498 research outputs found

    The persistent shadow of the supermassive black hole of M 87

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    In April 2019, the Event Horizon Telescope (EHT) Collaboration reported the first-ever event-horizon-scale images of a black hole, resolving the central compact radio source in the giant elliptical galaxy M 87. These images reveal a ring with a southerly brightness distribution and a diameter of ∼42 μas, consistent with the predicted size and shape of a shadow produced by the gravitationally lensed emission around a supermassive black hole. These results were obtained as part of the April 2017 EHT observation campaign, using a global very long baseline interferometric radio array operating at a wavelength of 1.3 mm. Here, we present results based on the second EHT observing campaign, taking place in April 2018 with an improved array, wider frequency coverage, and increased bandwidth. In particular, the additional baselines provided by the Greenland telescope improved the coverage of the array. Multiyear EHT observations provide independent snapshots of the horizon-scale emission, allowing us to confirm the persistence, size, and shape of the black hole shadow, and constrain the intrinsic structural variability of the accretion flow. We have confirmed the presence of an asymmetric ring structure, brighter in the southwest, with a median diameter of 43.3−3.1+1.5 μas. The diameter of the 2018 ring is remarkably consistent with the diameter obtained from the previous 2017 observations. On the other hand, the position angle of the brightness asymmetry in 2018 is shifted by about 30° relative to 2017. The perennial persistence of the ring and its diameter robustly support the interpretation that the ring is formed by lensed emission surrounding a Kerr black hole with a mass ∼6.5 × 109 M⊙. The significant change in the ring brightness asymmetry implies a spin axis that is more consistent with the position angle of the large-scale jet

    Research progress in hydrogel microneedle

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    Microneedles (MN) as a minimally invasive device consisting of a micro-raised array, can penetrate the cuticle to the epidermis and dermis, and which has the advantages of safety, painless, minimally invasive, self-administration and convenience. As a new kind of microneedles, hydrogel microneedles have attracted more attentions in the medical field due to its excellent performance. Hydrogel microneedles have good biocompatibility and mechanical properties, and can be completely removed after skin action without residual polymer in the body. Its unique swelling property can realize minimally invasive extraction of human detection substance and slow release of drugs, which can play a huge role in the field of personal health monitoring and drug controlled release in the future. The mechanism of action, design, preparation and application of hydrogel microneedles were reviewed in this paper, focusing on the current design parameters of hydrogel microneedles and their applications in drug delivery, extraction monitoring and wound healing, and the problems of hydrogel microneedles in skin infection risk, pharmacokinetics and wearing comfort were pointed out. In the future, the key research direction is to combine with intelligent devices to realize both human body monitoring and intelligent drug controlled release on the microneedle patch

    Identification and comparative analysis of vibration modal parameters of rice planter frame

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    Modal parameter analysis is a common method for structural characteristic analysis. To accurately identify the modal parameters that describe the vibration characteristics of the structure, the chassis frame of the rice transplanter is taken as the research object. The least squares complex frequency domain method, the admittance circle method, and the modal peak picking method are used to process the measured vibration signal. The modal parameters, such as the natural frequency, damping ratio, and modal shape of the chassis frame, are obtained. Except for the eight-order frequency error greater than 10%, the errors of all other orders are all less than 10%. In the modal test of the frame structure of the rice transplanter, the identification results of the modal parameters are generally reliable. The recognition accuracy of the PolyLeast-Squares Complex Frequency (PolyLSCF) algorithm is lower than the recognition accuracy of the admittance circle method and modal peak picking. The values of some off-diagonal elements in the modal mass matrix calculated by the PolyLSCF algorithm and the admittance circle method are greater than 0.2. The diagonal elements of the modal mass matrix calculated by the modal peak picking method are all 1

    Additional file 1 of Molecular mechanism of vimentin nuclear localization associated with the migration and invasion of daughter cells derived from polyploid giant cancer cells

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    Additional file 1: Table S1. Detail information of antibodies used in the paper. Table S2. SUMO1-siRNA interfering sequences. Table S3. SUMO2-siRNA interfering sequences. Table S4. SUMO3-siRNA interfering sequences. Table S5. P62-siRNA interfering sequences. Table S6. Vimentin-siRNA interfering sequences. Table S7. CDC42-siRNA interfering sequences. Table S8. CDC42 primer sequences

    Identification of risk factors for infection after mitral valve surgery through machine learning approaches

