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Capturing images under extremely low-light conditions usually suffers from various types of noise due to the limited photon and low signal-to-noise ratio (SNR), which makes low-light denoising a challenging task in the field of imaging technology. Nevertheless, existing methods primarily focus on investigating the precise modeling of real noise distributions while neglecting improvements in the noise modeling capabilities of learning models. To address this situation, a novel self-adaptive deformable-convolution-based U-Net (SD-UNet) model is proposed in this paper. Firstly, deformable convolution is employed to tackle noise patterns with different geometries, thus extracting more reliable noise representations. After that, a self-adaptive learning block is proposed to enable the network to automatically select appropriate learning branches for noise with different scales. Finally, a novel structural loss function is leveraged to evaluate the difference between denoised and clean images. The experimental results on multiple public datasets validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Haze or cloud always shrouds satellite images, obscuring valuable geographic information for military surveillance, natural calamity surveillance and mineral resource exploration. Satellite image dehazing (SID) provides the possibility for better applications of satellite images. Most of the existing dehazing methods are tailored for natural images and are not very effective for satellite images with non-homogeneous haze since the semantic structure information and inconsistent attenuation are not fully considered. To tackle this problem, this study proposes a hierarchical semantic-guided contextual structure-aware network (SCSNet) for spectral satellite image dehazing. Specifically, a hybrid CNN–Transformer architecture integrated with a hierarchical semantic guidance (HSG) module is presented to learn semantic structure information by synergetically complementing local representation from non-local features. Furthermore, a cross-layer fusion (CLF) module is specially designed to replace the traditional skip connection during the feature decoding stage so as to reinforce the attention to the spatial regions and feature channels with more serious attenuation. The results on the SateHaze1k, RS-Haze, and RSID datasets demonstrated that the proposed SCSNet can achieve effective dehazing and outperforms existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Given the impressive achievement of convolutional neural networks (CNNs) in grasping image priors from extensive datasets, they have been widely utilized for tasks related to image restoration. Recently, there is been significant progress in another category of neural architectures—Transformers. These models have demonstrated remarkable performance in natural language tasks and higher‐level vision applications. Despite their ability to address some of CNNs limitations, such as restricted receptive fields and adaptability issues, Transformer models often face difficulties when processing images with a high level of detail. This is because the complexity of the computations required increases significantly with the image's spatial resolution. As a result, their application to most high‐resolution image restoration tasks becomes impractical. In our research, we introduce a novel Transformer model, named DehFormer, by implementing specific design modifications in its fundamental components, for example, the multi‐head attention and feed‐forward network. Specifically, the proposed architecture consists of the three modules, that is, (a) multi‐scale feature aggregation network (MSFAN), (b) the gated‐Dconv feed‐forward network (GFFN), (c) and the multi‐Dconv head transposed attention (MDHTA). For the MDHTA module, our objective is to scrutinize the mechanics of scaled dot‐product attention through the utilization of per‐element product operations, thereby bypassing the need for matrix multiplications and operating directly in the frequency domain for enhanced efficiency. For the GFFN module, which enables only the relevant and valuable information to advance through the network hierarchy, thereby enhancing the efficiency of information flow within the model. Extensive experiments are conducted on the SateHazelk, RS‐Haze, and RSID datasets, resulting in performance that significantly exceeds that of existing methods. [ABSTRACT FROM AUTHOR]

Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to optimize models and have difficulty effectively modeling the real visual errors between the enhanced images and the normally exposed images. In this paper, we propose an adaptive dual aggregation network with normalizing flows (ADANF) for LLIE. First, an adaptive dual aggregation encoder is built to fully explore the global properties and local details of the low-light images for extracting illumination-robust features. Next, a reversible normalizing flow decoder is utilized to model real visual errors between enhanced and normally exposed images by mapping images into underlying data distributions. Finally, to further improve the quality of the enhanced images, a gated multi-scale information transmitting module is leveraged to introduce the multi-scale information from the adaptive dual aggregation encoder into the normalizing flow decoder. Extensive experiments on paired and unpaired datasets have verified the effectiveness of the proposed ADANF. [ABSTRACT FROM AUTHOR]

The corner detection plays an important role in the area of image processing and computer vision. The current corner detection methods often utilize few cues or single model to improve the detection correctness and repeatability. A composite model of both intensity, pattern, curvature, and scale is proposed as a possible solution to these problems. Firstly, a corner measure function that reflects both the intensity, pattern, and curvature difference is formulated based on the 8-neighbor pixel blocks. Secondly, some scale-based global scale importance factors are formulated based on the contour distribution and corner distribution. Thirdly, based on the corner measure and the importance factors, a high-performance corner detector (IPCS) is derived. The experiments based on both the ground truth and the standard image set are conducted to evaluate the correctness and repeatability of the proposed detector. The experiment results come up with that the proposed detector has remarkable performance advantages among the comprising state-of-the-art detectors. [ABSTRACT FROM AUTHOR]

From the perspective of detector optimisation, detecting objects using only a one‐level feature cannot provide good performance for a wide range of scales. Various complex feature pyramidal structures address this problem using the divide‐and‐conquer strategy and multi‐scale feature fusion. However, this requires adding too many additional convolutional layers and fusion operations. To address the issue, a simple detection part is proposed, which includes three components, namely a one‐level feature map for detection, the encoder structure with feedback connection, and a decoupled head. The redesigned encoder and decoupled head can successfully address the performance decline caused by the one‐level feature‐based detection. Moreover, the proposed method can accelerate the convergence of the detector and achieve a faster inference time. Based on the optimised detection part, an adaptive feedback connection with a single‐level feature (AFS) is proposed for object detection. The experiments conducted on the MS COCO 2017 benchmark show that the proposed method can achieve comparable results with its multi‐scale pyramid counterpart, You Only Look Once v4 (YOLOv4). In addition, AFS can help the YOLOv4 achieve 44.9 mAP at 27 frame per second and converging 82 epochs earlier under the image size of 608×608, which represents a 42.1% improvements in the convergence speed. [ABSTRACT FROM AUTHOR]

The accuracy and stability of the photoelectric stabilized platform will be inevitably affected by the friction disturbance and the base platform disturbance in the actual operation. To improve the disturbance rejection performance, two kinds of the disturbance observers are employed and compared in this paper, including the adaptive proportion-integrator observer and the robust sliding mode observer. The disturbances of the friction torque and the moving base are observed, then these observed values are compensated to the voltage loop by the feedback and feedforward, respectively. While the disturbances of the friction torque and the shaking base are compensated, the parameters of the speed stability loop are also tuned to improve the performance of this photoelectric stabilized platform. Finally, the effectiveness of the proposed method is verified by both simulations and experiments. The results show that the proposed disturbance compensation control method based on the sliding mode observer has strong robustness and can effectively reduce the impact of system disturbances. [ABSTRACT FROM AUTHOR]

