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Abstract Engineering high‐performance and low‐cost bifunctional catalysts for H2 (hydrogen evolution reaction [HER]) and O2 (oxygen evolution reaction [OER]) evolution under industrial electrocatalytic conditions remains challenging. Here, for the first time, we use the stronger electronegativity of a rare‐Earth yttrium ion (Y3+) to induce in situ NiCo‐layered double‐hydroxide nanosheets from NiCo foam (NCF) treated by a dielectric barrier discharge plasma NCF (PNCF), and then obtain nitrogen‐doped YNiCo phosphide (N‐YNiCoP/PNCF) after the phosphating process using radiofrequency plasma in nitrogen. The obtained N‐YNiCoP/PNCF has a large specific surface area, rich heterointerfaces, and an optimized electronic structure, inducing high electrocatalytic activity in HER (331 mV vs. 2000 mA cm−2) and OER (464 mV vs. 2000 mA cm−2) reactions in 1 M KOH electrolyte. X‐ray absorption spectroscopy and density functional theory quantum chemistry calculations reveal that the coordination number of CoNi decreased with the incorporation of Y atoms, which induce much shorter bonds of Ni and Co ions and promote long‐term stability of N‐YNiCoP in HER and OER under the simulated industrial conditions. Meanwhile, the CoN‐YP5 heterointerface formed by plasma N‐doping is the active center for overall water splitting. This work expands the applications of rare‐Earth elements in engineering bifunctional electrocatalysts and provides a new avenue for designing high‐performance transition‐metal‐based catalysts in the renewable energy field.

Thermal stratification phenomena in SFRs and machine learning applications in nuclear system R&D have captured the community's attention. Existing methods, such as CFD, demand significant time and computational resources, hindering the timely prediction and management of thermal stratification in reactors. In response, this paper introduces a novel approach employing data-driven models for super real-time prediction of thermal stratification. Themodel aims to enhance the efficiency and speed of thermal stratification predictions. Refined flow field data were first obtained via CFD, revealing the transition mechanism between the dominant roles of inertial and buoyancy forces during thermal stratification formation. To select the optimal data-driven model, this paper employs the proper orthogonal decomposition (POD) method to evaluate three algorithms - k-nearest neighbor (KNN), classification and regression tree (CART), and least absolute shrinkage and selection operator (LASSO) - basedon temperature data from a temperature probe. Evaluation reveals that the POD-KNN model offers notable improvements in prediction speed while ensuring consistent accuracy, outperforming traditional numerical simulation methods for super real-time thermal stratification prediction.

Abstract Hydrogen generated by water electrolysis is considered as one of the most promising protocols to partly replace the roles of traditional fossil fuels. However, high‐performance electrocatalyst satisfied with the industrial requirement still faces significant challenges. Low‐temperature plasma contains numerous high‐energy ions, electrons and other reactive species, which can provide a highly reactive environment for tuning the physio‐chemical structures of catalysts through plasma milling, etching, doping and/or deposition. It is well‐known that high‐temperature micro‐filaments contained in plasmas can cause some special modifications of the catalyst surface, thus effectively adjusting the physio‐chemical structure of latterly engineered compounds. Therefore, low‐temperature plasma technologies, especially the dielectric barrier discharge (DBD) and radio frequency (RF) plasmas, can be considered as a green and sustainable strategy for engineering high‐performance electrocatalysts for water splitting (hydrogen evolution reaction [HER]; oxygen evolution reaction [OER]). Herein, recent progress of DBD and RF plasmas for fabricating and modifying transition metal‐based electrocatalysts (e.g. sulphide, phosphide, selenide, oxide, hydroxide) for hydrogen evolution reaction or OER is comprehensively reviewed, and the role of plasma is also discussed.

Abstract Hyperhomocysteinemia with hypertension can synergistically increase the risk of stroke. The China stroke primary prevention trial showed that combining 0.8 mg folic acid (FA) with angiotensin‐converting enzyme inhibitor (ACEI) can effectively lower plasma total homocysteine (tHcy) and blood pressure (BP); and reduce first stroke risk by additional 21% compared to ACEI alone. However, intolerance to ACEI is common in Asians and amlodipine can be alternative. This is a multicenter, randomized, double‐blind, parallel‐controlled clinical trial (RCT) which evaluated whether amlodipine combined with FA is more efficacious than amlodipine alone in lowering tHcy and BP among Chinese hypertensive with hyperhomocysteinemia and intolerance to ACEI. 351 Eligible patients were randomly assigned by 1:1:1 ratio to receive amlodipine‐FA tablet daily (amlodipine 5 mg/FA 0.4 mg, A group); amlodipine 5 mg/FA 0.8 mg tablet daily (B group); amlodipine 5 mg daily (C group, control group). Follow‐up was conducted at 2, 4, 6, and 8 weeks. The primary outcome was efficacy of lowering both tHcy and BP at the end of 8‐week treatment. Compared with C group, A group had a significantly higher rate of lowering both tHcy and BP (23.3% vs. 6.0%; Odds Ratio [OR], 8.68; 95% CI, 3.04‐24.78, P

