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Background The cell wall plays crucial roles in establishing the morphology of plant cells and defense response to biotic and abiotic stresses. The plant COBRA protein family plays an important role in secondary cell wall biosynthesis and the orientation of cell expansion, which have been extensively studied in Arabidopsis thaliana, maize, rice, etc., but no systematic studies were conducted in wheat. Methods and Results In this study, the full-length sequence of TaCOBLs was obtained by homology cloning from wheat, and a conserved motif analysis confirmed that TaCOBLs belonged to the COBRA protein family. qRT-PCR results showed that the TaCOBL transcripts were induced by abiotic stresses, including cold, drought, salinity, and abscisic acid (ABA). Two haplotypes of TaCOBL-5B (Hap5B-a and Hap5B-b), harboring one indel (----/TATA) in the 5’ flanking region (-550 bp), were found on chromosome 5BS. A co-dominant marker, Ta5BF/Ta5BR, was developed based on the polymorphism of the two TaCOBL-5B haplotypes. Significant correlations between the two TaCOBL-5B haplotypes and cold resistance were observed under four environmental conditions. Conclusion Hap5B-a, a favored haplotype acquired during wheat polyploidization, may positively contribute to enhanced cold resistance in wheat. Based on the promoter activity analysis, the Hap5B-a promoter containing a TATA-box was more active than that of Hap5B-b without the TATA-box under low temperature. Our study provides valuable information indicating that the TaCOBL genes are associated with cold response in wheat.

Introduction. Along with blood glucose levels, diabetic retinopathy (DR) development also involves endogenous risk factors, such as trimethylamine-N-oxide (TMAO), a product of intestinal flora metabolic disorder, which exacerbates diabetic microvascular complications. However, the effect of TMAO on retinal cells under high-glucose conditions remains unclear. Therefore, this study examined the effects of TMAO on high-glucose-induced retinal dysfunction in the context of NLRP3 inflammasome activation, which is involved in DR. Materials and Methods. TMAO was assessed in the serum and aqueous humor of patients using ELISA. Human retinal microvascular endothelial cells (HRMECs) were treated for 72 h as follows: NG (normal glucose, D-glucose 5.5 mM), NG + TMAO (5 μM), HG (high glucose, D-glucose 30 mM), and HG + TMAO (5 μM). The CCK8 assay was then used to assess cell proliferation; wound healing, cell migration, and tube formation assays were used to verify changes in cell phenotype. ZO-1 expression was determined using immunofluorescence and western blotting. Reactive oxygen species (ROS) formation was assessed using DCFH-DA. NLRP3 inflammasome complex activation was determined using a western blot. Results. The serum and aqueous humor from patients with PDR contained higher levels of TMAO compared to patients with nontype 2 diabetes (Control), non-DR (NDR), and non-PDR (NPDR). TMAO showed significant acceleration of high-glucose-induced cell proliferation, wound healing, cell migration, and tube formation. ZO-1 expression decreased remarkably with the combined action of TMAO and a high glucose compared to either treatment alone. TMAO also promoted high-glucose-activated NLRP3 inflammasome complex. Conclusion. The combination of TMAO and high-glucose results in increased levels of ROS and NLRP3 inflammasome complex activation in HRMECs, leading to exacerbated retinal dysfunction and barrier failure. Thus, TMAO can accelerate PDR occurrence and development, thus indicating the need for early fundus monitoring in diabetic patients with intestinal flora disorders.

The development of dual chemodynamic therapy and NO therapy can significantly improve the efficiency of cancer treatment.

The capability to monitor doping in a rapid and sensitive manner is highly promising in the field of doping control.

Signal transducer and activator of transcription (STAT) proteins compose a family of transcription factors critical for cancer stem cells (CSCs), and they are involved in maintaining stemness properties, enhancing cell proliferation, and promoting metastasis. Recent studies suggest that STAT proteins engage in reciprocal communication between CSCs and infiltrate immune cell populations in the tumor microenvironment (TME). Emerging evidence has substantiated the influence of immune cells, including macrophages, myeloid-derived suppressor cells, and T cells, on CSC survival through the regulation of STAT signaling. Conversely, dysregulation of STATs in CSCs or immune cells contributes to the establishment of an immunosuppressive TME. Thus, STAT proteins are promising therapeutic targets for cancer treatment, especially when used in combination with immunotherapy. From this perspective, we discuss the complex roles of STATs in CSCs and highlight their functions in the crosstalk between CSCs and the immune microenvironment. Finally, cutting-edge clinical trial progress with STAT signaling inhibitors is summarized.

