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The association between betaine and hypertension risk remains uncertain. Few prospective studies were originally designed to investigate this association.

Intra-abdominal hypertension (IAH) is an important risk factor for organ dysfunction, and it occurs in the early phase of severe acute pancreatitis (SAP). We have reported a novel step-up approach and shown the benefit of performing abdominal paracentesis drainage (APD) ahead of percutaneous catheter drainage (PCD) when treating Patients with SAP with fluid collections. This study aimed to evaluate the efficacy of APD in Patients with SAP complicated with IAH in the early phase.In the present study, 206 AP patients complicated with IAH in the early phase were enrolled in hospital between June 2017 and December 2020. The patients were divided into two groups: 109 underwent APD (APD group) and 97 were managed without APD (non-APD group). We retrospectively compared the outcomes of the APD and non-APD groups for IAH treatment. The parameters including mortality, infection, organ failure, inflammatory factors, indications for further interventions, and drainage-related complications were observed.The demographic data and severity scores of the two groups were comparable. The mortality rate was lower in the APD group (3.7%) than in the non-APD group (8.2%). Compared with the non-APD group, the intra-abdominal pressure and laboratory parameters of the APD group decreased more rapidly, and the mean number of failed organs was lower. However, there was no significant difference in incidence of infections between the two groups.Application of APD is beneficial to AP patients. It significantly attenuated inflammation injury, avoided further interventions, and reduced multiple organ failure.

The general tensor-based methods can recover missing values of multidimensional images by exploiting the low-rankness on the pixel level. However, especially when considerable pixels of an image are missing, the low-rankness is not reliable on the pixel level, resulting in some details losing in their results, which hinders the performance of subsequent image applications (e.g., image recognition and segmentation). In this article, we suggest a novel multiscale feature (MSF) tensorization by exploiting the MSFs of multidimensional images, which not only helps to recover the missing values on a higher level, that is, the feature level but also benefits subsequent image applications. By exploiting the low-rankness of the resulting MSF tensor constructed by the new tensorization, we propose the convex and nonconvex MSF tensor train rank minimization (MSF-TT) to conjointly recover the MSF tensor and the corresponding original tensor in a unified framework. We develop the alternating directional method of multipliers (ADMMs) to solve the convex MSF-TT and the proximal alternating minimization (PAM) to solve the nonconvex MSF-TT. Moreover, we establish the theoretical guarantee of convergence for the PAM algorithm. Numerical examples of real-world multidimensional images show that the proposed MSF-TT outperforms other compared approaches in image recovery and the recovered MSF tensor can benefit the subsequent image recognition.

International audience; Hyperspectral images (HSIs) are of crucial importance in order to better understand features from a large number of spectral channels. Restricted by its inner imaging mechanism, the spatial resolution is often limited for HSIs. To alleviate this issue, in this work, we propose a simple and efficient architecture of deep convolutional neural networks to fuse a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution HSI (HR-HSI). The network is designed to preserve both spatial and spectral information thanks to a new architecture based on: 1) the use of the LR-HSI at the HR-MSI’s scale to get an output with satisfied spectral preservation and 2) the application of the attention and pixelShuffle modules to extract information, aiming to output high-quality spatial details. Finally, a plain mean squared error loss function is used to measure the performance during the training. Extensive experiments demonstrate that the proposed network architecture achieves the best performance (both qualitatively and quantitatively) compared with recent state-of-the-art HSI super-resolution approaches. Moreover, other significant advantages can be pointed out by the use of the proposed approach, such as a better network generalization ability, a limited computational burden, and the robustness with respect to the number of training samples. Please find the source code and pretrained models from https://liangjiandeng.github.io/Projects_Res/HSRnet_2021tnnls.html .