Abstract Retraction: [Kaiyi Qiu, Xin Liu, Jie Liu, Hongbo Ma, Jingya Li, Zhengchao Zhang, Guangliang Chen, Li Cai, Research on tridimensional monitoring and defence technology of substation, IET Circuits, Devices & Systems 2022 (https://doi.org/10.1049/cds2.12129)]. The above article from IET Circuits, Devices & Systems, published online on 14 September 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Harry E. Ruda, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

Abstract Glioma stem cells (GSCs) are the important cause of tumorigenesis, recurrence, and chemo(radio)resistance in glioma. Targeting GSCs helps improve the outcomes of glioma treatment. Polo-like kinase 1 (PLK1) is a member of the serine/threonine protein kinase family, which is highly conserved. In recent years, it has been suggested that increased levels of PLK1 and its activity are associated with tumor progression and poor prognosis. We aimed to identify whether PLK1 plays a critical role in stemness maintenance and apoptosis regulation in GSCs. Here we identify that PLK1 inhibition can induce apoptosis and DNA damage of GSCs, we have also delineat the possible underlying molecular mechanisms: PLK1 interacts with YBX1 and directly phosphorylates serine 174 and serine 176 of YBX1. Inhibition of PLK1 reduces the phosphorylation level of YBX1, and decreased phosphorylation of YBX1 prevents its nuclear translocation, thereby inducing apoptosis and DNA damage of GSCs. We confirmed that YBX1 knockdown resulted in the apoptosis and DNA damage of GSCs. These findings uncover that PLK1 inhibition induces cell apoptosis and DNA damage in GSCs through YBX1 phosphorylation, providing new insights into the mechanism by which PLK1 inhibition contributes to the apoptosis of and DNA damage in gliomas.

Abstract Background Glioblastoma is one of the malignant tumors of the central nervous system with high lethality, high disability and low survival rate. Effective induction of its death is one of the existing challenges. In recent studies, heat shock protein 27 (HSP27) has been shown to be associated with ferroptosis; therefore, targeting HSP27 may be a potential therapeutic approach for GBM. Methods Immunohistochemistry and western blot analysis were used to detect the expression of HSP27 in GBM tissues. CCK8, plate clone formation assay, EdU proliferation assay for cell proliferation ability, PI, LDH release assay for cell viability. Reactive oxygen, iron levels, and mitochondrial potential for HSP27 silencing were assayed for ferrotosis in vitro. Western blotting and IP were used to verify the relationship between HSP27 and ACSL4. The effect of knockdown of HSP27 on tumor growth capacity was assessed in an intracranial xenograft model. Results HSP27 was significantly highly expressed in GBM. In vitro experiments, knockdown of HSP27 significantly induced ferroptosis in GBM cells. IP and western blot demonstrated a sumo-ization link between HSP27 and ACSL4. In vivo experiments, HSP27 deficiency retarded tumor growth rate by promoting ferroptosis. Conclusions HSP27 deficiency promotes GBM ferroptosis. Targeting HSP27 may serve as a new direction for GBM treatment.

Abstract Background Anastomotic leakage (AL) is one of the most serious postoperative complications after colorectal anastomosis. This study aims to evaluate the feasibility and diagnostic accuracy of magnetic resonance imaging (MRI) in the early detection of AL in patients with clinically suspected AL after rectal anterior resection. Methods This was a prospective study including patients who underwent anterior resection and postoperative MRI examination. AL was diagnosed by comprehensive indictors, which were mainly confirmed by clinical signs, symptoms, and retrograde contrast enema (RCE) radiography. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of diagnosing AL with MRI were calculated. Results In total, 347 patients received anterior resection for rectal cancer, and 28 patients were suspected to have AL. Finally, 23 patients were included and received MRI examination. The median time interval from surgery to MRI was 10 days (3–21 days). The median distance from anastomosis to anal verge was 4.0 cm (2.0–10 cm), and 11 patients underwent diverted ileostomy. Eighteen patients had an anastomotic leak, including one patient who had a pelvic abscess and five patients who had no evidence of AL in the MRI examination. The overall sensitivity and specificity were 94.4% (95% CI 70.6% to 99.7%) and 80% (95% CI 29.8% to 98.9%), respectively. The PPV was 0.94 (95% CI 0.71 to 0.99) and the NPV was 0.80 (95% CI 0.29 to 0.99). For patients who had anastomosis less than 5 cm, the diagnostic accuracy of MRI was 93.7% (15/16). T2-weighted imaging with fat suppression can effectively reveal the leak track. Conclusions The accuracy of plain MRI examination in diagnosing AL was favorable for patients with a suspected AL. T2-weighted imaging with fat suppression was the best imaging modality to diagnose AL. A multicenter prospective study with more samples is needed to further determine the safety and feasibility of MRI in the diagnosis of AL.