• IMPT resulted in less late xerostomia than IMRT for oropharyngeal cancer. • Oral cavity dosimetric variables were related to the occurrence of late xerostomia. • The dosimetric variables of parotids or submandibular were not associated with late xerostomia. To determine rates of xerostomia after intensity-modulated radiotherapy (IMRT) or intensity-modulated proton therapy (IMPT) for oropharyngeal cancer (OPC) and identify dosimetric factors associated with xerostomia risk. Patients with OPC who received IMRT (n = 429) or IMPT (n = 103) from January 2011 through June 2015 at a single institution were studied retrospectively. Every 3 months after treatment, each patient completed an eight-item self-reported xerostomia-specific questionnaire (XQ; summary XQ score, 0–100). An XQ score of 50 was selected as the demarcation value for moderate-severe (XQs ≥ 50) and no-mild (XQs < 50) xerostomia. The mean doses and percent volumes of organs at risk receiving various doses (V5-V70) were extracted from the initial treatment plans. The dosimetric variables and xerostomia risk were compared using an independent-sample t -test or chi-square test. The median follow-up time was 36.2 months. The proportions of patients with moderate-severe xerostomia were similar in the two treatment groups up to 18 months after treatment. However, moderate-severe xerostomia was less common in the IMPT group than in the IMRT group at 18–24 months (6% vs. 20%; p = 0.025) and 24–36 months (6% vs. 20%; p = 0.01). During the late xerostomia period (24–36 months), high dose/volume exposures (V25-V70) in the oral cavity were associated with high proportions of patients with moderate-severe xerostomia (all p < 0.05), but dosimetric variables regarding the salivary glands were not associated with late xerostomia. IMPT was associated with less late xerostomia than was IMRT in OPC patients. Oral cavity dosimetric variables were related to the occurrence of late xerostomia. [ABSTRACT FROM AUTHOR]

In many scenarios, where videos are transmitted through bandwidth‐limited channels for subsequent semantic analytics, the choice of frame rates has to balance between bandwidth constraints and analytics performance. Faced with this practical challenge, this study focuses on enhancing object tracking at low frame rates and proposes a learning Interpolation for tracking framework. This framework embeds an implicit video frame interpolation sub‐network, which is concatenated and jointly trained with another object tracking sub‐network. Once a low frame‐rate video is an input, it is first mapped into a high frame‐rate latent video, based on which the tracker is learned. Novel strategies and loss functions are derived to ensure the effective end‐to‐end optimisation of the authors' network. On several challenging benchmarks and settings, their method achieves a highly competitive tradeoff between frame rate and tracking accuracy. As is known, the implications of interpolation on semantic video analytics and tracking remain unexplored, and the authors expect their method to find many applications in mobile embedded vision, Internet of Things and edge computing. [ABSTRACT FROM AUTHOR]

This study proposes a local bias field and difference estimation (LBDE) model for medical image segmentation and bias field correction. Firstly, the LBDE model uses a linear combination of a given set of smooth orthogonal basis functions, which is called Chebyshev polynomial, to estimate the bias field. Then, a clustering criterion function is defined by considering the difference between the measured image and approximated image in a small region. By applying this difference in the local region, the LBDE model can obtain accurate segmentation results and estimation of the bias field. Finally, the energy functional is incorporated into a level set formulation with a regularisation term, and it is minimised via the level set evolution process. The LBDE model first appears as a two‐phase model and then extends to the multi‐phase one. Extensive experiments on medical images demonstrate that the LBDE model achieves more precise segmentation results in terms of Jaccard similarity and dice similarity coefficient than the comparative models. Therefore the proposed model can increase the segmentation accuracy and robustness to noise. [ABSTRACT FROM AUTHOR]

For applications such as remote sensing imaging and medical imaging, high-resolution (HR) images are urgently required. Image Super-Resolution (SR) reconstruction has great application prospects in optical imaging. In this paper, we propose a novel unified Partial Differential Equation (PDE)-based method to single image SR reconstruction. Firstly, two directional diffusion terms calculated by Anisotropic Nonlinear Structure Tensor (ANLST) are constructed, combing information of all channels to prevent singular results, making full use of its directional diffusion feature. Secondly, by introducing multiple orientations estimation using high order matrix-valued tensor instead of gradient, orientations can be estimated more precisely for junctions or corners. As a unique descriptor of orientations, mixed orientation parameter (MOP) is separated into two orientations by finding roots of a second-order polynomial in the nonlinear part. Then, we synthesize a Gradient Vector Flow (GVF) shock filter to balance edge enhancement and de-noising process. Experimental results confirm the validity of the method and show that the method enhances image edges, restores corners or junctions, and suppresses noise robustness, which is competitive with the existing methods. [ABSTRACT FROM AUTHOR]

With the recent emergence of Middle East Respiratory Syndrome coronavirus in humans and the outbreak of devastating porcine epidemic diarrhea coronavirus in swine, therapeutic intervention is urgently needed. However, anti-coronavirus drugs currently are not available. In an effort to assist rapid development of anti-coronavirus drugs, here we screened the NIH Clinical Collection in cell culture using a luciferase reporter-expressing recombinant murine coronavirus. Of the 727 compounds screened, 84 were found to have a significant anti-coronavirus effect. Further experiments revealed that 51 compounds blocked virus entry while 19 others inhibited viral replication. Additional validation studies with the top 3 inhibitors (hexachlorophene, nitazoxanide and homoharringtonine) demonstrated robust anti-coronavirus activities (a reduction of 6 to 8 log 10 in virus titer) with an IC 50 ranging from 11 nM to 1.2 μM. Furthermore, homoharringtonine and hexachlorophene exhibited broad antiviral activity against diverse species of human and animal coronaviruses. Since the NIH Clinical Collection consists of compounds that have already been through clinical trials, these small molecule inhibitors have a great potential for rapid development as anti-coronavirus drugs. [ABSTRACT FROM AUTHOR]

Small-cell lung cancer (SCLC) is one of the most aggressive cancers, yet the molecular mechanisms underlying its devastating clinical outcome remain elusive. In this study, we investigated whether microRNA (miRNA) expression profiles can predict the clinical outcomes of SCLC patients. A total of 82 patients with limited SCLC, who were treated with surgical resection and adjuvant chemotherapy, were enrolled in this study. First, we surveyed the expression of 924 miRNAs from 42 SCLC patients to discover survival-relevant miRNAs and develop prognostic models, which were then validated in an independent cohort of 40 cases using quantitative real-time PCR. We found that the miR-150/miR-886-3p signature was significantly correlated with the overall survival (OS) of SCLC patients (p = 0.02) in the training set, and both miRNA expression levels were much lower in the SCLC samples than normal lung samples. The miRNA signature also proved to be a significant predictor of survival in the validation set. Patients with high-risk miRNA signatures had poor overall survival (p = 0.005) and progression-free survival (p = 0.017) compared with those with low-risk scores. These findings retained statistical significance after adjusting for age, gender and smoking status (HR: 0.26, 95%: CI 0.10–0.69, p = 0.007), which suggested it may be an independent predictor of survival. In summary, we developed a prognostic miR-150/miR-886-3p signature and validated expression in an independent dataset of resectable SCLC. These preliminary results indicated that miRNAs may serve as promising molecular prognostic markers and new therapeutic targets for SCLC. [ABSTRACT FROM AUTHOR]

