detected using Cell Counting Kit-8 (CCK-8), and the metastasis was detected using Transwell. The binding of miR-141-3p to NME1 was detected by dual luciferase reporter gene detection system. The effects of miR-141-3p on tumor growth were also determined in vivo. Results The results showed that the expression of miR-141-3p significantly increased in various tumor cell lines and the expression of NME1 was higher in NP69 cells and lower in 5-8F cells, which had significant negative correlation. Furthermore, the expression of NME1 was significantly reduced after transfection of miR-141-3p and miR-141-3p promoted cell proliferation and metastasis. The double luciferase reporter gene detection system confirmed that NME1 was the target gene of miR-141-3p. Knockout of NME1 promoted the proliferation and metastasis of NP69 or 6-10B cells and the activation of p-Akt, which were abrogated by miR-141-3p. In vivo, the tumor volumes and weights in the miR-141-3p group significantly increased followed by down-regulation of NME1 and activation of p-Akt. Conclusions We confirmed that miR-141-3p promotes the proliferation and metastasis of NPC by targeting NME1.