Background Neuroblastoma (NB) metastasis in high-risk patients is the most common cause of poor prognosis, but the mechanism of cancer metastasis is still unclear. RPLP1 is a member of a group of proteins called ribosomal proteins that are associated with tumor occurrence and metastasis. However, the expression and potential function of RPLP1 in NB are still unclear. Methods Bioinformatics methods were used to identify RPLP1 as a potential prognostic factor for NB. Real-time polymerase chain reaction and Western blotting were used to detect the expression of RPLP1 in NB tissues and cell lines to determine the correlation between RPLP1 expression and clinicopathological features. In vitro, we identified the role and mechanism of RPLP1 in NB cell line tumor metastasis. Results We detected high levels of RPLP1 expression in NB samples and cell lines. High expression levels are associated with an increased risk of recurrence and metastasis. In vitro experiments have shown that overexpression of RPLP1 promotes the metastatic ability of NB cells; in RPLP1 knockout cells, the opposite is true. In addition, the dual-luciferase reporter gene results indicated that RPLP1 is a potential downstream gene of MYC. MYC can promote the proliferation of NB cells by regulating the expression of RPLP1 and enhance cell metastasis through the epithelial mesenchymal transition (EMT) pathway. Conclusion In summary, our research revealed that RPLP1 is a potential biomarker and candidate therapeutic target for the poor prognosis of NB patients.

Oral lichen planus (OLP) is a T-cell-mediated immunoinflammatory disease. Several studies have proposed that Escherichia coli (E. coli) may participate in the progress of OLP. In this study, we examined the functional role of E. coli and its supernatant via toll-like receptor 4 (TLR4)/nuclear factor-kappab (NF-kappa B) signaling pathway in regulating T helper (Th) 17/regulatory T (Treg) balance and related cytokines and chemokines profile in OLP immune microenvironment. We discovered that E. coli and supernatant could activate the TLR4/NF-kappa B signaling pathway in human oral keratinocytes (HOKs) and OLP-derived T cells and increase the expression of interleukin (IL)-6, IL-17, C-C motif chemokine ligand (CCL) 17 and CCL20, thereby increasing the expression of retinoic acid-related orphan receptor (RoR gamma t) and the proportion of Th17 cells. Furthermore, the co-culture experiment revealed that HOKs treated with E. coli and supernatant increased T cell proliferation and migration, which promoted HOKs apoptosis. TLR4 inhibitor (TAK-242) successfully reversed the effect of E. coli and its supernatant. Consequently, E. coli and supernatant activated the TLR4/NF-kappa B signaling pathway in HOKs and OLP-derived T cells, leading to increased cytokines and chemokines expression and Th17/Treg imbalance in OLP.

Head and neck squamous cell carcinoma (HNSCC) has become a prevalent malignancy, and its incidence and mortality rate are increasing worldwide. Accumulating evidence has indicated that lipid metabolism-related genes (LMRGs) are involved in the occurrence and development of HNSCC. This study investigated the latent association of lipid metabolism with HNSCC and established a prognostic signature based on LMRGs. A prognostic risk model composed of eight differentially expressed LMRGs (PHYH, CYP4F8, INMT, ELOVL6, PLPP3, BCHE, TPTE, and STAR) was constructed through The Cancer Genome Atlas database. Then, ELOVL6 expression was validated in oral squamous cell carcinoma (OSCC), which is a common type of HNSCC, by immunohistochemical analysis. ELOVL6 expression in the OSCC II/III group was significantly higher than that in the other three groups (normal, dysplasia, and OSCC I), and OSCC patients with high ELOVL6 expression had poorer survival than those with low ELOVL6 expression. In summary, the LMRG-based prognostic feature had prognostic predictive capacity. ELOVL6 may be a potential prognostic factor for HNSCC patients.

To investigate the potential role of COVID-19 in relation to Behcet's disease (BD) and to search for relevant biomarkers. We used a bioinformatics approach to download transcriptomic data from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients and PBMCs of BD patients, screened the common differential genes between COVID-19 and BD, performed gene ontology (GO) and pathway analysis, and constructed the protein-protein interaction (PPI) network, screened the hub genes and performed co-expression analysis. In addition, we constructed the genes-transcription factors (TFs)-miRNAs network, the genes-diseases network and the genes-drugs network to gain insight into the interactions between the 2 diseases. We used the RNA-seq dataset from the GEO database (GSE152418, GSE198533). We used cross-analysis to obtain 461 up-regulated common differential genes and 509 down-regulated common differential genes, mapped the PPI network, and used Cytohubba to identify the 15 most strongly associated genes as hub genes (ACTB, BRCA1, RHOA, CCNB1, ASPM, CCNA2, TOP2A, PCNA, AURKA, KIF20A, MAD2L1, MCM4, BUB1, RFC4, and CENPE). We screened for statistically significant hub genes and found that ACTB was in low expression of both BD and COVID-19, and ASPM, CCNA2, CCNB1, and CENPE were in low expression of BD and high expression of COVID-19. GO analysis and pathway analysis was then performed to obtain common pathways and biological response processes, which suggested a common association between BD and COVID-19. The genes-TFs-miRNAs network, genes-diseases network and genes-drugs network also play important roles in the interaction between the 2 diseases. Interaction between COVID-19 and BD exists. ACTB, ASPM, CCNA2, CCNB1, and CENPE as potential biomarkers for 2 diseases.