Avian influenza virus (AIV)is easy to cause diseases in birds and humans.It causes great economic losses to the poultry farms and leads to public health problems. Using vaccines is the main approach to control the prevalence of AIV. In our previously published article, a recombinant Lactobacillus plantarum (L. plantarum) expressing the NP-M2 peptide ofH9N2 AIV was generated, and its protective effect was evaluated in a chicken model. In this study, the protective effect was estimated in mice model. Humoral and cellular immune response parameters were measured using flow cytometry adding to body weight loss, survival rate, virus load, and histopathological changes in the lung. The obtained results elucidated that, the recombinant L. plantarum can promote the activation of dendritic cells (DC), proliferation of T and B cells adding to eliciting protective secretory IgA (sIgA) and humeral IgG level in mice model. Accordingly, it could be used as a patent vaccine to control the AIV infection.

OBJECTIVE Preoperative diagnosis of diffuse midline glioma, H3 K27–altered (DMG-A) and midline high-grade glioma without H3 K27 alteration (DMG-W), as well as midline primary CNS lymphoma (PCNSL) in adults, is challenging but crucial. The aim of this study was to develop a model for predicting these three entities using machine learning (ML) algorithms. METHODS Thirty-three patients with DMG-A, 35 with DMG-W, and 35 with midline PCNSL were retrospectively enrolled in the study. Radiomics features were extracted from contrast-enhanced T1-weighted MR images. Two radiologists evaluated the conventional MRI features of the tumors, such as shape. Patient age, tumor volume, and conventional MRI features were considered clinical features. The data set was randomly stratified into 70% training and 30% testing cohorts. Predictive models based on the clinical features, radiomics features, and integration of clinical and radiomics features were established through ML. The performances of the models were evaluated by calculating the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Subsequently, 10 patients with DMG-A, 10 with DMG-W, and 12 with PCNSL were enrolled from another institution to validate the established models. RESULTS The predictive models based on clinical features, radiomics features, and the integration of clinical and radiomics features through the support vector machine algorithm had the optimal accuracies in the training, testing, and validation cohorts, and the accuracies in the testing cohort were 0.871, 0.892, and 0.903, respectively. Age, 2 radiomics features, and 3 conventional MRI features were the 6 most significant features in the established integrated model. CONCLUSIONS The integrated prediction model established by ML provides high discriminatory accuracy for predicting DMG-A, DMG-W, and midline PCNSL in adults.

Waldenström Macroglobulinemia (WM) is a rare type of non-Hodgkin lymphoma with no standard first-line treatment, and the disease is still incurable. This study evaluated the clinical efficacy, safety, and prognostic factors of bortezomib-based chemotherapy as initial treatment in WM patients. We retrospectively analyzed the clinical data collected from 44 newly diagnosed WM patients treated with bortezomib-based regimens at the Affiliated Hospital of Nantong University from December 2011 to June 2021. Univariate and multivariate analyses were used to assess prognostic factors for overall survival (OS) and progression-free survival (PFS). The median age was 67 years old, with an overall response rate (ORR) of 93.2%, complete response (CR) rate of 6.8%, and very good partial response (VGPR) rate of 29.5%. With a median follow-up of 39 months, the 2-year overall survival (OS) and 2-year PFS rates were 88.0% and 59.0%, respectively. By the last follow-up, eight patients (18.2%) had died. Univariate analysis showed patients with B symptoms, elevated LDH, international prognostic stage system of WM (IPSSWM) stage III, high Revised IPSSWM (R-IPSSWM) score, and those who did not achieve VGPR were associated with shorter PFS. And patients with B symptoms, with high R-IPSSWM score, and who do not achieve VGPR also had shorter OS than their counterparts. Multivariate analysis confirmed that failure to achieve VGPR was an independent adverse prognostic factor for OS and PFS. In conclusion, we showed that bortezomib-based chemotherapy effectively treated newly diagnosed patients with WM. However, combinations of drugs with different mechanisms are recommended for patients with a high tumor burden. In addition, deep remission can improve patients' survival.

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide. Transcatheter arterial chemoembolization (TACE) has been performed as a palliative treatment for patients with HCC. However, HCC is easy to recur after TACE. Magnetic resonance imaging (MRI) has clinical potential in evaluating the TACE treatment effect for patients with liver cancer. However, traditional MRI has some limitations.To explore the clinical potential of diffusion kurtosis imaging (DKI) in predicting recurrence and cellular invasion of the peritumoral liver zone of HCC after TACE.Seventy-six patients with 82 HCC nodules were recruited in this study and underwent DKI after TACE. According to pathological examinations or the overall modified response evaluation criteria in solid tumors (mRECIST) criterion, 48 and 34 nodules were divided into true progression and pseudo-progression groups, respectively. The TACE-treated area, peritumoral liver zone, and far-tumoral zone were evaluated on DKI-derived metric maps. Non-parametricDKI metrics, including mean diffusivity (MD), axial diffusivity (DA), radial diffusivity (DR), axial kurtosis (KA), and anisotropy fraction of kurtosis (Fak), showed statistically different values between the true progression and pseudo-progression groups (DKI has been demonstrated with robust performance in predicting the therapeutic response of HCC to TACE. Moreover, DKI might reveal cellular invasion of the peritumoral zone by molecular diffusion-restricted change.