Purpose: Poor pulmonary function (PF) is often considered a contraindication to definitive radiation therapy for lung cancer. This study investigated whether baseline PF was associated with radiation-induced lung toxicity (RILT) in patients with non-small cell lung cancer (NSCLC) receiving conformal radiation therapy (CRT). Methods and Materials: NSCLC patients treated with CRT and tested for PF at baseline were eligible. Baseline predicted values of forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), and diffusion capacity of lung for carbon monoxide (DLCO) were analyzed. Additional factors included age, gender, smoking status, Karnofsky performance status, coexisting chronic obstructive pulmonary disease (COPD), tumor location, histology, concurrent chemotherapy, radiation dose, and mean lung dose (MLD) were evaluated for RILT. The primary endpoint was symptomatic RILT (SRILT), including grade ≥2 radiation pneumonitis and fibrosis. Results: There was a total of 260 patients, and SRILT occurred in 58 (22.3%) of them. Mean FEV1 values for SRILT and non-SRILT patients were 71.7% and 65.9% (P=.077). Under univariate analysis, risk of SRILT increased with MLD (P=.008), the absence of COPD (P=.047), and FEV1 (P=.077). Age (65 split) and MLD were significantly associated with SRILT in multivariate analysis. The addition of FEV1 and age with the MLD-based model slightly improved the predictability of SRILT (area under curve from 0.63-0.70, P=.088). Conclusions: Poor baseline PF does not increase the risk of SRILT, and combining FEV1, age, and MLD may improve the predictive ability. [Copyright &y& Elsevier]

Abstract: The purpose of this study was to compare the biochemical and biological properties of nonstructural protein (nsp) 15 among mouse hepatitis virus (MHV), severe acute respiratory syndrome coronavirus (SARS-CoV) and transmissible gastroenteritis virus (TGEV) in virus-infected and ectopically expressed cells. In virus-infected cells, MHV nsp15 distributed unevenly throughout the cytoplasm but predominantly in the perinuclear region. When expressed as N-terminal enhanced green fluorescence protein (EGFP) fusion, it predominantly formed speckles in the cytoplasm. In contrast, SARS-CoV and TGEV EGFP-nsp15s distributed smoothly in the whole cell and did not form speckles. Deletion mapping experiments identified two domains responsible for the speckle formation in MHV EGFP-nsp15: Domain I (aa101–150) and Domain III (aa301–374). Interestingly, Domain II (aa151–250) had an inhibitory effect on Domain III- but not on Domain I-mediated speckle formation. Expression of a small (35aa) sequence in Domain III alone was sufficient to form speckles for all 3 viral nsp15s. However, addition of surrounding sequences in Domain III abolished the speckle formation for TGEV nsp15 but not for MHV and SARS-CoV nsp15s. Further domain swapping experiments uncovered additional speckle-inducing and -suppressive elements in nsp15s of SARS-CoV and TGEV. Homotypic interaction involving Domain III of MHV nsp15 was further demonstrated biochemically. Moreover, the biological functions of the expressed nsp15s were assessed in MHV-infected cells. It was found that the effects of EGFP-nsp15s on MHV replication were both virus species- and nsp15 domain-dependent. Collectively these results thus underscore the differential biochemical and biological functions among the nsp15s of MHV, TGEV and SARS-CoV in host cells. [Copyright &y& Elsevier]

Low-light images usually contain high noise and low contrast. This brings bad visual feelings and hinders subsequent computer vision work. At present, many algorithms have been proposed to enhance low-light images. However, the existing methods still have some problems, such as insufficient enhancement, color distortion, or overexposure. In this paper, we propose a low-light image enhancement network based on the spatial pyramid to solve the problems existing in other methods, so as to make the enhancement result closer to the normal illumination image in brightness and color. The network is divided into two parts. Firstly, the decomposition network is designed based on Retinex theory, and the image is decomposed into the illumination image and reflection image. Then, the illumination image is processed through the three convolution kernels on the spatial pyramid module to obtain three sets of features with different scales. Next, we concatenate these three groups of features together. And the concatenated features are extracted through a convolution kernel to obtain the enhanced illumination image. Finally, the enhanced illumination image and the decomposed reflection image are multiplied pixel by pixel to obtain an enhanced image. In addition, we introduce a color loss function to solve the problem of color distortion. The experimental results show that the proposed algorithm has better visual feelings than other algorithms. We also calculate the peak signal-to-noise ratio, structural similarity index and average brightness of the enhanced results of different algorithms, and the results show that the proposed algorithm performs better. [ABSTRACT FROM AUTHOR]

Abstract: The complete nucleotide sequence of polyprotein gene 1 and the assembled full-length genome sequence are presented for turkey coronavirus (TCoV) isolates 540 and ATCC. The TCoV polyprotein gene encoded two open reading frames (ORFs), which are translated into two products, pp1a and pp1ab, the latter being produced via −1 frameshift translation. TCoV polyprotein pp1a and pp1ab were predicted to be processed to 15 non-structure proteins (nsp2–nsp16), with nsp1 missing. ClustalW analysis revealed 88.99% identity and 96.99% similarity for pp1ab between TCoV and avian infectious bronchitis virus (IBV) at the amino acid level. The whole genome consists of 27,749nucleotides for 540 and 27,816nucleotides for ATCC, excluding the poly(A) tail. A total of 13 ORFs were predicted for TCoV. Five subgenomic RNAs were detected from ATCC-infected turkey small intestines by Northern blotting. The whole genome sequence had 86.9% identity between TCoV and IBV, supporting that TCoV is a group 3 coronavirus. [Copyright &y& Elsevier]

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective. [ABSTRACT FROM AUTHOR]

Reliable biomarkers for the prognosis of hepatocellular carcinoma (HCC) are rare, and novel biomarkers are required for the appropriate management of HCC. 5′-Nucleotidase domain containing 2 (NT5DC2) acts as an oncogene in various tumors, but its functions as a biomarker have not been confirmed. Therefore, the present study aimed to resolve these functions by analyzing the prognostic value of NT5DC2 in patients with HCC. A tissue microarray (TMA) was prepared and NT5DC2 expression was measured via IHC staining in TMA dots. The liver cancer (LIHC) cohort in The Cancer Genome Atlas (TCGA) was enrolled as a secondary cohort. Kaplan-Meier survival analyses and Cox regression models were used for assessment of the prognostic value of NT5DC2. Gene set enrichment analysis (GSEA) was performed in TCGA LIHC cohort. A total of 134 patients with HCC were retrospectively enrolled in the Peking Union Medical College Hospital cohort and clinical data were collected. A total of 359 patients with HCC in TCGA were enrolled as TCGA cohort. NT5DC2 was used as an indicator of overall survival (OS) and relapse-free survival (RFS) in multiple cohorts. In the multivariate Cox regression model, NT5DC2 upregulation was an independent prognostic factor of OS in both cohorts. GSEA indicated the enrichment of a series of survival- and metastasis-related gene-sets, such as LEE_LIVER_CANCER_SURVIVAL_UP and LIAO_METASTASIS. Collectively, it was suggested that NT5DC2 upregulation was associated with poor OS and RFS in HCC, and was a potential predictive marker for HCC stratification. [ABSTRACT FROM AUTHOR]

Multi‐scale feature fusion is often used to address the issue of scale variations in object detection. However, most of the proposed network architectures only combine the features of two adjacent levels sequentially, so the first fusion nodes in both top‐down and bottom‐up pathways must be blank nodes that only have one input with no feature fusion. In this work, cross‐scale feature fusion connection (CFFC) is proposed which aims to enhance the entire feature hierarchy by propagating the features of each level more efficiently. The proposed method reuses and aggregates all the features of other scales to the blank nodes in both top‐down and bottom‐up pathways. Furthermore, the authors remove the 1 × 1 convolutional layer and replace the shortcut with concatenation before fusing multiple features. These concatenated feature maps are then supervised by the channel attention block at the fusion nodes. This modification allows the network to learn the important degree of each level in concatenated feature maps along the channel dimension. It is also observed that the proposed method alleviates the inconsistency in feature pyramids with fewer parameters. The performance of a YOLO object detector equipped with the proposed method on the COCO test‐dev 2017 is evaluated. The results show that the proposed method outperforms other architectures presented in the literature. [ABSTRACT FROM AUTHOR]