Dyslipidaemia is associated with cancers. However, the specific expression of serum lipids in oral potentially malignant disorders (OPMD) and oral squamous cell carcinoma (OSCC) remains unclear, and it remains unknown whether serum lipids are associated with the development of OPMD and OSCC. This study investigated the serum lipid profiles of OPMD and OSCC patients, and the association of serum lipids with the occurrence of OPMD and OSCC. A total of 532 patients were recruited from the Affiliated Hospital of Stomatology, Nanjing Medical University. Serum lipid parameters including total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A (Apo-A), apolipoprotein B (Apo-B), and lipoprotein (a) (Lpa) were analysed, and clinicopathological data were collected for further analysis. Furthermore, a regression model was used to evaluate the relationship between serum lipids and the occurrence of OSCC and OPMD. After adjusting for age and sex, no significant differences were observed in serum lipid or body mass index (BMI) between OSCC patients and controls (P > 0.05). HDL-C, Apo-A, and Apo-B levels were lower in OSCC patients than in OPMD patients (P < 0.05); HDL-C and Apo-A levels were higher in OPMD patients than in controls (P < 0.05). Furthermore, female OSCC patients had higher Apo-A and BMI values than males. The HDL-C level was lower in patients under 60 years of age than in elders (P < 0.05); and age was related to a higher risk of developing OSCC. Female patients with OPMD had higher TC, HDL-C, and Apo-A levels than males (P < 0.05); OPMD patients over 60 years of age had higher HDL-C than youngers (P < 0.05), whereas the LDL-C level was lower in elders (P < 0.05). The HDL-C and BMI values of the patients with oral leukoplakia (OLK) with dysplasia were more elevated than those of the oral lichen planus group, and the LDL-C, and Apo-A levels in patients with OLK with dysplasia were decreased (P < 0.05). Sex, high HDL-C and Apo-A values were associated with the development of OPMD. Serum lipids exhibited certain differences according to the occurrence and development of OSCC; high levels of HDL-C and Apo-A might be markers for predicting OPMD.

Quantum search is one kind of the most important quantum algorithms, which is the only threat to postquantum cryptography till now. In this paper, we consider the problem of partial searching, where we are interested in part of the information of the item being searched. Then, we present the quantum partial search algorithm with smaller oracles, which reduces the difficulty of designing oracles without errors. The time complexity of this algorithm is smaller than that of the Grover search algorithm in practical instances. Furthermore, we present a punctuated version of the quantum partial search algorithm with smaller oracles to make the algorithm more practical by decreasing the number of iterations further. The punctuated algorithm could be running on several quantum computers in parallel. Taking these factors into consideration, the quantum partial search algorithm with smaller oracles for multiple target items is practical for running on a quantum computer and solving many real problems, such as the Hamiltonian circuit problem and solving systems of nonlinear equations.

The rapid development of the transportation in-dustry brings great challenge to airport taxiway routing. This paper presents a collaborative quantum inspired ant colony algorithm(CQIACA) for finding a reasonable solution to the airport taxiway routing problem. Theoretical analysis and simulation results shows that our algorithm performs better in terms of efficiency and effectiveness, and our algorithm is more suitable for actual needs.

The specific etiology and pathogenesis of oral lichen planus (OLP) remain elusive, and microbial dysbiosis may play an important role in OLP. We evaluated the saliva and tissue bacterial community of patients with OLP and identified the colonization of bacteria in OLP tissues. The saliva (n = 60) and tissue (n = 24) samples from OLP patients and the healthy controls were characterized by 16S rDNA gene sequencing and the bacterial signals in OLP tissues were detected by fluorescence in situ hybridization (FISH) targeting the bacterial 16S rDNA gene. Results indicate that the OLP tissue microbiome was different from the microbiota of OLP saliva. Compared with the healthy controls, Capnocytophaga and Gemella were higher in OLP saliva, while Escherichia-Shigella and Megasphaera were higher in OLP tissues, whereas seven taxa, including Carnobacteriaceae, Flavobacteriaceae, and Megasphaera, were enriched in both saliva and tissues of OLP patients. Furthermore, FISH found that the average optical density (AOD) of bacteria in the lamina propria of OLP tissues was higher than that of the healthy controls, and the AOD of bacteria in OLP epithelium and lamina propria was positively correlated. These data provide a different perspective for future investigation on the OLP microbiome.

