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Heat stress (HS) has serious negative effects on plant development and has become a major threat to agriculture. A rapid transcriptional regulatory cascade has evolved in plants in response to HS. Nuclear Factor-Y (NF-Y) complexes are critical for this mechanism, but how NF-Y complexes are regulated remains unclear. In this study, we identified NF-YC10 (NF-Y subunit C10), a central regulator of the HS response in Arabidopsis thaliana, as a substrate of SUMOylation, an important post-translational modification. Biochemical analysis showed that the SUMO ligase SIZ1 (SAP AND MIZ1 DOMAIN-CONTAINING LIGASE1) interacts with NF-YC10 and enhances its SUMOylation during HS. The SUMOylation of NF-YC10 facilitates its interaction with and the nuclear translocation of NF-YB3, in which the SUMO interaction motif (SIM) is essential for its efficient association with NF-YC10. Further functional analysis indicated that the SUMOylation of NF-YC10 and the SIM of NF-YB3 are critical for HS-responsive gene expression and plant thermotolerance. These findings uncover a role for the SIZ1-mediated SUMOylation of NF-YC10 in NF-Y complex assembly under HS, providing new insights into the role of a post-translational modification in regulating transcription during abiotic stress responses in plants.

Soil temperature of vegetated landfill earthen covers varies with time during their service, due to heat released by the degradation of municipal solid waste. In this study, an experimental campaign to capture soil water-retention curve (SWRC) and hydraulic conductivity function (HCF) of unsaturated rooted soils was conducted at different temperatures (25, 45, and 60 °C). Simplified evaporation tests were conducted by subjecting soil specimens to a prolonged drying period at maintained temperature. Based on the responses of matric suction and water content, SWRC and HCF were determined by the instantaneous profile method. It was found that increasing temperature reduced water-retention capacity, due to root shrinkage, thermal expansion of liquid phase, and reduction in forces of capillarity and adsorption. Soil hydraulic conductivity was enhanced by the increase in soil temperature due to reduction of water viscosity and potential preferential flow at root channels induced by root shrinkage. Temperature effects on SWRC and HCF of rooted soils are suction-dependent. A new nonisothermal SWRC model was developed to capture the dual effects of roots and temperature. By using the same set of calibrated parameters, the model demonstrates its ability to predict SWRCs reasonably well within the range of temperature investigated.

Salvia rosmarinus is considered a sacred plant because of its special fragrance and is commonly used in cooking and traditional medicine. Here, we report a high-quality chromosome-level assembly of the S. rosmarinus genome of 1.11 G in size; the genome has a scaffold N50 value of 95.5 Mb and contains 40,701 protein-coding genes. In contrast to other diploid Labiatae, an independent whole genome duplication (WGD) event was shown by S. rosmarinus at approximately 15 MYA. Transcriptomic comparison of two S. rosmarinus cultivars with contrasting carnosic acid (CA) content identified 842 genes significantly positively associated with carnosic acid biosynthesis in S. rosmarinus. Many of these genes have been reported to be involved in CA-biosynthesis previously, such as several MVA/MEP pathways and CYP71-coding genes. Based on the genomes and these genes, we propose a model of CA biosynthesis in S. rosmarinus. Further, comparative genome analysis of the congeneric species revealed the species-specific evolution of CA biosynthetic genes. The genes encoding diterpene synthase and the cytochromes P450 (CYPs) family of CA synthesis-associated genes form a biosynthetic gene cluster (CPSs-KSLs-CYP76AHs) responsible for the synthesis of leaf and root diterpenoids, which are located on S. rosmarinus chromosome 1 and 2, respectively. Such clustering is also observed in other sage plants, thus suggesting that genes involved in diterpenoid synthesis are conserved in the Labiataceae family. These findings provide new insights into the synthesis of aromatic terpenoids and their regulation.