Purpose The purpose of this study is to investigate the enhancement and suppression of heat transfer for hybrid nanofluids (Cu–Al2O3/water) in a square enclosure containing a thermal-conductive cylinder when the Lorentz force is applied to the hybrid nanofluids. Design/methodology/approach Since the inner conductive cylinder in present research has a complex geometry, an in-house meshless method, namely, the local radial basis function (LRBF) method, is applied to solve the 2 dimensional (2D) incompressible Navier–Stokes equation in the fluid domain and Fourier heat conduction equation in solid domain. The solid–fluid interface remains the physical continuity of temperature and heat flux. Only the Lorentz force is considered for the presence of the magnetic field. The conjugate natural convection is assumed to be steady, thus only fully developed heat exchange from the nanofluids to solid or vice versa is comprehensively investigated. Findings It can be concluded that Lorentz force plays a more significant role than hybrid nanofluids in enhancing/suppressing heat transfer when the orientation of magnetic field is the same to the x direction. The thermal conductivity ratio can dramatically change the isotherms and streamlines as well as the mean value of the Nusselt number, resulting in totally different heat transfer phenomena. The included angle of magnetic field also has a significant effect on the heat transfer rate when it changes from horizontal to vertical. Research limitations/implications The constant thermo-physical properties of incompressible fluid and the 2D steady flow are considered in this study. Originality/value The conjugate MHD natural convection of hybrid nanofluids is numerically investigated by an in-house meshless LRBF method. The enhancement and suppression of heat transfer under the combined influence of the volume fraction of nanoparticles, Hartmann number and the thermal conductivity ratio are comprehensively investigated.

Several factors influence axial length in children with myopia treated using overnight orthokeratology. To identify these factors, this retrospective study collected axial length and corneal aberration data on 78 eyes before and 1-year after orthokeratology. Patients were divided according to axial elongation (cut-off, 0.25 mm/year). Baseline characteristics included age, sex, spherical equivalent refraction, pupil diameter, axial length, and orthokeratology lens type. Corneal shape effects were compared through tangential difference maps. Group differences in higher-order aberrations of a 4 mm zone were compared at baseline and 1-year following therapy. Binary logistic regression analysis was conducted to identify the variables determined for axial elongation. Significant differences between both groups included the initial age of wearing orthokeratology lenses, type of orthokeratology lens, size of central flattening area, corneal total surface C12 (1-year), corneal total surface C8 (1-year), corneal total surface spherical aberration (SA) (1-year root mean square [RMS] values), change in total corneal surface C12, and change in front and total corneal surface SA (RMS values). The age when wearing an orthokeratology lens was the most important factor influencing axial length in children with orthokeratology-treated myopia, followed by lens type and change in the C12 of the total corneal surface.

Estimating endmembers and corresponding abundances from mixed pixels are essential steps for hyperspectral unmixing. In hyperspectral unmixing, obtaining accurate unmixing results is difficult since less prior knowledge is available. Besides, the unmixing results are influenced by noise and highly correlated endmembers, so that the obtained abundance maps exist small values which are not present in the image. In this paper, we separate each abundance map into a feature layer and a sparse layer to protect the obtained abundance maps from the above-mentioned factors. The feature layer represents the main information of the abundance map. And the sparse layer contains outliers dominated by the above-mentioned factors. In particular, we design a feature extraction regularization to describe the feature layer and use a weighted l 1 norm to describe the sparse layer. Then, under the framework of non-negative tensor factorization (NTF), we propose a novel unmixing algorithm named spatial feature extraction NTF (SFE-NTF) for hyperspectral unmixing. The proposed SFE-NTF is based on an augmented multiplicative algorithm. Experimental results on both synthetic and real hyperspectral data demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.