Abstract Ferroptosis, a novel non-apoptotic form of cell death, can induce tumor cell death and treatment resistance. Lipid metabolism is closely related to ferroptosis; however, the effect of mammary adipocytes on breast cancer ferroptosis remains to be elucidated. Here, we established the co-culture system of adipocyte-breast cancer cells and revealed the protection of triple-negative breast cancer from ferroptosis by adipocytes. Then, we performed the lipidomics analysis comparing lipid metabolites of co-cultured and normal-cultured cells. Mechanistically, oleic acid secreted from adipocytes inhibited lipid peroxidation and ferroptosis of triple-negative breast cancer cells in the presence of ACSL3. Taken together, mammary adipocytes can protect breast cancer cells from ferroptosis through oleic acid in the presence of ACSL3. These findings could provide new ideas and targets for tumor treatment.

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

Abstract Objectives To define different clinical phenotypes and assess prognostic factors of adult-onset Still’s disease (AOSD). Methods Overall, 492 patients with AOSD seen between 2004 and 2018 at a single centre were retrospectively studied. Results Of these patients, 78% were female, and the median age at onset was 34 (25–49) years [median (25th–75th percentile)]. The median follow-up time was 7 (3–10) years [median (25th–75th percentile)]. Clinical manifestations at admission were used to subdivide patients with AOSD as follows: systemic inflammation (cluster 1), pure (cluster 2), and intermediate (cluster 3). Each subtype had distinct clinical manifestations and prognoses: cluster 1 (34.6%)—multiple organ manifestations, highest infection rate and mortality, and more than half of the patients with at least one relapse during follow-up; cluster 2 (21.3%)—exclusively female, rash and joint involvement, no internal organ involvement, no mortality, and most of the patients with a monocyclic course; and cluster 3 (44.1%)—less infection rate, no serious complications, and lower mortality rate. The 5- and 10-year survival rates after diagnosis were 92.4% and 86.9%, respectively. Independent risk factors for mortality were age at onset ≥50 (hazard ratio (HR): 6.78, 95% CI: 2.10–21.89), hepatomegaly (HR: 5.05, 95% CI: 1.44–17.70), infection (HR: 15.56, 95% CI: 5.88–41.20), and MAS (HR: 26.82, 95% CI: 7.52–95.60). Conclusion Three subtypes of AOSD were identified with distinct clinical manifestations and prognoses. Age at onset ≥50, hepatomegaly, infection, and MAS were prognostic factors for AOSD mortality.

Background: Glioblastoma (GBM) is a very frequent primary tumour in the cerebrospinal nervous system. Temozolomide (TMZ) is the first-line treatment for patients with GBM. However, some of GBM patients do not respond to TMZ. O6-methylguanine-DNA-methytransferase (MGMT) remains a major cause. In a previous study, we detected antibodies against MGMT peptides in patients with glioma, and five highly responsive autoantibodies anti-MGMT-02, anti-MGMT-04, anti-MGMT-07, anti-MGMT-10, and anti-MGMT-18 were identified that could be used to dynamically assess chemotherapy-resistant TMZ. Therefore, targeting MGMT peptides may be a potential therapeutic approach for GBM to fight TMZ resistance. Methods: First, MGMT-02 and MGMT-04 polypeptides with cell-penetrating peptides were designed and connected to FITC tracer for immunofluorescence localisation. CCK-8 and colony formation assay were performed to evaluate cell proliferation ability. Western blot and immunofluorescence analysis were used to detected the expression of apoptosis-related protein. Flow cytometry was used to detect the proportion of apoptosis in cells. TMZ-resistant effect of MGMT-02/04 peptides was assessed in intracranial xenograft nude mouse model. Results: We also found reduced apoptosis of cells treated with MGMT-02 and MGMT-04 peptides and TMZ compared with those treated separately with TMZ in vivo and in vitro experiences. Conclusion: The results of this study indicate that MGMT-02 and MGMT-04 peptides have a role in glioma resistance and that MGMT peptides may serve as a precise target for TMZ-resistant GBM.

This research serves to advance the development of engineering computational fluid dynamics (CFD) computing efficiency for the analysis of pressurized water reactor (PWR) core using rod-type fuel assemblies with mixing vanes (one kind of typical PWR core). In this research, a CFD scheme based on the reconstruction of the initial fine flow field (RIF CFD scheme) is proposed and analyzed. The RIF scheme is based on the quantitative regulation of flow velocities in the rod-type PWR core and the principle that the CFD computing efficiency can be improved greatly by a perfect initialization. In this paper, it is discovered that the RIF scheme can significantly improve the computing efficiency of the CFD computation for the rod-type PWR core. Furthermore, the RIF scheme also can reduce the computing resources needed for effective data storage of the large fluid domain in a rod-type PWR core. Moreover, a flow-ranking RIF CFD scheme is also designed based on the ranking of the flow rate, which enhances the utilization of the flow field with a closed flow rate to reconstruct the fine flow field. The flow-ranking RIF CFD scheme also proved to be very effective in improving the CFD efficiency for the rod-type PWR core.