With the understanding of the complex interaction between the tumour microenvironment and immunotherapy, there is increasing interest in the role of immune regulators in the treatment of head and neck squamous cell carcinoma (HNSCC). Activation of T cells and immune checkpoint molecules is important for the immune response to cancers. Immune checkpoint molecules include cytotoxic T lymphocyte antigen 4 (CTLA-4), programmed death 1 (PD-1), T-cell immunoglobulin mucin protein 3 (TIM-3), lymphocyte activation gene 3 (LAG-3), T cell immunoglobin and immunoreceptor tyrosine-based inhibitory motif (TIGIT), glucocorticoid-induced tumour necrosis factor receptor (GITR) and V-domain Ig suppressor of T cell activation (VISTA). Many clinical trials using checkpoint inhibitors, as both monotherapies and combination therapies, have been initiated targeting these immune checkpoint molecules. This review summarizes the functional mechanism and use of various immune checkpoint molecules in HNSCC, including monotherapies and combination therapies, and provides better treatment options for patients with HNSCC.

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

Oral and maxillofacial tumors (OMTs), such as oral squamous cell carcinoma (SCC), pleomorphic adenoma, and ameloblastoma, are common head and neck tumors. Lipopolysaccharide-binding protein (LBP) is a type I acute reactive protein, which participates in body inflammatory response modulation through lipopolysaccharide (LPS)-induced signaling pathway by targeting macrophages (expressing cluster of differentiation 204 [CD204]). Although it is well established that LBP is associated with the development of multiple types of cancer, little is known about the role of LBP in OMTs. This study aims to explore the expression of LBP in OMTs. Here, immunohistochemical (IHC) double staining of LBP and CD204 and enzyme-linked immunosorbent assay (ELISA) were conducted to explore the LBP expression in OMTs. The findings demonstrated that the LBP expression in OMTs was significantly elevated (p 0.001). In addition, the LBP expression was associated with the clinical stage (p 0.001), T classification (p 0.001), and lymph node metastasis (p 0.001, except ELISA) but independent of histological grade of SCC, gender, and age in patients with SCC. The optional cutoff of the LBP serum level is 0.721 μg/ml. To conclude, LBP contributes to the development of OMTs and could be a biomarker in the screening and predicting metastasis in patients with OMTs.

The modification of chimeric antigen receptor (CAR) endowing T cells with tumor‐specific cytotoxicity induces antitumor immunity. However, the structural characteristics of solid tumors, the loss of specific antigens, and the strong immunosuppressive environment are challenges to treat solid tumors with CAR‐T therapy. The purpose of our study was to find and verify the potentials of CAR‐T therapies for patients with head and neck squamous cell carcinoma (HNSCC). First, we selected MUC1 as our research target and verified its differential expression in cancer tissues and adjacent non‐neoplastic tissues (ANNT). Next, we constructed a second‐generation CAR and validated the cytotoxic function in vitro. In our study, we found that exogenous addition human IL22 recombinant protein could increase the MUC1 expression and enhance the function of T cells. In addition, we constructed a fourth‐generation CAR that secretes IL22. Finally, we verified the antitumor function of two different CAR‐T cells in vitro and in vivo, respectively. CAR‐MUC1‐IL22 T cells were found to have a stronger and more effective cytotoxic function against MUC1 + HNSCC cells. Taken together, these results demonstrate the potential effectiveness of CAR‐T in the treatment of patients with HNSCC and provide evidence‐based of MUC1 + CAR‐T therapy., Identified the expression of MUC1 is higher in head and neck squamous cell carcinomas than non‐neoplastic tissues. Construct the second‐generation CAR‐T cell (CAR‐MUC1 T cell) and the fourth‐generation CAR‐T cell (CAR‐MUC1‐IL22 T cell) structure, respectively, and verify the function of different T cells to target tumors in vitro and in vivo. Since IL22 can upregulate the expression of MUC1, the fourth‐generation CAR‐T can kill HNSCC cells more effectively.

N 2 /H 2 plasmas are often adopted for controllable etching of C-based materials, such as for etching back end of line (BEOL) tri-layer resist. In this paper, N 2 /H 2 chemistry has been used for a spin-on carbon (SOC) recess application in a Lam capacitively coupled plasma (CCP) etch system. Peeling defects are more problematic for this recess etch than the traditional tri-layer carbon etch due to the Si-based byproducts that form readily on SiO 2 in N 2 /H 2 plasmas. Here, we report on the important role N 2 plays in this process space and describe two methods to mitigate the peeling defect mode for this application.