There is no consensus on whether triplet regimen is better than doublet regimen in the first-line treatment of advanced gastric cancer (AGC). We aimed to compare the efficacy and safety of oxaliplatin plus capecitabine (XELOX) and epirubicin, oxaliplatin, plus capecitabine (EOX) regimens in treating AGC.This phase III trial enrolled previously untreated patients with AGC who were randomly assigned to receive the XELOX or EOX regimen. The primary endpoint was non-inferiority in progression-free survival (PFS) for XELOX as compared with EOX on an intention-to-treat basis.Between April 10, 2015 and August 20, 2020, 448 AGC patients were randomized to receive XELOX (n = 222) or EOX (n = 226). The median PFS (mPFS) was 5.0 months (95% confidence interval [CI] = 4.5-6.0 months) in the XELOX arm and 5.5 months (95% CI = 5.0-6.0 months) in the EOX arm (hazard ratio [HR] = 0.989, 95% CI = 0.812-1.203; PThis non-inferiority trial demonstrated that the doublet regimen was as effective as the triplet regimen and had a better safety profile and QoL as a first-line treatment for AGC patients. However, the triplet regimen might have a survival advantage in patients with poorly differentiated adenocarcinoma and liver metastasis.

Pansharpening is about fusing a high spatial resolution panchromatic image with a simultaneously acquired multispectral image with lower spatial resolution. In this paper, we propose a Laplacian pyramid pansharpening network architecture for accurately fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image, aiming at getting a higher spatial resolution multispectral image. The proposed architecture considers three aspects. First, we use the Laplacian pyramid method whose blur kernels are designed according to the sensors’ modulation transfer functions to separate the images into multiple scales for fully exploiting the crucial spatial information at different spatial scales. Second, we develop a fusion convolutional neural network (FCNN) for each scale, combining them to form the final multi-scale network architecture. Specifically, we use recursive layers for the FCNN to share parameters across and within pyramid levels, thus significantly reducing the network parameters. Third, a total loss consisting of multiple across-scale loss functions is employed for training, yielding higher accuracy. Extensive experimental results based on quantitative and qualitative assessments exploiting benchmarking datasets demonstrate that the proposed architecture outperforms state-of-the-art pansharpening methods. Code is available at https://github.com/ChengJin-git/LPPN .

This article proposes a new denoising method for hyperspectral images (HSIs) corrupted by mixtures (in a statistical sense) of stripe noise, Gaussian noise, and impulsive noise. The proposed method has three distinctive features: 1) it exploits the intrinsic characteristics of HSIs, namely, low-rank and self-similarity; 2) the observation noise is assumed to be additive and modeled by a mixture of Gaussian (MoG) densities; 3) the inference is performed with an expectation maximization (EM) algorithm, which, in addition to the clean HSI, also estimates the mixture parameters (posterior probability of each mode and variances). Comparisons of the proposed method with state-of-the-art algorithms provide experimental evidence of the effectiveness of the proposed denoising algorithm. A MATLAB demo of this work will be available at https://github.com/TaiXiangJiang for the sake of reproducibility.

Recently, the transform-based tensor nuclear norm (TNN) methods have shown promising performance and drawn increasing attention in tensor completion (TC) problems. The main idea of these methods is to exploit the low-rank structure of frontal slices of the tensor under the transform. However, the transforms in TNN methods usually treat all modes equally and do not consider the different traits of different modes (i.e., spatial and spectral/temporal modes). To address this problem, we suggest a new low-rank tensor representation based on the coupled nonlinear transform (called CoNoT) for a better low-rank approximation. Concretely, spatial and spectral/temporal transforms in the CoNoT, respectively, exploit the different traits of different modes and are coupled together to boost the implicit low-rank structure. Here, we use the convolutional neural network (CNN) as the CoNoT, which can be learned solely from an observed multidimensional image in an unsupervised manner. Based on this low-rank tensor representation, we build a new multidimensional image completion model. Moreover, we also propose an enhanced version (called Ms-CoNoT) to further exploit the spatial multiscale nature of real-world data. Extensive experiments on real-world data substantiate the superiority of the proposed models against many state-of-the-art methods both qualitatively and quantitatively.