BACKGROUND: Aspartate aminotransferase (AST), an indicator of liver cell damage, was related to the prognosis of certain malignant tumors. OBJECTIVE: This study examined the predictive value of AST in patients with extranodal natural killer/T cell lymphoma (ENKTL). METHODS: We reviewed 183 cases diagnosed with ENKTL and selected 26 U/L as the optimum cut-off value of AST. We used the univariate and multivariate Cox regression to compare the different AST groups' overall survival (OS) and progression-free survival (PFS). RESULTS: Prior to propensity score matching (PSM), Kaplan-Meier analysis showed that patients in the low AST subgroup had better OS and PFS than the high AST subgroup. Multivariate analysis revealed that AST was an independent indicator for prognosis. After PSM, the low AST subgroup maintained a significantly better OS and PFS than the high AST subgroup. CONCLUSION: AST might represent a significant prognostic marker for ENKTL patients. [ABSTRACT FROM AUTHOR]

Given that existing salient object detection methods cannot effectively predict the fine contours of salient objects when extracting local or global contexts and features, we propose a novel contour self-compensated network (CSCNet) to generate a more accurate saliency map with complete contour. Unlike the common binary saliency detection, we reconstruct the salient object detection problem into a multi-classification problem of the background, the salient object, and the salient contour, where the salient contour is used as the third label for ground truth. Meanwhile, the image and its superpixel map are concatenated as the input of our network to add more edge information. Also, a penalty loss is proposed to restrict the spatial relationship between the background, objects, and their contours. Experimentally, we evaluate the proposed CSCNet on six benchmark datasets in both accuracy and efficiency and evaluate the attribute-based performance on the SOC dataset. Compared with 13 state-of-the-art algorithms, our CSCNet can detect salient objects more accurately and completely without adding too many convolutional layers and parameters. [ABSTRACT FROM AUTHOR]

BACKGROUND: At present, peripheral blood markers are easily accessible information and clinically valuable prognostic indicators in extranodal nasal-type natural killer/T-cell lymphoma (ENKTCL). Nevertheless, the role of its comprehensive score in ENKTCL remains to be determined. OBJECTIVE: Therefore, this study aimed to investigate the prognostic effect of the peripheral inflammation score on ENKTCL. METHODS: The retrospective study included 183 patients with ENKTCL. Univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO) Cox regression were used to construct the inflammation-related prognostic index named Risk. Univariate and multivariate Cox regression analyses and regression adjustment with propensity score matching (PSM) were used to evaluate the prognostic ability of risk. The performance of the modified nomogram-revised risk index (NRI) by integrating risk was evaluated with the area under the time-dependent receiver operating characteristic (ROC) curve (AUC), decision curve analysis (DCA), and integrated Brier score (IBS). RESULTS: The risk cut-off value, constructed by the lymphocyte count, platelet count, albumin level, LMR, and PNI, was - 1.3486. Before PSM, multivariate analysis showed that risk was significantly associated with OS (HR = 2.577, 95% CI = 1.614–4.114, P < 0.001) and PFS (HR = 2.679, 95% CI = 1.744–4.114, P < 0.001). After PSM adjustment, risk was still an independent factor for OS (HR = 2.829, 95% CI = 1.601–5.001, P < 0.001) and PFS (HR = 2.877, 95% CI = 1.735–4.770, P < 0.001). With the NRI, the modified NRI by integrating risk increased the AUC and clinical net benefit and decreased the IBS. CONCLUSIONS: Risk is an easily accessible and inexpensive indicator that may be used as a prognostic marker and could improve NRI predictive power in patients with ENKTCL. [ABSTRACT FROM AUTHOR]

The precision of the camera calibration is one of the key factors that affect attitude measurement accuracy in many computer vision tasks. This study proposes a new calibration approach for binocular cameras. Firstly, based on singular value decomposition, the best transformation matrix to the essential matrix is approximated as the initial guess, which is solved in using the Frobenius norm. Secondly, the initial guess is refined through maximum likelihood estimation. A new calculating expression is derived for computing the relative position matrix of the binocular cameras. The Levenberg–Marquardt algorithm is then implemented to refine the initial guess. Large sets of synthesised and real point correspondences were tested to demonstrate the validity of the proposed method. Extensive experiments demonstrated that the proposed method outperforms the state‐of‐the‐art methods. The error rate of the proposed method was 0.5% for the length test and about 1% for the angle test at a range of 1 m. This method can advance three‐dimensional (3D) computer vision one additional step from laboratory environments to real‐world use. [ABSTRACT FROM AUTHOR]

An outstanding calibration algorithm is the most important factor that affects the precision of attitude measurement. This study proposes a non‐linear optimisation algorithm to refine the solutions of the initial guess obtained using the Zhang's technique, the Bouget's technique, or the Hartley's algorithm. Large sets of point correspondences were adopted to test the validity of the proposed method. Extensive practical experiments demonstrated that the proposed method can significantly improve the accuracy of calibration and ultimately obtains higher measurement precision. The error of the reprojection in the proposed method was <0.13 px. At a range of 1 m, the error rate was 0.5% for the length test and about 3% for the angle test. This study proposes a new method to calibrate the relationship between laser radar and the camera. Binocular vision was used to reconstruct the point cloud of the non‐cooperative target. At the same time, data was also obtained using laser radar. Finally, the two groups of systems were fused. Accurate and dense three‐dimensional information of the target was obtained. It could not only obtain the dense pose information of the target surface but also the texture and colour feature information of the target surface. [ABSTRACT FROM AUTHOR]

Purpose: Ovarian cancer is the leading cause of gynecologic cancer-related death worldwide. Early diagnosis of ovarian cancer can significantly improve patient prognosis. Hence, there is an urgent need to identify key diagnostic and prognostic biomarkers specific for ovarian cancer. Because high-grade serous ovarian cancer (HGSOC) is the most common type of ovarian cancer and accounts for the majority of deaths, we identified potential biomarkers for the early diagnosis and prognosis of HGSOC. Methods: Six datasets (GSE14001, GSE18520, GSE26712, GSE27651, GSE40595, and GSE54388) were downloaded from the Gene Expression Omnibus database for analysis. Differentially expressed genes (DEGs) between HGSOC and normal ovarian surface epithelium samples were screened via integrated analysis. Hub genes were identified by analyzing protein–protein interaction (PPI) network data. The online Kaplan-Meier plotter was utilized to evaluate the prognostic roles of these hub genes. The expression of these hub genes was confirmed with Oncomine datasets and validated by quantitative real-time PCR and Western blotting. Results: A total of 103 DEGs in patients with HGSOC—28 upregulated genes and 75 downregulated genes—were successfully screened. Enrichment analyses revealed that the upregulated genes were enriched in cell division and cell proliferation and that the downregulated genes mainly participated in the Wnt signaling pathway and various metabolic processes. Ten hub genes were associated with HGSOC pathogenesis. Seven overexpressed hub genes were partitioned into module 1 of the PPI network, which was enriched in the cell cycle and DNA replication pathways. Survival analysis revealed that MELK, CEP55 and KDR expression levels were significantly correlated with the overall survival of HGSOC patients (P < 0.05). The RNA and protein expression levels of these hub genes were validated experimentally. Conclusion: Based on an integrated analysis, we propose the further investigation of MELK, CEP55 and KDR as promising diagnostic and prognostic biomarkers of HGSOC. [ABSTRACT FROM AUTHOR]