We introduce the concept of geometric stability to the problem of 6D object pose estimation and propose to learn pose inference based on geometrically stable patches extracted from observed 3D point clouds. According to the theory of geometric stability analysis, a minimal set of three planar/cylindrical patches are geometrically stable and determine the full 6DoFs of the object pose. We train a deep neural network to regress 6D object pose based on geometrically stable patch groups via learning both intra-patch geometric features and inter-patch contextual features. A subnetwork is jointly trained to predict per-patch poses. This auxiliary task is a relaxation of the group pose prediction: A single patch cannot determine the full 6DoFs but is able to improve pose accuracy in its corresponding DoFs. Working with patch groups makes our method generalize well for random occlusion and unseen instances. The method is easily amenable to resolve symmetry ambiguities. Our method achieves the state-of-the-art results on public benchmarks compared not only to depth-only but also to RGBD methods. It also performs well in category-level pose estimation., Comment: CVPR 2021, 10 pages

BackgroundIn recent decades, the serum lipid profile of apolipoprotein(a) (Lp(a)) level and apolipoprotein B/apolipoprotein A1 ratio (Apo B/ApoA1) ratio were found more representative for serum lipid level and were recognized as the independent risk factors for various diseases. Although the serum lipid levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) were found associated with symptomatic IDH, no studies have been conducted to date for the evaluation of the association of Apo AI, Apo B, Lp(a) and Apo B/Apo AI levels with symptomatic IDH.MaterialsA total of 1,839 Chinese patients were recruited in the present study. 918 patients were diagnosed as IDH cases and were enrolled in the experimental group. A control group of 921 patients underwent a physical examination during the same period. The serum lipid levels of TC, TG, LDL-C, HDL-C, Lp(a), Apo B and Apo B/Apo AI were examined and analyzed.ResultsThe patients in the control group were collected randomly from patients who were matched with the baseline levels of the aforementioned lipid molecular. The patients with IDH exhibited significantly higher TC, TG, LDL, Apo B and Lp(a) levels compared with the control subjects. The percentage of high-TC, high-TG, high-LDL, high-Apo B and high-Lp(a) were significantly higher in the IDH group. However, hyperlipidaemia was not associated with the degenerated segment of the IDH (P=0.201). The odds ratios (OR) for the incidence of IDH with an elevated LDL-C, TC, TG, Lp(a), Apo B and Apo B/Apo AI were 1.583, 1.74, 1.62, 1.58, 1.49 and 1.39, respectively. The correlation analysis revealed the correlation between elevated LDL-C, TC, TG, Apo B, Lp(a) and incidence of IDH was significant (R2LDL=0.017; R2TC=0.004; R2TG=0.015; R2Apo B=0.004; R2LP(a)=0.021) (P

6D object pose estimation is a fundamental problem for many computer vision and robotics applications. Recent work has shown that data-driven approaches could enable accurate 6D pose estimation for objects with sufficient texture on the surface. However, few works have focused on estimation 6D pose for texture-less objects. In this paper, we present a network that estimating 6D pose for texture-less objects by using the multi-scale relational features. The proposed network, which leverages both the appearance and geometry features from multi-scale point groups, is able to extract distinctive features for texture-less region. In particular, the multi-scale features encode relational information of the point groups, are more informative compared to the feature comes from vanilla convolutional neural networks and PointNet. The proposed network is end-to-end trainable. Experiments on T-LESS dataset demonstrate our method achieves competitive results on 6D pose estimation task of texture-less objects.