Cancer stem-like cells (CSLC) are considered a major contributor to the development and progression of hepatocellular carcinoma (HCC). Previous studies indicated that CSLC are characterized by resistance to ferroptosis, a type of lipid peroxidation-dependent cell death. Here, we identified a set of ferroptosis-related stemness genes (FRSG) and found that these genes may be involved in immune infiltration in HCC. A four-FRSG (CDKN2A, GABARAPL1, HRAS, RPL8) risk model with prognostic prediction was constructed by a Cox analysis in HCC. Among these four genes, GABARAPL1 was downregulated in HCC tumor-repopulating cells (TRC; a type of CSLC). Its downregulation decreased the sensitivity of HCC TRC to erastin- or sorafenib-triggered ferroptosis. Together, we uncovered a molecular mechanism via which CSLC could achieve tolerance to ferroptosis. Further studies may provide potential therapeutic strategies targeting CSLC in HCC.

Driven by real-life applications in geo-social networks, we study the problem of computing radius-bounded k-cores (RB-k-cores) that aims to find communities satisfying both social and spatial constraints. In particular, the model k-core (i.e., the subgraph where each vertex has at least k neighbors) is used to ensure the social cohesiveness, and a radius-bounded circle is used to restrict the locations of users in an RB-k-core. We explore several algorithmic paradigms to compute RB-k-cores, including a triple-vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating-circle-based paradigm is further enhanced by several pruning techniques to achieve better efficiency. In addition, to find representative RB-k-cores, we study the diversified radius-bounded k-core search problem, which finds t RB-k-cores to cover the most number of vertices. We first propose a baseline algorithm that identifies the distinctive RB-k-cores after finding all the RB-k-cores. Beyond this, we design algorithms that can efficiently maintain the top-t candidate RB-k-cores and also achieve a guaranteed approximation ratio. Experimental studies on both real and synthetic datasets demonstrate that our proposed techniques can efficiently compute (diversified) RB-k-cores. Moreover, our techniques can be used to compute the minimum-circle-bounded k-core and significantly outperform the existing techniques.

Based on the basic theory of health care coverage and using the research methodology of health economics, this study analyses the level of health care coverage in Malaysia based on the ratio of health care coverage expenditures as a percentage of GDP over the past 21 years and actual data from countries around the world. The results show that Malaysia is higher than the average level of South Asia and lower than high-income countries. Finally, based on the current situation of the level of health care coverage in Malaysia, policy recommendations are made to promote health care coverage.

In this paper, a novel bat algorithm with an asymmetrical weighed variational method (AWVM-BA) is proposed. The proposed algorithm employs the BA with a point-to-point modified asymmetrical variation above the three-dimensional flying region, which treats the space as sets of geodesics in a second order Euclidean weighed warped space. Mutation and the local selection procedure can be avoided at the same time, which solves the problem of a local optimum in concave regions. As shown in the results, the proposed algorithm does not have much impact on the calculation complexity and time in convex regions. It can greatly reduce the calculation time and avoid local optimization in concave regions. The disadvantage of the proposed algorithm is that the iteration number increases comparatively faster with the increase in the deviation of the wind speed. Therefore, it requires a higher hardware calculation ability.

In the field of metallurgical industry, identifying the granularity of raw materials is an essential process during transportation. We propose an image segmentation method by the GCN (global convolutional network)-Unet to extract the contour edge of raw materials granules. To obtain legible images of raw materials, a stationary industrial high speed camera is used to photograph the operating belt conveyor from above. Then, a well-trained GCN-Unet model is used to compute the images and output the results with the contour edge of granules and tiny parts of the materials segmented. We combined the U-Net with several global convolutional network models and boundary refinement blocks and compared the prediction results of the GCN-Unet and the U-Net, showing that the GCN-Unet has a better prediction ability with fewer parameters (7,876,675, while the U-Net has 31,101,448 parameters) and a higher calculating speed (about twice faster than the U-Net). Based on the CNN (convolutional neural network), our computer version method can almost replace traditional manual sampling inspection method for the corresponding overall analysis and the automatically identification process.

With the development of information technology, financial forecasting is the research direction of traditional mathematical statistics and combines emerging data mining technologies such as machine learning and deep learning to make forecasts. This paper starts from the traditional time series model , which can handle the conditional heteroscedasticity of time series data and combines the advantages of a support vector machine (SVM) that can handle the nonlinear part of financial time data . Stock market forecasts. In order to test the prediction effect of the model and compare traditional prediction methods with modern machine learning prediction methods, this paper establishes ARIMA-GARCH, SVM, and the combination ARIMA-GARCH- SVM model of these two types, respectively.