Non‐rigid point set registration is a key component in many computer vision and pattern recognition tasks. In this study, the authors propose a robust non‐rigid point set registration method based on high‐dimensional representation. Their central idea is to map the point sets into a high‐dimensional space by integrating the relative structure information into the coordinates of points. On the one hand, the point set registration is formulated as the estimation of a mixture of densities in high‐dimensional space. On the other hand, the relative distances are used to compute the local features which assign the membership probabilities of the mixture model. The proposed model captures discriminative relative information and enables to preserve both global and local structures of the point set during matching. Extensive experiments on both synthesised and real data demonstrate that the proposed method outperforms the state‐of‐the‐art methods under various types of distortions, especially for the deformation and rotation degradations. [ABSTRACT FROM AUTHOR]

Background: Lung immune prognostic index (LIPI) is a prognostic marker of extensive-stage small cell lung cancer (ES-SCLC) patients received immunotherapy or chemotherapy. However, its ability in limited-stage SCLC (LS-SCLC) should be evaluated extensively.Methods: We retrospectively enrolled 497 patients diagnosed as LS-SCLC between 2015 and 2018, and clinical data included pretreatment lactate dehydrogenase (LDH), white blood cell count, and absolute neutrophil count levels were collected. According to the LIPI scores, the patients were stratified into low-risk (0 points) and high-risk (1-2 points). The correlations between LIPI and overall survival (OS) or progression-free survival (PFS) were analyzed by the Cox regression. Additionally, the propensity score matching (PSM) and inverse probability of treatment weight (IPTW) methods were used to reduce the selection and confounding bias. A nomogram was constructed using on multivariable Cox model.Results: Two hundred fifty and 247 patients were in the LIPI high-risk group and low-risk group, and their median OS was 14.67 months (95% CI: 12.30-16.85) and 20.53 months (95% CI: 17.67-23.39), respectively. In the statistical analysis, High-risk LIPI was significantly against worse OS (HR = 1.377, 95%CI:1.114-1.702) and poor PFS (HR = 1.338, 95%CI:1.1-1.626), and the result was similar after matching and compensating with the PSM or IPTW method. A novel nomogram based on LIPI has a decent level of predictive power.Conclusion: LIPI stratification was a significant factor against OS or PFS of LS-SCLC patients. [ABSTRACT FROM AUTHOR]

Simple Summary: The brain is a common metastasis site of small-cell lung cancer (SCLC), and up to 50% of SCLC patients are at risk of brain metastasis (BM) within 2 years. Prophylactic cranial irradiation (PCI), as an essential treatment for reducing the risk of BM, inevitably leads to neurotoxicity. Differentiating the risk of BM in patients and individualized PCI treatment decisions may play an essential role in reducing the occurrence of BM, prolonging the overall survival, and improving the quality of life. Our study constructed and validated a clinical model to predict the incidence of BM and risk stratification for individualized PCI decisions. PCI could reduce the incidence of BM and improve overall survival (OS) in patients with a high risk of BM, but there was no significant difference between PCI and non-PCI groups in patients within a low-risk cohort. Prophylactic cranial irradiation (PCI), as an essential part of the treatment of limited-stage small-cell lung cancer (LS-SCLC), inevitably leads to neurotoxicity. This study aimed to construct a brain metastasis prediction model and identify low-risk patients to avoid PCI; 236 patients with LS-SCLC were retrospectively analyzed and divided into PCI (63 cases) and non-PCI groups (173 cases). The nomogram was developed based on variables determined by univariate and multivariate analyses in the non-PCI group. According to the cutoff nomogram score, all patients were divided into high- and low-risk cohorts. A log-rank test was used to compare the incidence of brain metastasis between patients with and without PCI in the low-risk and high-risk groups, respectively. The nomogram included five variables: chemotherapy cycles (ChT cycles), time to radiotherapy (RT), lactate dehydrogenase (LDH), pro-gastrin-releasing peptide precursor (ProGRP), and lymphocytes–monocytes ratio (LMR). The area under the receiver operating characteristics (AUC) of the nomogram was 0.763 and 0.782 at 1 year, and 0.759 and 0.732 at 2 years in the training and validation cohorts, respectively. Based on the nomogram, patients were divided into high- and low-risk groups with a cutoff value of 165. In the high-risk cohort, the incidence of brain metastasis in the non-PCI group was significantly higher than in the PCI group (p < 0.001), but there was no difference in the low-risk cohort (p = 0.160). Propensity score-matching (PSM) analysis showed similar results; the proposed nomogram showed reliable performance in assessing the individualized brain metastasis risk and has the potential to become a clinical tool to individualize PCI treatment for LS-SCLC. [ABSTRACT FROM AUTHOR]

• The eighth edition of the TNM classification showed improved prognostic accuracy compared with the seventh edition in SCLC. • Patients with N3 contralateral supraclavicular nodes had a significantly worse prognosis than those without. • For patients with single-site metastasis, liver involvement showed a worse prognosis compared to brain involvement. The newly released eighth edition of the American Joint Committee on Cancer TNM staging system for lung cancer seeks to improve prognostic accuracy but lacks external validation for small cell lung cancer (SCLC). Moreover, previous studies posed a few questions concerning survival differences for patients with specific site N3 node involvement or single-site metastasis (SSM) in different distant organs. The aim of this study was to validate the eighth edition of the TNM classification for SCLC in an independent multi-institutional cohort from China and answer the questions raised by the previous research. Patients with SCLC from four Chinese cancer centers between 2009 and 2019 were reclassified according to the seventh and eighth edition of the TNM classification. Survival was estimated using the Kaplan-Meier method. Comparisons between adjacent categories and stage groups were performed using Cox proportional hazard regression. R2 statistics were calculated to evaluate the discriminating performance of editions. Of 3384 enrolled cases, 3358 had clinical stage, 537 had pathological stage, and 511 had both. Progressive deterioration of survival was observed with advancing of TNM categories and stages both in the seventh and the eighth edition. The eighth edition stages had a higher R2 statistic than the seventh edition (0.207 versus 0.197). Newly defined categories M1b and M1c and stages IIIC, IVA and IVB in the eighth edition discriminated groups with significantly different prognosis. Patients with N3 contralateral supraclavicular nodes had a significantly worse prognosis than those without (p = 0.032). For patients with single-site metastasis, liver involvement showed a worse prognosis compared to brain involvement (p = 0.030). Our study provided an external validation of the eighth edition of the TNM classification for lung cancer in Chinese patients with SCLC, and confirmed its improved prognostic accuracy compared with the seventh edition. Patients with N3 and M1b might represent heterogeneous populations that warrant further research. [ABSTRACT FROM AUTHOR]

Chirp-free ultra-short pulses propagating in optical fiber with complex parameters are investigated for the first time. The existence condition for such chirp-free ultrashort pulses is that the imaginary parts of the nonlinear terms, i.e., the nonlinear absorption coefficient ( a 2 i ), nonlinear dispersion coefficient ( a 4 i ) and imaginary Raman coefficient ( a 2 i ) fulfill a linear relationship a 2 i = a 4 i ω = − 2 3 a 5 i ω . Bight solitons can stably propagate in such complex nonlinear optical fiber. It is found that the single Jacobi elliptic function solutions have two free parameters while hybrid Jacobi elliptic function solutions have only one free parameter. [ABSTRACT FROM AUTHOR]