To explore the feasibility of assessing the cancerization risk of oral potentially malignant disorders (OPMD) through a clinical risk model combined with autofluorescence and brush biopsy with DNA-image cytometry.

Pan-sharpening is a common image-fusion method. To improve the quality of fused images, a multilevel deep learning Pan-sharpening method is proposed in this paper. In the training phase, we introduce Coupled Sparse Denoising Autoencorder (CSDA) to reconstruct high-Resolution (HR) multispectral (MS) image from low-Resolution (LR) MS image and HR Panchromatic (Pan) image. CSDA has four networks including LM-HP network, HR-MS network, feature mapping network and fine-tuning network. The hidden features in LM-HP network and HR-MS network as well as the mapping function between the two features are learned through joint optimization. In LM-HP and HR-MS networks, the hidden features of image patch pairs are extracted by the sparse autoencoder. A sparse denoising autoencoder is used to build the nonlinear mapping between the extracted features. In the testing phase, the LR-MS and HR-Pan images patches are fed to the CSDA network to reconstruct the fused HR-MS image. The experimental results show that the proposed method is better than the traditional pans-sharpening methods.

Gas plume detection (GPD) of Hyperspectral video sequences (HVSs) has become a hot topic in the field of remote sensing. The traditional HVS processing methods reshape the extracted video to a 2-D matrix, which is at expense of destroying spatial or spectral structure. In this paper, we propose a novel method of Multi-feature Tensor Decomposition (MTD), where the 3-dimensional (3-D) structure of the extracted video can be seen as a 3-order tensor, thus the spatial and temporal structures in HVS are preserved. We employ the tensor nuclear norm to model the low-rank property of the background, and apply tensor sparse norm to constrain the sparsity of the gas plume. Moreover, taking into consideration the continuity in both spatial and temporal domain of the gas plume, we add a 3-D total variation regularization (3DTV) in the proposed detection model, and assume the support of the gas plume in different features are the same. The final objective function of gas plume detection is efficiently solved by augmented Lagrangian multiplier algorithm (ADMM). Experimental results demonstrate the effectiveness and high detection accuracy of the proposed method.

Most traditional compressive sensing (CS) reconstruction methods suffer from the intensive computation caused by iterations. This paper aims at presenting a non-iterative algorithm to reconstruct hyperspectral images (HSI) from patch-based compressively sensed measurements. Our method contains two residual convolutional neural networks. One is reconstruction network for compressive sensing reconstruction and the other is deblocking network for removing the blocky effect, which is caused by patch-based sampling. The reconstruction network can efficiently reconstruct all the bands of HSI jointly, thus the spectral correlation is well preserved. In addition, the deblock performance is enhanced by combining more patches into a larger patch in the deblocking network. Experimental results verify that our method outperforms the state-of-the-art compressive sensing reconstruction methods with patch-based CS measurement.

Recently, the dictionary-aided sparse regression (SR) method for hyperspectral unmixing has received much attention in the field of remote sensing. However, under the assumption that each pixel in the hyperspectral scene can be viewed as a combination of endmembers in the spectral library, most of SR methods ignore the spectral signature mismatches between an actual spectral signature and its corresponding endmember in spectral library. To overcome this problem, we proposed a joint optimizing unmixing model called DSPCSR which includes dictionary sparse pruning and collaborative sparse regression. By exploiting the sparse property of spectral mismatch error and the collaborative sparse property of the abundance matrix, the DSPCSR can provide good robustness and performance. Experiments on the synthetic and real datasets show that the proposed DSPCSR can achieve better performance compared with several state-of-art algorithms.

Generally, the improvement in resolution will lead to larger data volume and higher data dimension for Hyperspectral image, which raise a higher requirement for previous image processing algorithms. In this paper, a novel coupled spectral-spatial tensor representation framework (CSSTR) is proposed for denoising of hyperspectral images. Specifically, the proposed method is applied to describe the spectral-spatial features which decomposes a third-order tensor into the sum of several component tensors, with each component tensor being the outer product of a matrix and a vector. Owing to the spatial-spectral constraint fed back from the tensor representation method, CSSTR can capture the structural correlations and inherent feature information of data. Finally, several experiments were conducted to illustrate the advantage of the proposed algorithm.