For dynamic complex driving tasks of autonomous vehicle, traditional driving behavior learning methods still need to be improved in terms of learning accuracy and generalization performance. A convolutional neural network gaussian process regression (CNN-GPR) method for driving behavior learning is proposed to tackle these problems that full connection layers of end-to-end convolutional neural networks (CNN) have limited generalization ability and easily converge to local optimization. At the same time, the PilotNet-GPR algorithm based on the CNN-GPR method is designed. The gaussian process regression (GPR) method with global mapping capability and better generalization performance is used to improve fully connected layers of the end-to-end CNN in order to complete the mapping from features extracted to driving actions more efficiently for the proposed CNN-GPR method. In addition, the long short-term memory network (LSTM) is added into CNN-GPR method, and a convolutional long short-term memory network gaussian process regression (CNN-LSTM-GPR) method of driving behavior learning with time-sequential images is proposed in order to further improve the accuracy of driving behavior learning through time-sequential information. This method utilises the gaussian process regression method to improve structures of fully connected layers in the cascaded deep neural network (CNN-LSTM) in order to make a more efficient learning approximation between features extracted by the CNN-LSTM and driving actions. Verification experiments on the Apollo end-to-end dataset for autonomous driving show that the proposed CNN-GPR method can further improve the imitation accuracy for end-to-end driving behavior learning and promote generalization performance of learned models compared with the PilotNet method. compared with related end-to-end driving behavior learning methods based on single image under the same condition, the proposed CNN-LSTM-GPR method can make full use of time-sequential information of images, which leads to smaller imitation errors. Additionally, it can further enhance the learning accuracy and demonstrates a more satisfying generalization performance compared with the CNN-LSTM method.

The omnidirectional ring structured light depth perception system can project a 360-degree structured light.However, due to the influence of ambient light, refraction and other noise factors, it is easy to cause false detection of structured light. Therefore, denoising plays an important role in the omnidirectional ring structured light depth perception system.Firstly, this paper uses the DCGAN network to generate more data sets due to the lack of data sets. Secondly, based on the above data sets, this paper uses the autoencoder network to denoise the ring structured light. Experiments show that by using a combination of DCGAN and autoencoder network for denoising, the structured light’s noise is lesstherefore more robust to the surrounding environment compared to other methods.

First, this article presents a simulation experiment of hydraulic reclamation, and then a vacuum preloading (VP) test using the sedimentary soil obtained by the first experiment. In the VP ...

BACKGROUND Multicystic mucinous adenocarcinoma is rarely recognized and has a high misdiagnosis rate. PURPOSE To distinguish malignant multicystic mucinous adenocarcinomas from benign multicystic lesions in the uterine cervix using multiparametric MR. STUDY TYPE Retrospective. POPULATION Forty patients with seven cystic mucinous adenocarcinomas and 33 benign multicystic lesions. FIELD STRENGTH/SEQUENCE 3.0T. Diffusion-weighted images (DWI) and dynamic contrast-enhanced (DCE) images. ASSESSMENT Lesion size, intracystic hemorrhage, solid component, and heterogeneous enhancement were subjectively assessed, and apparent diffusion coefficient (ADC) values, Ktrans , Kep , and Ve parameters were compared. STATISTICAL ANALYSIS Student's t-test was used to compare age, tumor size, ADC values, and DCE parameters. Pearson's chi-square test was used to compare intracystic hemorrhage, solid component, and heterogeneous enhancement. Receiver-operating-characteristic (ROC) analysis of ADC values, tumor size, and Ktrans were performed. RESULTS The size of mucinous adenocarcinomas was larger than benign multicystic lesions (4.09 ± 2.09 vs. 2.23 ± 0.58 cm, P

Renal hyperparathyroidism is a common complication of chronic kidney disease (CKD). Parathyroidectomy (PTX) for these patients continues to be a valuable option in the era of calcimimetics. Postoperative hypocalcemia is common after surgery. The aim of this study was to identify clinical factors to define postoperative calcium requirements. From February 2013 to May 2017, 68 patients with chronic kidney disease 5 (CKD5) who underwent PTX were reviewed. We collected clinical and laboratory data preoperatively and calculated the total calcium requirement in a week after surgery. Univariate and multiple analyses were performed to study whether these clinical and laboratory factors were associated with calcium requirement. Univariate analysis showed that preoperative alkaline phosphatase (ALP), calcium (Ca), parathyroid hormone and hemoglobin were independently associated with calcium requirement. Multivariate model showed that the preoperative ALP was the only independent factor that could predict the requirement of calcium. In the context of a high dCa (1.75 mmol/l) and a stable dose of calcitriol, preoperative ALP levels were significantly associated with calcium requirement in patients with CKD5 undergoing PTX.