• We present an on-orbit calibration of space camera lens distortion with the vanishing points obtained from a single image of the solar panel. • We propose a parallel-line-extraction method based on collinear constraints to obtain the parallel lines, and train the optimal vanishing point using the common point constraint method. Using the optimal vanishing point, we establish the optimization function of lens distortion based on vanishing point consistency. • We establish the optimization function of lens distortion based on vanishing point consistency and propose an improved genetic algorithm (IGA) to solve the optimization function. • Simulation and real physical experiments demonstrate that the proposed method is flexible and accurate with good anti-noise interference capacity. The method proposed in this paper makes more realistic sense for a number of important space tasks. Since space cameras need to withstand the harsh mechanical and thermal conditions in the space environment for a long time, it is necessary to calibrate them in orbit. However, existing calibration methods have various disadvantages, making them impossible to use in orbit. To address this problem, we present an on-orbit calibration of space camera lens distortion with the vanishing points obtained from a single image of the solar panel. First, we propose a parallel-line-extraction method based on collinear constraints to obtain the parallel lines. Then, we train the optimal vanishing point using the common point constraint method. Using the optimal vanishing point, we establish the optimization function of lens distortion based on vanishing point consistency. Finally, we present an improved genetic algorithm to solve the optimization function. Simulations and experiments show that the proposed method is flexible and robust. [ABSTRACT FROM AUTHOR]

Accurate three-dimensional measurement of potholes is a significant concern in road maintenance. However, the assessment of road potholes still relies heavily on human inspectors to make maintenance suggestions. To realize full-field measurement of pavement pothole automatically, a high-precision volume measurement method based on second-order Taylor expansion is proposed, where the second-order Taylor estimation of each point is converted into a convolution operation. On the one hand, this method discards the second-order fitting of the surface, which greatly reduces the computational complexity. On the contrary, the second-order Taylor estimation is not restricted by the surface shape because it only depends on the distribution of the adjacent points. Experiments on synthesized and real point sets demonstrate that the proposed method outperforms the state-of-the-art methods under various point cloud shapes. [ABSTRACT FROM AUTHOR]

Recent years have witnessed several modified discriminative correlation filter (DCF) models exhibiting excellent performance in visual tracking. A fundamental drawback to these methods is that rotation of the target is not well addressed which leads to model deterioration. In this paper, we propose a novel rotation-aware correlation filter to address the issue. Specifically, samples used for training of the modified DCF model are rectified when rotation occurs, rotation angle is effectively calculated using phase correlation after transforming the search patch from Cartesian coordinates to the Log-polar coordinates, and an adaptive selection mechanism is further adopted to choose between a rectified target patch and a rectangular patch. Moreover, we extend the proposed approach for robust tracking by introducing a simple yet effective Kalman filter prediction strategy. Extensive experiments on five standard benchmarks show that the proposed method achieves superior performance against state-of-the-art methods while running in real-time on single CPU. • A novel framework coping with target rotation is proposed, the new framework benefits from real training samples, enlarged search region and rotation-aware learning. • Log-polar transform and phase correlation are used to effectively rectify target rotation and estimate scale variation. • An adaptive selection mechanism makes the proposed framework leverages the complementary properties of rectified target patch and rectangular target patch. • A simple yet effective Kalman filter prediction strategy is used to cope with out-of-view and full occlusion scenarios. [ABSTRACT FROM AUTHOR]

Abstract: Infection of mouse oligodendrocytes with a recombinant mouse hepatitis virus (MHV) expressing a green fluorescence protein facilitated specific selection of virus-infected cells and subsequent establishment of persistence. Interestingly, while viral genomic RNAs persisted in infected cells over 14 subsequent passages with concomitant synthesis of viral subgenomic mRNAs and structural proteins, no infectious virus was isolated beyond passage 2. Further biochemical and electron microscopic analyses revealed that virions, while assembled, contained little spike in the envelope, indicating that lack of infectivity during persistence was likely due to deficiency in spike incorporation. This type of non-lytic, non-productive persistence in oligodendrocytes is unique among animal viruses and resembles MHV persistence previously observed in the mouse central nervous system. Thus, establishment of such a culture system that can recapitulate the in vivo phenomenon will provide a powerful approach for elucidating the mechanisms of coronavirus persistence in glial cells at the cellular and molecular levels. [ABSTRACT FROM AUTHOR]

• Variational auto-encoderis is firstly used in image fusion field. • The fusion task is performed by using the Gaussian probability density function. • An infrared feature compensation network is proposed in this paper. • Experimental results have shown obvious subjective and objective advantages. With high sensitivity to capture rich details, visible imaging equipment can take images containing more textures and contours which are important to visual perception. Unlike visible cameras, infrared imaging devices can detect targets invisible in visible images, because the imaging principle of infrared sensors derives from differences of thermal radiation. Thus, the purpose of image fusion is to merge as much meaningful feature information from the infrared and visible images into the fused image as possible, such as contours as well as textures of the visible image and thermal targets of the infrared image. In this paper, we propose an image fusion network based on variational auto-encoder (VAE), which performs the image fusion process in deep hidden layers. We divide the proposed network into image fusion network and infrared feature compensation network. Firstly, in the image fusion network, the encoder of the image fusion network is created to generate the latent vectors in hidden layers from the input visible image and infrared image. Secondly, two different latent vectors merge into one based on the product of Gaussian probability density; accordingly, the decoder begins to reconstruct the fused image with the descent of the loss function value. Meanwhile, Residual block and symmetric skip connection methods are added to the network to enhance the efficiency of network training. Finally, due to the defect of the loss function setting in the fusion network, an infrared feature compensation network is designed to compensate critical radiation features of the infrared image. Experimental results on public available datasets demonstrate that the proposed method is superior to other traditional and deep learning methods in both objective metrics and subjective visual perception. [ABSTRACT FROM AUTHOR]