Hyperspectral image (HSI) mixed noise removal is a fundamental problem and an important preprocessing step in remote sensing fields. The low-rank approximation-based methods have been verified effective to encode the global spectral correlation for HSI denoising. However, due to the large scale and complexity of real HSI, previous low-rank HSI denoising techniques encounter several problems, including coarse rank approximation (such as nuclear norm), the high computational cost of singular value decomposition (SVD) (such as Schatten p-norm), and adaptive rank selection (such as low-rank factorization). In this article, two novel factor group sparsity-regularized nonconvex low-rank approximation (FGSLR) methods are introduced for HSI denoising, which can simultaneously overcome the mentioned issues of previous works. The FGSLR methods capture the spectral correlation via low-rank factorization, meanwhile utilizing factor group sparsity regularization to further enhance the low-rank property. It is SVD-free and robust to rank selection. Moreover, FGSLR is equivalent to Schatten p-norm approximation (Theorem 1), and thus FGSLR is tighter than the nuclear norm in terms of rank approximation. To preserve the spatial information of HSI in the denoising process, the total variation regularization is also incorporated into the proposed FGSLR models. Specifically, the proximal alternating minimization is designed to solve the proposed FGSLR models. Experimental results have demonstrated that the proposed FGSLR methods significantly outperform existing low-rank approximation-based HSI denoising methods.

Pansharpening refers to the fusion of a panchromatic (PAN) image with a high spatial resolution and a multispectral (MS) image with a low spatial resolution, aiming to obtain a high spatial resolution MS (HRMS) image. In this article, we propose a novel deep neural network architecture with level-domain-based loss function for pansharpening by taking into account the following double-type structures, i.e., double-level, double-branch, and double-direction, called as triple-double network (TDNet). By using the structure of TDNet, the spatial details of the PAN image can be fully exploited and utilized to progressively inject into the low spatial resolution MS (LRMS) image, thus yielding the high spatial resolution output. The specific network design is motivated by the physical formula of the traditional multi-resolution analysis (MRA) methods. Hence, an effective MRA fusion module is also integrated into the TDNet. Besides, we adopt a few ResNet blocks and some multi-scale convolution kernels to deepen and widen the network to effectively enhance the feature extraction and the robustness of the proposed TDNet. Extensive experiments on reduced- and full-resolution datasets acquired by WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate the superiority of the proposed TDNet compared with some recent state-of-the-art pansharpening approaches. An ablation study has also corroborated the effectiveness of the proposed approach. The code is available at https://github.com/liangjiandeng/TDNet.

Fusing a pair of low-spatial-resolution hyperspectral image (LR-HSI) and high-spatial-resolution multispectral image (HR-MSI) has been regarded as an effective and economical strategy to achieve HR-HSI, which is essential to many applications. Among existing fusion models, the tensor ring (TR) decomposition-based model has attracted rising attention due to its superiority in approximating high-dimensional data compared to other traditional matrix/tensor decomposition models. Unlike directly estimating HR-HSI in traditional models, the TR fusion model translates the fusion procedure into an estimate of the TR factor of HR-HSI, which can efficiently capture the spatial-spectral correlation of HR-HSI. Although the spatial-spectral correlation has been preserved well by TR decomposition, the spatial-spectral continuity of HR-HSI is ignored in existing TR decomposition models, sometimes resulting in poor quality of reconstructed images. In this article, we introduce a factor smoothed regularization for TR decomposition to capture the spatial-spectral continuity of HR-HSI. As a result, our proposed model is called factor smoothed TR decomposition model, dubbed FSTRD. In order to solve the suggested model, we develop an efficient proximal alternating minimization algorithm. A series of experiments on four synthetic datasets and one real-world dataset show that the quality of reconstructed images can be significantly improved by the introduced factor smoothed regularization, and thus, the suggested method yields the best performance by comparing it to state-of-the-art methods.