Abstract Background Glioblastomas (GBMs) are grade IV central nervous system tumors characterized by a poor prognosis and a short median overall survival. Effective induction of GBM cell death is difficult because the GBM cell population is genetically unstable, resistant to chemotherapy and highly angiogenic. In recent studies, ubiquitin-specific protease 7 (USP7) is shown to scavenge ubiquitin from oncogenic protein substrates, so effective inhibition of USP7 may be a potential key treatment for GBM. Methods Immunohistochemistry and western blotting were used to detect the expression of USP7 in GBM tissues. In vitro apoptosis assay of USP7 inhibition was performed by western blotting, immunofluorescence, and flow cytometry. Anti-apoptotic substrates of USP7 were defined by Co-IP and TMT proteomics. Western blotting and IP were used to verify the relationship between USP7 and its substrate. In an in vivo experiment using an intracranial xenograft model in nude mice was constructed to assess the therapeutic effect of target USP7. Results Immunohistochemistry and western blotting confirmed that USP7 was significantly upregulated in glioblastoma samples. In in vitro experiments, inhibition of USP7 in GBM induced significant apoptosis. Co-IP and TMT proteomics identified a key anti-apoptotic substrate of USP7, ADP-ribosylation factor 4 (ARF4). Western blotting and IP confirmed that USP7 interacted directly with ARF4 and catalyzed the removal of the K48-linked polyubiquitinated chain that binded to ARF4. In addition, in vivo experiments revealed that USP7 inhibition significantly suppressed tumor growth and promoted the expression of apoptotic genes. Conclusions Targeted inhibition of USP7 enhances the ubiquitination of ARF4 and ultimately mediates the apoptosis of GBM cells. In a clinical sense, P5091 as a novel specific inhibitor of USP7 may be an effective approach for the treatment of GBM.

Background Primary breast diffuse large B-cell lymphoma (PB-DLBCL) is a rare subtype of non-Hodgkin lymphoma (NHL) with limited data on the clinical features and prognostic factors. Patients and Methods A consecutive cohort of patients with PB-DLBCL was retrospectively analyzed in our hospital from February 1997 through July 2018. The primary endpoint is overall survival (OS) contributing to any cause. Results A total of 76 patients were diagnosed with PB-DLBCL. The median age at diagnosis was 51 years (range: 25-80 years), with female prevalence (98.7%). Forty (52.6%) patients had right-sided breast involvement but no bilateral breast involvement at diagnosis. Overall, disease stages IE and IIE were seen in 55 (72.4%) and 21 (27.6%) patients, respectively. According to the stage-modified International Prognostic Index (IPI), 37 (48.7%) patients were classified in the very good risk group (IPI 0). Of the 72 patients available, the non-germinal center B-cell (non-GCB) subtype of DLBCL was observed in 66 (91.6%) patients. All patients received anthracycline-based chemotherapy, 56 (73.7%) with rituximab, 31 (40.8%) also with additional radiation therapy, and 14 (18.4%) patients received a prophylactic intrathecal injection. Seven (9.2%) patients had refractory disease. With a median follow-up of 6.8 years (range 0.4-25.0 years), 10 (13.2%) patients had a relapse in the central nervous system (CNS) site. The 5-year and 10-year OS of all the patients was 97.2% (95% CI: 99.3-89.5) and 84.8% (95% CI: 70.0-93.5), respectively. The median OS was not reached. The median progression-free survival (PFS) was 10.3 years for patients with PB-DLBCL(Figure 2B). The 5-year PFS of all the patients was 76.3% (95% CI: 64.6-84.6). Univariate analysis revealed several prognostic factors, including stage-modified IPI, breast surgery, refractory disease, and CNS relapse. Multivariate analyses produced two independent prognostic factors for patients with PB-DLBCL, including stage-modified IPI score (2-3 versus 0) (hazard ratio: 19.114, 95% CI 1.841 to 198.451, p=0.013) and CNS relapse (hazard ratio: 5.522, 95% CI 1.059 to 28.788, p=0.043). Conclusion In our cohort, PB-DLBCL clinical features are similar to prior literature reports. Stage-modified IPI score and CNS relapse were associated with overall survival.

In this paper, the coupling of fine-mesh computational fluid dynamics (CFD) thermal-hydraulics (TH) code and neutronics code is achieved using the Ansys Fluent User Defined Function (UDF) for code development, including parallel meshing mapping, data computation, and data transfer. Also, some CFD schemes are designed for mesh mapping and data transfer to guarantee physical conservation in the coupling computation. Because there is no rigorous research that gives robust guidance on the various CFD schemes that must be obtained before the fine-mesh coupling computation, this work presents a quantitative analysis of the CFD meshing and mapping schemes to improve the accuracy of the value and location of key physical prediction. Furthermore, the effect of the sub-pin scale coupling computation is also studied. It is observed that even the pin-resolved coupling computation can also create a large deviation in the maximum value and spatial locations, which also proves the significance of the research on mesh mapping and data transfer for CFD code in a coupling computation.