• An edge-preserving guided filter named EPGF is proposed, which takes the image gradient map into consideration. • Salient feature layers of IR and VIS images will be respectively obtained by using EPGF and a novel IR saliency map. • The proposed luminance layer reflecting the overall brightness of the image background is used to compensate the brightness of the fused image. • A new optimization model of image fusion is designed to optimize the initial fused image by using ADMM. Infrared (IR) images can distinguish salient targets from their backgrounds based on the radiation difference in all-weather conditions. By contrast, visible (VIS) images can contain high-resolution texture and details information, which is more suitable for human observation. Therefore, it is quite necessary to combine both imaging advantages of these two kinds of images. Compared with the existing methods, we believe that scale decomposition based methods are the most active and efficient image fusion methods, which also have the best fusion effects. Inspired by the present scale decomposition methods, we propose a new feature decomposition method. Firstly, we propose an improved guided filter called edge-preserving guided filter (EPGF), which adopts the image gradient map for further improving the filtering effect. Subsequently, by using the EPGF, we decompose the IR and VIS images into three kinds of layers, including salient feature layers, luminance layers and detail layers. At the same time, we combine all the layers together to get an initial fusion result. Finally, we optimize the initial fusion image according to a new image fusion optimization model and ADMM, and the final fusion result will be obtained after several iterations. Compared with other scale decomposition methods, our proposed feature decomposition based method takes the IR salient targets, IR and VIS background illumination, as well as VIS details into consideration which is more in line with human visual observation, besides the computational efficiency is also superior. Experimental results indicate that this method has better subjective and objective evaluation results compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Weighted variance guided filter is introduced in the image decomposition stage. • Non-Local Means(NLM) is used in the visible image detail layer enhancement. • Image division along the vertical direction is proposed in the fusion stage. • Multiple sub-fused results are combined to form the final fused image. With extraordinary advances in sensor technology, infrared and visible image fusion has been widely used in both military and civilian applications. In this paper, we propose a novel image fusion method based on decomposition and division based strategy. The proposed method improves the guided filter to better decompose images and restrict artifacts around image boundaries. Furthermore, because the quality of visible images is easily affected by low light conditions and noises, it is necessary to enhance the contrast of visible images to improve the visual quality before applying image fusion. Subsequently, we divide the infrared and visible image into several sub-images in vertical direction, because there is more similar image content in this direction such as the sky and land. Additionally, each sub-image is decomposed into a base layer and a detail layer. Different from previous methods, the fusion in our proposed method is executed by two different strategies, one takes the sub infrared base layer as the main image to get the fusion result, while the other one takes the sub visible base layer as the main image, and two different sub-fusion results are obtained. We also propose a new fusion strategy called gradient-brightness criterion to adaptively output the final fused image. As a result, the fused image preserves more details of visible image and clearer infrared objects at the same time, which is well suited for human visual perception. Experimental results indicate that our proposed method achieves a superior performance compared with previous fusion methods in both subjective and objective assessments. [ABSTRACT FROM AUTHOR]

Long-term tracking is one of the most challenging problems in computer vision. In this paper, we make full use of the Discriminative Correlation Filter (DCF), and propose a real-time long-term tracker by exploiting a joint tracking–verification–detection–refinement framework. We utilize a DCF which is updated aggressively to estimate translation and scale variation of the target. Subsequently, a passively updated DCF checks the reliability of the tracking result. Once the result is not reliable, we evoke the proposed optimized candidate detector to generate a small number of relatively high quality candidates. Finally, one DCF with an adaptive online learning rate is adopted to refine the predictions that the sparse candidates inferred. In addition, we employ a selection mechanism for the correlation responses to maintain reliable samples effectively. Extensive experiments show that the proposed method performs favorably against lots of state-of-the-art methods while running more than 30 frames per second on single CPU. • Joint tracking–verification–detection–refinement framework is proposed for real-time long-term tracking. • Proposed optimized candidate detector can both coarsely predict the target position and significantly reduce the number of candidates that need to be verified. • Selection mechanism makes the proposed framework benefit from the passive–aggressive learning of the filter models. • Verification module, optimized candidate detector, and refinement module are updated passively to prevent the corruption of the filter models. • Optimized candidate detector avoids the high computational complexity of the sliding window search and the blindness of the particle sampling. [ABSTRACT FROM AUTHOR]

Abstract: Use of carbon dioxide controlled atmospheres has proven to be effective for the preservation of cereal grain during storage, but its preservation mechanisms remain to be fully elucidated. In this study, preservation mechanism of high concentration carbon dioxide controlled atmosphere for paddy rice storage was evaluated based on quality analyses and a novel method using molecular modeling tools. During 90 days storage, the fat acidity, cooked rice hardness, and lipoxygenase activity of paddy rice were gradually increased and alpha-amylase activity was decreased rapidly, but the degree of change of the paddy rice stored in controlled atmosphere was lower than that in non-controlled atmosphere. The rate constants for hydration water disaggregation from biological macromolecules, such as proteins and starch in paddy rice, and isothermal kinetic parameters were calculated according to simple first-order kinetics and the Arrhenius's law. The hydration water of paddy rice stored in non-controlled atmosphere presented the higher rate constant for degradation, especially at higher temperatures. Values of activation energies were 28.27 and 15.80 kJ/mol, respectively, for hydration water of paddy rice stored in controlled and non-controlled atmosphere. The mechanism of controlled atmospheres for paddy rice preservation may be the higher of activation energies for hydration water or biological macromolecules. Highlights • Mechanism of high concentration carbon dioxide controlled atmosphere storage for rice preservation was evaluated. • First-order rate constants for hydration water disaggregation were calculated. • Isothermal kinetic parameters were calculated according to the Arrhenius's law. • Hydration water of paddy stored in high concentration carbon dioxide controlled atmosphere has higher activation energy. [ABSTRACT FROM AUTHOR]

Background Several indices have been developed to predict survival of brain metastases ( BM) based on prognostic factors. However, such models were designed for general brain metastases from different kinds of cancers, and prognostic factors vary between cancers and histological subtypes. Recently, studies have indicated that epidermal growth factor receptor ( EGFR) mutation status may be a potential prognostic biological factor in BM from lung adenocarcinoma. Thus, we sought to define the role of EGFR mutation in prognoses and introduce a prognostic model specific for BM from lung adenocarcinoma. Methods Data of 256 patients with BM from lung adenocarcinoma identified with EGFR mutations were collected. Independent prognostic factors were confirmed using a Cox regression model. The new prognostic model was developed based on the results of multivariable analyses. The score of each factor was calculated by six-month survival. Prognostic groups were divided into low, medium, and high risk based on the total scores. The prediction ability of the new model was compared to the three existing models. Results EGFR mutation and Karnofsky performance status were independent prognostic factors and were thus integrated into the new prognostic model. The new model was superior to the three other scoring systems regarding the prediction of three, six, and 12-month survival by pairwise comparison of the area under the curve. Conclusion Our proposed prognostic model specific for BM from lung adenocarcinoma incorporating EGFR mutation status was valid in predicting patient survival. Further verification is warranted, with prospective testing using large sample sizes. [ABSTRACT FROM AUTHOR]

Measuring the pose of non-cooperative targets in space is a critical supporting technology for cleaning up space debris and recovering items. However, most existing methods are simulation experiments conducted in good lighting environments and tend to show poor performance in dark lighting environments. A target pose measurement method based on binocular vision is proposed, which is suitable for dark lighting environments. First, the traditional features from accelerated segment test algorithm are improved to reduce the influence of illumination on the performance of feature point extraction under various postures. The point feature and line feature are combined to extract image features more easily in a dark lighting environment while retaining the high accuracy of the pose measurement algorithm based on point features. Second, the normalized cross-correlation coefficient matching method is combined with the epipolar constraint to narrow the search range of the matching points from the two-dimensional plane to the epipolar line, which substantially improves the matching efficiency and accuracy of the matching algorithm. Finally, post-processing through feature matching is performed to reduce the probability of mismatches. Simulation and physical experiment results show that our method can stably extract features and obtain high-precision target pose information in well-illuminated as well as dark lighting environments, making it suitable for high-precision target pose measurement under insufficient illumination. [ABSTRACT FROM AUTHOR]