Pansharpening refers to a spatio-spectral fusion of a lower spatial resolution multispectral (MS) image with a high spatial resolution panchromatic image, aiming at obtaining an image with a corresponding high resolution both in the domains. In this article, we propose a generic fusion framework that is able to weightedly combine variational optimization (VO) with deep learning (DL) for the task of pansharpening, where these crucial weights directly determining the relative contribution of DL to each pixel are estimated adaptively. This framework can benefit from both VO and DL approaches, e.g., the good modeling explanation and data generalization of a VO approach with the high accuracy of a DL technique thanks to massive data training. The proposed method can be divided into three parts: 1) for the VO modeling, a general details injection term inspired by the classical multiresolution analysis is proposed as a spatial fidelity term and a spectral fidelity employing the MS sensor's modulation transfer functions is also incorporated; 2) for the DL injection, a weighted regularization term is designed to introduce deep learning into the variational model; and 3) the final convex optimization problem is efficiently solved by the designed alternating direction method of multipliers. Extensive experiments both at reduced and full-resolution demonstrate that the proposed method outperforms recent state-of-the-art pansharpening methods, especially showing a higher accuracy and a significant generalization ability.

The cytoprotective function of p53 recently has been exploited as a therapeutic advantage for cancer prevention; agents activating the prosurvival activity of p53 are shown to prevent UV-induced damages. To explore the mechanisms of p53-mediated protection from UV-induced apoptosis, we have established stable clones of H1299 lung carcinoma cells expressing a temperature-sensitive p53 mutant, tsp53V143A. At the permissive temperature of 32°C, the tsp53V143A-expressing cells were arrested in G1 phase without the occurrence of apoptosis; consistent with this is the preferential induction of genes related to growth arrest and DNA damage repair. Previous expression of functional tsp53V143A for ≥18 hours inhibited the release of proapoptotic molecules from mitochondria and protected the cells from UV-induced apoptosis; moreover, it suppressed the activation of c-Jun NH2-terminal kinase (JNK) signaling and relieved the effect of UV on p53 target gene activation. p53 associated with JNK and inhibited its kinase activity. Using the p53-null H1299 cells, we showed that inhibition of JNK blocked the UV-elicited mitochondrial death signaling and caspase activation. Our results suggest that the ability of p53 to bind and inactivate JNK, together with the activation of the p53 target genes related to cell cycle arrest and DNA damage repair, is responsible for its protection of cells against UV-induced apoptosis.

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Aim To screen specific biomarkers which early diagnose the risk of threatened preterm birth (TPB) and preterm birth (PB) to improve the pregnancy outcome and analyze its underlying mechanisms. Methods Three groups of vaginal secretions samples, including full-term birth (FB), TPB, and PB were collected to clarify the relationship between metabolites and health status of pregnant women. Results Here, screened out two species (Prevotella melaninogenica and Serratia marcescens) and five genera (Stenotrophomonas, Caulobacter, Sphingomonas, Asticcacaulis, and Sneathia) to be differentially distributed among three groups. There were 30 significantly different metabolites in three groups, and nine metabolites (pos_3691, pos_2441, pos_2629, pos_1152, pos_615, pos_1229, pos_2110, pos_1357, and pos_1737) to be screened as potential metabolic markers in three groups. Five metabolites, including pos_3691, pos_2441, pos_1343, pos_1647, and pos_3239 could be used as a metabolic marker for early diagnosis of TPB. And the significantly enriched pathway was phenylalanine, tyrosine, and tryptophan biosynthesis. Conclusion This study clarifies vaginal flora and metabolites to be associated with TPB and PB, two species and five genera of vaginal bacteria, nine metabolites may be the potential biomarker in distinguishing different health states of pregnant women, to early diagnose the risk of TPB and PB and improve the pregnancy outcome.