Abstract Background This study aimed to test the feasibility and titration methods used to achieve specific blood pressure (BP) control targets in hypertensive patients of rural China. Methods A randomized, controlled, open-label trial was conducted in Rongcheng, China. We enrolled 105 hypertensive participants aged over 60 years, and who had no history of stroke or cardiovascular disease. The patients were randomly assigned to one of three systolic-BP target groups: standard: 140 to

ObjectiveThe aim of this study was to investigate the association of total cerebral small vessel disease (cSVD) score with the risk of intracerebral hemorrhage (ICH) in patients with acute ischemic stroke who received intravenous thrombolysis (IVT) using recombinant tissue-plasminogen activator (rt-PA).MethodsWe retrospectively reviewed clinical data from two stroke registries of patients with acute ischemic stroke treated with IVT. We assessed the baseline magnetic resonance (MR) visible cSVD markers and total cSVD score (ranging from 0 to 4) between patients with and without ICH after IVT. Logistic regression analysis was used to determine the association of total cSVD score with the risk of ICH after IVT, adjusted for cofounders selected by least absolute shrinkage and selection operator (LASSO). We additionally performed an E-value analysis to fully explain away a specific exposure-outcome association. Receiver operating characteristic (ROC) curve analysis was used to quantify the predictive potential of the total cSVD score for any ICH after IVT.ResultsAmong 271 eligible patients, 55 (20.3%) patients experienced any ICH, 16 (5.9%) patients experienced a symptomatic ICH (sICH), and 5 (1.85%) patients had remote intracranial parenchymal hemorrhage (rPH). Logistic regression analysis showed that the risk of any ICH increased with increasing cSVD score [per unit increase, adjusted odds ratio (OR) 2.03, 95% CI 1.22–3.41, P = 0.007]. Sensitivity analyses using E-value revealed that it would need moderately robust unobserved confounding to render the exposure-outcome (cSVD-any ICH) association null. ROC analysis showed that compared with the National Institutes of Health Stroke Scale (NIHSS) score alone, a combination of cSVD and NIHSS score had a larger area under the curve for any ICH (0.811, 95% CI 0.756–0.866 vs. 0.784, 95% CI 0.723–0.846, P = 0.0004).ConclusionThe total cSVD score is associated with an increased risk of any ICH after IVT and improves prediction for any ICH compared with NIHSS alone.

Purpose: To identify factors associated with lymphoma in patients with anterior Mycobacterium tuberculosis (TB). Methods: A retrospective case-control analysis was performed in a highly tuberculosis-endemic area. TB-infected patients were retrospectively identified when a history of TB exists before the diagnosis of lymphoma. Inpatients with lymphoma (n=1,057) and benign tumors (n=12,916) were consecutively enrolled at Xinjiang Medical University Cancer Hospital between January 2016 and December 2019. Results: The proportion of TB in patients with lymphoma (n=148, 14.0%) was significantly higher than that in the control (benign tumor) group (n=175, 1.4%) (p60 years) was associated with the B-cell NHL. Conclusion: A high proportion of Mycobacterium tuberculosis infection was found in patients with lymphoma. In TB-infected patients, older age and male gender are associated with susceptibility to lymphoma, suggesting the awareness of a screening program might be useful for early detection of lymphoma.

Abstract Bromodomain and extraterminal domain (BET) family proteins are considered to be epigenetic readers that regulate gene expression by recognizing acetyl lysine residues on histones and nonhistone chromatin factors and have been classified as curative targets for a variety of cancers. Glioma‐initiating cells (GICs), which commit self‐renewal, perpetual proliferation, multidirectional differentiation, and vigorous tumorigenicity, sustain the peculiar genetic and epigenetic diversification in the GBM patients, thus, GICs result in tumor recurrence. Abundant evidence demonstrates that BET proteins regulate differentiation of stem cells. However, it endures ambiguous how individual BET proteins take part in GIC advancement, and how do small molecule inhibitors like I‐BET151 target functional autonomous BET proteins. Here, we validated that BRD4, not BRD2 or BRD3, has value in targeted glioma therapy. We announce a signaling pathway concerning BRD4 and Notch1 that sustains the self‐renewal of GICs. Moreover, in‐depth mechanistic research showed that BRD4 was concentrated at the promoter region of Notch1 and may be involved in the process of tumor metabolism. Furthermore, in intracranial models, I‐BET151 eliminated U87 GICs’ tumorigenicity. The outcomes of this research could be conducive to design clinical trials for treatment of glioma based on BRD4.

Numerical simulation has been widely used in nuclear reactor safety analyses to gain insight into key phenomena. The Critical Heat Flux (CHF) is one of the limiting criteria in the design and operation of nuclear reactors. It is a two-phase flow phenomenon, which rapidly decreases the heat transfer performance at the rod surface. This paper presents a numerical simulation of a steady state flow in a vertical pipe to predict the CHF phenomena. The detailed Computational Fluid Dynamic (CFD) modeling methodology was developed using FLUENT. Eulerian two-phase flow model is used to model the flow and heat transfer phenomena. In order to gain the peak wall temperature accurately and stably, the effect of different turbulence models and wall functions are investigated based on different grids. Results show that O type grid should be used for the simulation of CHF phenomenon. Grids with Y+ larger than 70 are recommended for the CHF simulation because of the acceptable results of all the turbulence models while Grids with Y+ lower than 50 should be avoided. To predict the dry-out position accurately in a fine grid, Realizable k-ε model with standard wall function is recommended. These conclusions have some reference significance to better predict the CHF phenomena of vertical pipe. It can also be expanded to rod bundle of Boiling Water Reactor (BWR) by using same pressure condition.