Anomaly detection in hyperspectral images aims to separate the abnormal pixels from the background, and becomes an important application of hyperspectral data processing. Anomaly detection based on Low-Rank and Sparse Representation (LRASR) can detect abnormal pixels accurately. However, with the growth of the hyperspectral data volumes, this algorithm consumes a huge amount of time and computational resources, and needs to be improved accordingly. Spark is a distributed big data processing platform, and is applicable for complex iterative calculations, because of its powerful in-memory computation and efficient task scheduling. Based on Spark, this paper proposes a distributed and parallel LRASR (called DP-LRASR), which first segments hyperspectral images using narrow dependency of resilient distributed datasets, and afterwards, a parallel clustering algorithm is employed to improve the efficiency, remarkably. Experimental results demonstrate that DP-LRASR achieves a good speedup with high scalability, in the premise of remarkable detection accuracy.

In this paper, a 4×32 low side-lobe slot antenna array using unequal microstrip-ridge gap waveguide (GWG) feeding network at 94 GHz is presented. The slot antenna array is built by two double-sided printed circuit boards (PCBs). The top one employs SIW-based 2×2 subarrays and the bottom one is a broadband 2×16 way unequal GWG feeding network. Applying Taylor amplitude weighting in the 16 way GWG feeding network, low side-lobe performance is achieved. The unequal T-junction dividers with phase compensation are proposed and designed for various output ratios. Simulated results show that the antenna array achieves 5 GHz bandwidth with a peak gain of 26.3 dBi at 94 GHz. Across the entire band, low side lobe level (SLL) below −20 dB is realized.

In this paper, we propose an improved method for simultaneous estimation of the bias field and segmentation of tissues for magnetic resonance images, which is an extension of the method in. Firstly, the bias field is modeled as a linear combination of a set of basis functions, and thereby parameterized by the coefficients of the basis functions. Then we model the distribution of intensity in each tissue as a Gaussian distribution, and use the maximum a posteriori probability and total variation (TV) regularization to define our objective energy function. At last, an efficient iterative algorithm based on split Bregman method is used to minimize our energy function at a fast rate. Comparisons with other approaches demonstrate the superior performance of this algorithm.

利用系留汽艇边界层气象要素探测资料,结合Micaps系统平台和NCEP分析资料,对2006年12月24-27日南京地区一次大雾天气过程的天气学环流背景、热力结构、边界层气象要素和微观结构进行分析,揭示有利于大雾形成和维持的机制。结果显示:700 hPa以下气压场长时间呈均匀分布为大雾的产生和维持提供了良好的环流背景;近地层以上逆温的存在、近地层以上暖湿空气的侵入是大雾发展和加强的主要原因。边界层的气象要素与雾的微观特征有紧密联系。

The absorption and scattering coefficients of atmospheric aerosols were continuously measured with a Photoacoustic Soot Spectrometer (PASS, DMT Inc. USA) at a suburb site of Nanjing, one of the regions experiencing rapid industrialization in China. The measurements were carried out during autumn and winter 2007. A preliminary analysis of the data shows that, the scattering coefficient, Bscat, is two to ten times larger than the absorption coefficient, Babs, implying that the aerosols formed/emitted in this area are more scattering than previous assumed, and can be more important in cooling the Earth-atmosphere system. The results also indicate that the absolute values of both parameters are very much dependent on the meteorological conditions, such as wind speed and direction, fog, rain, etc. as well as the time of the day. Higher values often appear at nighttimes when wind is weak, especially when a temperature inverse layer is present near the surface. Higher values of Bscat and Babs were also observed under hazy and foggy weather conditions or when wind is blown from east, where a large industrial zone is located. Simultaneous measurements of the number concentrations, chemical compositions, and size distributions of aerosol particles are used to explain the characteristics of the changes in Bscat and Babs.

Based on observing materials of atmospheric fine particles from July to December, 2006, at the north suburban area of Nanjing, the concentration changes and size distributions characteristics of these particles with sizes 0.01-2.5 μm were studied. The particle number concentration was quite high relatively in this region, reaching 10 4/cm 3, in which, ultra fine particles (size 0.01-0.1 μm) contribution was great relatively to the total particle number concentration, occupying about 87%; the number concentration spectrum distribution in summer, autumn and winter all appeared single peak type structure, the peak value was concentrated in 0.02-0.05 μm; the number concentration of atmospheric fine particle reached the peak value in noon solar radiation strongest, the precipitation process on the removing action was obvious. The ultra fine particle concentration was highest in summer, this might be related to the meteorological condition of high temperature, high humidity, meanwhile, stronger solar radiation could also make the producing rate of this seasonal atmospheric fine particles higher.