An efficient, tandem one-pot approach to synthesize multisubstituted 2-acylpyrroles from readily prepared

This research found that the clinical outcomes (PFS, ORR, OS) of the non-platinum-based doublet regimen (docetaxel capecitabine combination) were similar to those of the platinum-based (oxaliplatin capecitabine combination) when used as first line therapy for MGC patients.BackgroundDocetaxel, platinum and fluorouracil are the three most important drugs in the treatment of MGC. This study was to compare clinical outcomes of the docetaxel capecitabine combination and the oxaliplatin capecitabine combination as first-line therapy in MGC patients.MethodsIn this phase II trial, MGC patients were randomly assigned and treated with either TX (capecitabine 1000 mg/m2/twice daily/1-14 days and docetaxel 60/75 mg/m2 on the 1st day) (because of toxicity, the dose of docetaxel was reduced to 60 mg/m2) or XELOX (capecitabine the same dose with TX and oxaliplatin 130 mg/m2 on the 1st day) as first-line therapy. After progression, patients were crossover to the other group as second-line treatment.ResultsTotal 134 MGC patients were randomized (69 in TX, 65 in XELOX). There was no significant difference between the PFS of the two groups (TX vs XELOX, 4.6 months vs 5.1 months, p=0.359), and the SFS (9.3 months vs 7.5 months, p=0.705), OS (13.1 months vs 9.6 months, p=0.261), and ORR (46.4% vs 46.2%) were also similar. Among patients with ascites, the TX group had significantly longer PFS and OS than the XELOX group. A total of 85 patients (48 in TX, 37 in XELOX) received second-line treatment, with overall survival of second-line chemotherapy (OS2) of 8.0 m and 5.3 m (p=0.046), respectively. Grade 3 to 4 treatment-related adverse events of first line treatment occurred more in TX group than that in XELOX group(60.6% vs 55.4%).ConclusionTX regimen is an alternative choice of first-line treatment for MGC patients. We still need to explore the large number of cohort to confirm this results.

PurposeThe construction industry is the major sector in China but it has been exposed to a series of problems including low productivity and workforce shortage. However, construction robots as an effective and sustainable approach to overcome the difficulties in construction industry have not been widely adopted. Few studies attempted to investigate on the adoption of construction robots in China. In order to fill this gap, this study aim to address the barriers to the adoption of construction robots in China.Design/methodology/approachThrough literature review, semi-structured interview 24 factors hindering the adoption of construction robots are summarized. Next, a total of valid 150 questionnaires delivered to the 7 stakeholders were collected. Ranking analysis was used to identify 21 critical factors was determined by the mean score analysis and factor analysis extracted 21 critical factors into 5 clusters.FindingsResults indicate that the “technological performance and management” cluster is the most dominant of the 5 clusters. The most important barrier is “Limited research and design input”, followed by “High purchase cost” and “Unstructured construction environment”. Construction robots are still under R&D have had limited field applications in the production and construction process.Originality/valueThe research findings provide a useful reference for different stakeholders to identify the critical factors appropriate strategies to promote the adoption of construction robots. Furthermore, this study provides recommendations to promote adoption of construction robots.

In hyperspectral image (HSI) denoising, subspace-based denoising methods can reduce the computational complexity of the denoising algorithm. However, the existing matrix subspaces, which are generated by the unfolding matrix of the HSI tensor, cannot completely represent a tensor since the unfolding operation will destroy the tensor structure. To overcome this, we design a novel basis tensor that is directly learned from the original tensor and present a tensor subspace representation (TenSR), which is a more authentic representation for delivering the intrinsic structure of the tensor than a matrix subspace representation. Equipped with the TenSR, we then propose a TenSR-based HSI denoising (TenSRDe) model, which simultaneously considers the low-tubal rankness of the HSI tensor and the nonlocal self-similarity of the coefficient tensor. Moreover, we develop an efficient proximal alternating minimization (PAM) algorithm to solve the proposed nonconvex model and theoretically prove that the algorithm globally converges to a critical point. Experiments implemented on simulated and real data sets substantiate the denoising effect and efficiency of the proposed method.