CFD simulation for a PWR is an important part for the development of Numerical Virtual Reactor (NVR) in Harbin Engineering University of China. CFD simulation can provide the detailed information of the flow and heat transfer process in a PWR. However, a large number of narrow flow channels with numerous complex structures (mixing vanes, dimples, springs, etc.) are located in a typical PWR. To obtain a better CFD simulation, the challenges created by these structural features were analyzed and some quantitative regularity and estimation were given in this paper. It was found that both computing resources and time are in great need for the CFD simulation of a whole reactor. These challenges have to be resolved, so two schemes were designed to assist/realize the reduction of the simulation burden on resources and time. One scheme is used to predict the combined efficiency of the simulation conditions (configuration of computing resources and application of simulation schemes), so it can assist the better choice/decision of the combination of the simulation conditions. The other scheme is based on the suitable simplification and modification, and it can directly reduce great computing burden.

The flow characteristics of the primary side of the helical coil once-through steam generator (OTSG) have a significant impact on the safe operation of the reactor. Different from the land-based stationary working conditions, the flow of the primary side of the OTSG is influenced by sea waves and winds under ocean conditions. The rigid body motion model is used to calculate the flow of the primary side under different ocean conditions, including heeling, trimming, rolling, pitching, heaving, and combined conditions. The results show that under heeling and trimming conditions, the static pressure of the primary side is different from that under vertical conditions due to the influence of gravity, but its impact on the flow field is relatively small. Under rolling and pitching conditions, the mean flow velocity and pressure drop change periodically with the movement of the OTSG, and the smaller the sway angle, the smaller the variation amplitude of the mean flow velocity and pressure drop. Under the heaving condition, the variation amplitude of the mean flow velocity is greater than that under rolling and pitching conditions, and reverse flow occurs. Under the combined heaving and rolling condition, the mean flow velocity and pressure drop are influenced by the period and amplitude of both the two motions. [ABSTRACT FROM AUTHOR]

Oriented remote sensing object detection (ORSOD) has gained increasing significance in both military and civilian applications due to the necessity of accurately identifying objects with varying shapes and orientations in remote sensing data. Traditional ORSOD methods often employ fixed label assignment strategies to differentiate between positive and negative samples. However, most of them frequently overlook the impact of object shape on sample quality, leading to an imbalanced distribution of positive samples and exacerbating the inconsistency between classification and regression tasks, thereby limiting detection performance. To address these challenges, we propose a novel shape-dependent assignment (SDA) method that dynamically differentiates positive and negative samples based on object shape. It introduces a new metric for evaluating sample box quality by considering angular differences relative to ground truth (GT) boxes and adjusts the sample scoring threshold according to the aspect ratio of each GT box. In addition, we present a DIoU-adaptive weighting (DAW) module that enhances the interaction between classification and regression tasks by leveraging the distance-IoU metric. This approach not only balances the quantity of samples but also improves their quality, enabling more effective training schemes for samples of varying qualities. We validate our proposed methods through extensive experiments on three challenging ORSOD datasets: DOTA-1.0, HRSC2016, and UCAS-AOD. The results demonstrate that our approach achieves significant improvements, especially for objects with large aspect ratios.

In smart city contexts, traditional methods for semantic segmentation are affected by adverse conditions, such as rain, fog, or darkness. One challenge is the limited availability of semantic segmentation datasets, specifically for autonomous driving in adverse conditions, and the high cost of labeling such datasets. To address this problem, unsupervised domain adaptation (UDA) is commonly employed. In UDA, the source domain contains data from good weather conditions, while the target domain contains data from adverse weather conditions. The Adverse Conditions Dataset with Correspondences (ACDC) provides reference images taken at different times but in the same location, which can serve as an intermediate domain, offering additional semantic information. In this study, we introduce a method that leverages both the intermediate domain and frequency information to improve semantic segmentation in smart city environments. Specifically, we extract the region with the largest difference in standard deviation and entropy values from the reference image as the intermediate domain. Secondly, we introduce the Fourier Exponential Decreasing Sampling (FEDS) algorithm to facilitate more reasonable learning of frequency domain information. Finally, we design an efficient decoder network that outperforms the DAFormer network by reducing network parameters by 28.00%. When compared to the DAFormer work, our proposed approach demonstrates significant performance improvements, increasing by 6.77%, 5.34%, 6.36%, and 5.93% in mean Intersection over Union (mIoU) for Cityscapes to ACDC night, foggy, rainy, and snowy, respectively.

Remote sensing (RS) image semantic segmentation using deep convolutional neural networks (DCNNs) has shown great success in various applications. However, the high dependence on annotated data makes it challenging for DCNNs to adapt to different RS scenes. To address this challenge, we propose a cross-domain RS image semantic segmentation task that considers ground sampling distance, remote sensing sensor variation, and different geographical landscapes as the main factors causing domain shifts between source and target images. To mitigate the negative impact of domain shift, we propose a self-training guided disentangled adaptation network (ST-DASegNet) that consists of source and target student backbones to extract source-style and target-style features. To align cross-domain single-style features, we adopt feature-level adversarial learning. We also propose a domain disentangled module (DDM) to extract universal and distinct features from single-domain cross-style features. Finally, we fuse these features and generate predictions using source and target student decoders. Moreover, we employ an exponential moving average (EMA) based cross-domain separated self-training mechanism to ease the instability and disadvantageous effect during adversarial optimization. Our experiments on several prominent RS datasets (Potsdam, Vaihingen, and LoveDA) demonstrate that ST-DASegNet outperforms previous methods and achieves new state-of-the-art results. Visualization and analysis also confirm the interpretability of ST-DASegNet. The code is publicly available at https://github.com/cv516Buaa/ST-DASegNet.