Background: Ovarian cancer is the most lethal type of malignant tumor in gynecological cancers and is associated with a high percentage of late diagnosis and chemotherapy resistance. Thus, it is urgent to identify a tumor marker or a molecular target that allows early detection and effective treatment. RNA-binding proteins (RBPs) are crucial in various cellular processes at the post-transcriptional level. The eukaryotic translation initiation factor 4 gamma, 1(eIF4G1), an RNA-binding protein, facilitates the recruitment of mRNA to the ribosome, which is a rate-limiting step during the initiation phase of protein synthesis. However, little is known regarding the characteristics of eIF4G1 expression and its clinical significance in ovarian cancer. Therefore, we propose to investigate the expression and clinicopathological significance of eIF4G1 in ovarian cancer patients. Methods: We performed Real-time PCR in 40 fresh serous ovarian cancer tissues and 27 normal ovarian surface epithelial cell specimens to assess eIF4G1mRNA expression. Immunohistochemistry (IHC) was used to examine the expression of eIF4G1 at the protein level in 134 patients with serous ovarian cancer and 18 normal ovarian tissues. Statistical analysis was conducted to determine the correlation of the eIF4G1 protein levels with the clinicopathological characteristics and prognosis in ovarian cancer. Results: The expression of eIF4G1 was upregulated in serous ovarian cancer tissues at both the mRNA (P = 0.0375) and the protein (P = 0.0007) levels. The eIF4G1 expression was significantly correlated with the clinical tumor stage (P = 0.0004) and omentum metastasis (P = 0.024). Moreover, patients with low eIF4G1 protein expression had a longer overall survival time (P = 0.026). Conclusions: These data revealed that eIF4G1 is markedly expressed in serous ovarian cancer and that upregulation of the eIF4G1 protein expression is significantly associated with an advanced tumor stage. Besides, the patients with lower expression of eIF4G1 tend to have a longer overall survival time. Thus, eIF4G1 may contribute to the occurrence and metastasis of ovarian cancer and can serve as a potential therapeutic target for the treatment of ovarian cancer. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) bismuth-based semiconductors have aroused intensive concern owing to their prominent photocatalytic activity for organic pollutants removal. In this work, a facile strategy for introducing oxygen vacancy in Bi-based oxyiodides (Bi x O y I z) sheet-like architectures to activate molecular oxygen was proposed. The structure, photoelectric properties and visible light (λ > 420 nm) induced photocatalytic activities of these samples for decomposition of bisphenol A (BPA) were systematically characterized and evaluated. The as-prepared Bi 7 O 9 I 3 with a feeding Bi/I molar ratio of 1:1 exhibited the best photocatalytic activity comparable to those of similarly synthesized Bi 7 O 9 I 3 with other molar ratios and BiOIO 3 catalysts. The optimal Bi 7 O 9 I 3 achieved excellent photocatalytic activity with 99.6 % degradation efficiency of BPA within 20 min and superior structural stability with 95.1 % degradation retention over 5 cycling tests. In addition, the resulting Bi 7 O 9 I 3 sample displayed a high mineralization efficiency of BPA. Importantly, the plenty of oxygen vacancies (V o s) exsiting in Bi 7 O 9 I 3 played the dominant role in both accelerating electron transfer and activating molecular oxygen to facilitate the generation of superoxide radical (O 2 ·-) and singlet oxygen (1O 2), thereby proceeding oxidative degradation of BPA molecules during photoreactions. The efforts and attempts are also extendable to synthesis other 2D photocatalysts, providing potential for effective charge-carrier separation and molecular oxygen activation. [Display omitted] • Highly efficient 2D Bi 7 O 9 I 3 nanoplates with V o s were synthesized. • The introduction of V o s in Bi 7 O 9 I 3 accelerated electron transfer and separation. • The photodegradation capacity of Bi 7 O 9 I 3 was 53.8 times higher than that of BiOIO 3. • O 2 ·-, h+ and 1O 2 played critical roles for photodecomposition of BPA. • The electron transport pathway and degradation mechanism were discussed. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To evaluate the activity and safety of concurrent thoracic radiotherapy (TRT) plus weekly paclitaxel/carboplatin (PC) regimen compared with widely used cisplatin/etoposide (PE) regimen in patients with unresectable stage III non-small cell lung cancer (NSCLC). Patients and methods: Patients were randomly assigned to receive the following treatments: PE arm, cisplatin (50mg/m2) on days 1, 8, 29, and 36 and etoposide (50mg/m2) on days 1–5 and 29–33 plus 60Gy of TRT; PC arm, weekly concurrent carboplatin (AUC=2) and paclitaxel (45mg/m2) plus 60Gy of TRT. Results: A total of 65 patients were randomized (PE arm, n =33; PC arm, n =32). The 3-year overall survival (OS) was significantly better in the PE arm than in the PC arm (33.1% vs. 13%, P =.04). The incidence of Grade 3/4 neutropenia was 78.1% in the PE arm and 51.5% in the PC arm (P =.05). The rate of Grade 2 or greater radiation pneumonitis was 25% in the PE arm and 48.5% in the PC arm (P =.09). Conclusions: Compared to PE regimen, weekly PC regimen cannot be recommended since it failed to achieve an improvement in either OS or PFS. [Copyright &y& Elsevier]

Grain quality is one of the key targets for rice breeding. Two-meter-high giant rice, Xiangju-1, is a new high-quality restorer line of hybrid rice. Here, the grain quality and storability of three varieties Jufeng 5 (JF5), Jufeng 6 (JF6), and Jufeng 8 (JF8) were studied. The grain sizes of three giant hybrid rice varieties (JFs) were larger than that of the control rice Jingliangyou 1212 (1212). With respect to nutritional value, the three JFs varieties had similar protein content (~8%) but much higher fat content (>0.8%) than 1212. In addition, JF6 had better storability than JF8 when assessed by controlled deterioration treatment (CDT). RNA-seq analysis revealed that the heatmaps of differentially expressed genes in JF6 and JF8 were similar after 20 days of CDT when compared with 0 days. Of particular interest were the different mRNA levels of several genes related to the pathways of DNA damage response and abscisic acid biosynthesis in JF6 and JF8 after 20 days of CDT. Taken together, the data suggested that JF6 had better grain quality and storability than the other hybrid varieties, and several candidate genes for seed storability were identified. Image 1 • The grain quality and storability of three hybrid varieties were studied. • JF6 had better grain quality and storability than the other hybrid varieties. • Several candidate genes for seed storability were identified in rice. [ABSTRACT FROM AUTHOR]

Attitude measurement is an essential technology in projectile trajectory correction. Magnetometers have been used for projectile attitude measurement systems as they are small in size, lightweight, and low cost. However, magnetometers are seriously disturbed by the artillery magnetic field during launch. Moreover, the error parameters of the magnetometers, which are calibrated in advance, usually change after extended storage. The changed parameters have negative effects on attitude estimation of the projectile. To improve the accuracy of attitude estimation, the magnetometers should be calibrated again before launch or during flight. This paper presents a fast calibration method specific for a spinning projectile. At the launch site, the tri-axial magnetometer is calibrated, the parameters of magnetometer are quickly obtained by optimal ellipsoid fitting based on a least squares criterion. Then, the calibration parameters are used to compensate for magnetometer outputs during flight. The numerical simulation results show that the proposed calibration method can effectively determine zero bias, scale factors, and alignment angle errors. Finally, a semi-physical experimental system was designed to further verify the performance of the calibration method. The results show that pitch angle error reduces from 3.52° to 0.58° after calibration. The roll angle error is reduced from 2.59° to 0.65°. Simulations and experimental results indicate that the accuracy of magnetometer in strap-down spinning projectile has been greatly enhanced, and the attitude estimation errors are reduced after calibration. [ABSTRACT FROM AUTHOR]