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task.

We report a facile method by which to synthesize Au/Cdot nanohybrids using carbon dots (Cdots) obtained from rice straw as a direct reducing agent as well as a stabilizer. Rice-straw-derived Cdots possess oxygen-containing functional groups that act as an electron-donating source to reduce Au3+ to Au0 without the use of chemical reagents for the reduction of Au precursors. Additionally, the strong Au-carbon interaction promotes a high electron transfer of the Au/Cdot nanohybrid electrocatalyst, resulting in enhanced performance of the hydrogen evolution reaction (HER). The overpotential is 150 mV at a current density of 10 mA·cm−2 and the TOF value is 0.06·s−1, representing the most outstanding HER performance among Au-based catalysts as a non-Pt HER electrocatalyst. The HER activity is stably maintained for 2000 cycles as well as for 48 h. The synthesis of Cdots, the hybridization of Au/Cdot electrocatalyst, and the production of clean hydrogen fuel using Au/Cdots are zero CO2 emission strategies, not only preventing the emission of pollutants but also satisfying the energy needs of humankind. [ABSTRACT FROM AUTHOR]

Total saponins extracted from Dioscorea collettii (TSD), extracts of the Chinese herb Dioscorea, are thought to exhibit therapeutic benefit in gouty arthritis. However, its exact mechanism remains unclear. The current study aimed to elucidate the underlying mechanisms by investigating the effects of TSD on the inflammation induced by monosodium urate (MSU) crystals in THP-1 macrophages. The viability of THP-1 macrophages was examined using the MTT assay and the levels of inflammatory cytokines, including interleukin (IL)-1ß, IL- 18 and tumor necrosis factor (TNF)-a, released by the cells were quantitatively measured using ELISA kits. The results revealed that the protein level of cluster of differentiation 11b increased in THP-1 cells treated with 100 ng/ml phorbol ester, suggesting that monocytic THP-1 cells were successfully differentiated into macrophages. TSD decreased the levels of inflammatory cytokines, including TNF-a, IL- 18 and IL- 1ß, secreted by THP-1 macrophages. As the release of IL-1ß and IL- 18 is dependent on the NLR family pyrin domain containing 3 (NALP3) inflammasome and caspase-1, the current study investigated the effect of TSD on the aforementioned proteins. The results revealed that TSD decreased the protein levels of NALP3 and apoptosis-associated speck-like, which serve important roles in the assembly of the NALP3 inflammasome. Furthermore, NALP3 inflammasome-related proteins were also decreased by TSD in rotenone induced THP-1 macrophages, TSD inhibited the activation of caspase-1 and rotenone-induced NALP3 inflammasome activation in THP-1 macrophages. The results obtained in the current study revealed that TSD attenuated MSU crystal-induced inflammation by inhibiting rotenone-induced activation of the NALP3 inflammasome and caspase-1, suggesting that these two proteins may be novel targets for the treatment of gouty arthritis. [ABSTRACT FROM AUTHOR]

In this paper, a highly catalytic and nanosized Ag/Co3O4 composite for rhodamine B(RhB) degradation was fabricated by using the co-precipitation method at room temperature. The Ag/Co3O4 structure and catalytic properties were characterized through scanning electron microscopy (SEM), transmission electron microscopy (TEM), Brunauer–Emmett–Teller (BET) gas-sorption measurements, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and UV-Vis spectroscopy. The results showed that the Co3O4 nanosheets prepared by hydrothermal synthesis mainly exposed (2 2 0) and (1 1 1) facets, which played an important role in determining its catalytic oxidation performance. The Co3O4 nanosheets doped with Ag nanoparticles by a simple silver-mirror reaction exhibited a stable and well-dispersed property in dye solution. Compared to the Ag and Co3O4 nanostructure, the Ag nanoparticles with bigger diameter (10 nm) on Co3O4 surface also readily produced surface-active oxygen species and exhibited a higher catalytic activity for the degradation of RhB solution (5 mg ⋅ L-1) under the visible light. The kinetic constant K of Ag/Co3O4 catalyst for RhB degradation reaction was evaluated to 0.02724 min-1, which is relatively higher than those reported in the literatures. [ABSTRACT FROM AUTHOR]

We present a fast, versatile and adaptive-multiscale algorithm for analyzing a wide-variety of DNA microarray data. Its primary application is in normalization of array data as well as subsequent identification of ‘enriched targets’, e.g. differentially expressed genes in expression profiling arrays and enriched sites in ChIP-on-chip experimental data.We show how to accommodate the unique characteristics of ChIP-on-chip data, where the set of ‘enriched targets’ is large, asymmetric and whose proportion to the whole data varies locally.Contact:lerman@umn.eduSupplementary information: Supplementary figures, related preprint, free software as well as our raw DNA microarray data with PCR validations are available at http://www.math.umn.edu/~lerman/supp/bioinfo06 as well as Bioinformatics online. [ABSTRACT FROM AUTHOR]

Using allylamine monomer, a matrix of nanocones was fabricated by applying a low-temperature plasma plume without any catalysts and template. This nanocone acted as an adhesion layer immobilizing DNA probe for DNA hybridization assay. A simple conceptual model to describe the growth of the nanocones was also developed. The highest density of amino-labeled DNA probe was about 1.6 pM/cm2 confirmed by the dyed oligonucleotide, and each nanocone contained nearly 3×102 amine groups. This strategy provides a robust procedure to immobilize DNA, which is a very useful substrate for fabricating nanobiosensors. [ABSTRACT FROM AUTHOR]

Inspired by the formation process of natural thundershowers, we fabricated an organic nanocone matrix-like bamboo-shoot by using atmospheric plasma polymerization in the absence of any catalyst or template. The discharging characteristics affected the nanocone shape and distribution in an obvious way. The nanocones prepared by helium (He) plasma were about 120 nm in diameter and 80 nm high. The nanostructured surface acted as an adhesion layer immobilizing DNA probes for DNA hybridization assay. The density of NH2-DNA probes prepared by He, argon (Ar) and nitrogen (N2) plasma was confirmed by the dyed oligonucleotide and was found to be 3.2, 1.0 and 0.6 pM cm[?]2, respectively. Each nanocone prepared by helium plasma contains nearly 4 × 102 amine groups. [ABSTRACT FROM AUTHOR]

Background. Rhizomes from Dioscorea collettii are extensively used in traditional medicine for the treatment of arthritic diseases, particularly gouty arthritis (GA). This study aims to investigate whether the total saponin of Dioscorea collettii (TSD) can attenuate monosodium urate (MSU) crystal-induced inflammatory effects by suppressing the activation of the TLR4/NF-κB signaling pathway in vivo and in vitro. Methods. Seventy-two male Wistar rats and THP-1 cells were used in this study. Pathological examination was used to examine the ankle joints of rats. The expression levels of TLR4, NF-κB, MyD88, and IL-1β were detected by qRT-PCR, Western blotting, or immunofluorescence. Results. Compared with those in the normal group, the ankle joints of rats in the model group exhibited significant swelling, synovial tissue hyperplasia, inflammatory cell infiltration, and increased expression of IL-1β protein. The joint swelling degree of rats in the TSD high- and medium-dose groups and the colchicine group was significantly decreased, and the histopathology was obviously improved. TSD and colchicine reduced the levels of IL-1β and TNF-α in synovial fluid. They also decreased the mRNA expression of TLR4, NF-κB, and IL-1β in rat joint synovial tissue and the protein expression of TLR4, MyD88, and NF-κB. NF-κB protein expression in both the cytoplasm and nuclei of THP-1 cells showed the opposite trend. Furthermore, immunofluorescence showed that TSD reduced the nuclear translocation of NF-κBp65 in the model group. Conclusion. TSD exhibits an anti-inflammatory effect in the MSU-induced inflammation model, and the mechanism may be to reduce the production of cytokines by inhibiting the activation of the TLR4/NF-κB signaling pathway. [ABSTRACT FROM AUTHOR]

In this paper, we reported a recyclable and highly active porous catalyst of titanate nanowires (TNWs) coated with well-distributed Co3O4 nanoparticles (NPs) (Co3O4/TNWs). Sodium ions of TNWs were exchanged with hydrogen ions in the dilute nitric acid, and this protocol was very suitable for capturing cobalt ions. X-ray diffraction (XRD) demonstrated the existence of Co3O4 phase with unique lattice planes, such as (220), (311) and (511). Electron microscopes (FE-SEM and TEM) indicated that the Co3O4 NPs with an average diameter of 22±3nm were coated uniformly on TNWs surface (average diameter: 37±5.5nm), and the Co3O4 NPs mainly exposed their (220) and (222) active planes. XPS analysis confirms the formation of Co3O4 phase by the presence of Co 2p peaks at 780.1eV (2p 3/2) and 795.5eV (2p 1/2). Methylene blue (MB) and other organic dyes (rhodamine B (RhB) and methyl orange (MO)) were chosen as target compounds for catalytic degradation under indoor scattering light. Compared to the original Co3O4/TNWs catalyst, the catalytic efficiency of nanoscaled catalyst with oxone for MB was about 15 times higher, and the MB solution (10mgL-1) was completely degraded within 8min. The catalytic activity of recycled catalyst used in the sixth run still remained very active, and the degradation time for MB was only 16min. The nanosized catalyst also had a high activity for dyes of RhB (10mgL-1) and MO (10mgL-1), as the degradation efficiencies of RhB and MO after 10min of contact time were about 90.2% and 92.6%, respectively. [ABSTRACT FROM AUTHOR]