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    BackgroundSelecting features related to postoperative infection following cardiac surgery was highly valuable for effective intervention. We used machine learning methods to identify critical perioperative infection-related variables after mitral valve surgery and construct a prediction model.MethodsParticipants comprised 1223 patients who underwent cardiac valvular surgery at eight large centers in China. The ninety-one demographic and perioperative parameters were collected. Random forest (RF) and least absolute shrinkage and selection operator (LASSO) techniques were used to identify postoperative infection-related variables; the Venn diagram determined overlapping variables. The following ML methods: random forest (RF), extreme gradient boosting (XGBoost), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), AdaBoost, Naive Bayesian (NB), Logistic Regression (LogicR), Neural Networks (nnet) and artificial neural network (ANN) were developed to construct the models. We constructed receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) was calculated to evaluate model performance.ResultsWe identified 47 and 35 variables with RF and LASSO, respectively. Twenty-one overlapping variables were finally selected for model construction: age, weight, hospital stay, total red blood cell (RBC) and total fresh frozen plasma (FFP) transfusions, New York Heart Association (NYHA) class, preoperative creatinine, left ventricular ejection fraction (LVEF), RBC count, platelet (PLT) count, prothrombin time, intraoperative autologous blood, total output, total input, aortic cross-clamp (ACC) time, postoperative white blood cell (WBC) count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), PLT count, hemoglobin (Hb), and LVEF. The prediction models for infection after mitral valve surgery were established based on these variables, and they all showed excellent discrimination performance in the test set (AUC > 0.79).ConclusionsKey features selected by machine learning methods can accurately predict infection after mitral valve surgery, guiding physicians in taking appropriate preventive measures and diminishing the infection risk

    H<sub>2</sub>O<sub>2</sub> Solution Steaming Combined Method to Cellulose Skeleton for Transparent Wood Infiltrated with Cellulose Acetate

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    Hydrogen peroxide (H2O2) steaming, a green and highly efficient delignification method, has been demonstrated to provide a wood skeleton with a very low content of residual lignin in the manufacturing of transparent wood. It usually requires a long reaction time and a large amount of H2O2 because the piece of wood is treated using steaming equipment. Herein, a H2O2 solution steaming method was developed for the highly efficient removal of lignin from wood. Specifically, several wood samples were simultaneously immersed in a hot H2O2 solution to obtain delignified wood with a relatively high content of residual lignin, which provided a high strength and preserved the cellulose skeleton. Subsequently, the delignified wood with a relatively high content of residual lignin was further treated with H2O2 steam to obtain a very low lignin delignified wood. Compared with the previous H2O2 steaming method, the reaction time and used H2O2 volume of the H2O2 solution steaming method was reduced by 37.3% and 52.7%, respectively. All-biomass transparent wood could be obtained by infiltrating the delignified wood with cellulose acetate, which showed both a high transmittance of 83.0% and a low thermal conductivity of 0.30 Wm−1K−1

    Guidelines for the Diagnosis and Treatment of Primary Liver Cancer (2022 Edition)

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    Background: Primary liver cancer, around 75%–85% are hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people. Summary: Since the publication of Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China in June 2017, which were updated by the National Health Commission in December 2019, additional high-quality evidence has emerged from researchers worldwide regarding the diagnosis, staging, and treatment of liver cancer, that requires the guidelines to be updated again. The new edition (2022 Edition) was written by more than 100 experts in the field of liver cancer in China, which not only reflects the real-world situation in China, but also may re-shape the nationwide diagnosis and treatment of liver cancer. Key Messages: The new guideline aims to encourage the implementation of evidence-based practice, and improve the national average five-year survival rate for patients with liver cancer, as proposed in the "Health China 2030 Blueprint.

    A Physics‐Incorporated Deep Learning Framework for Parameterization of Atmospheric Radiative Transfer

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    Abstract The atmospheric radiative transfer calculations are among the most time‐consuming components of the numerical weather prediction (NWP) models. Deep learning (DL) models have recently been increasingly applied to accelerate radiative transfer modeling. Besides, a physical relationship exists between the output variables, including fluxes and heating rate profiles. Integration of such physical laws in DL models is crucial for the consistency and credibility of the DL‐based parameterizations. Therefore, we propose a physics‐incorporated framework for the radiative transfer DL model, in which the physical relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. It is also found that the prediction accuracy was improved with the physic‐incorporated layer. In addition, we trained and compared various types of DL model architectures, including fully connected (FC) neural networks (NNs), convolutional‐based NNs (CNNs), bidirectional recurrent‐based NNs (RNNs), transformer‐based NNs, and neural operator networks, respectively. The offline evaluation demonstrates that bidirectional RNNs, transformer‐based NNs, and neural operator networks significantly outperform the FC NNs and CNNs due to their capability of global perception. A global perspective of an entire atmospheric column is essential and suitable for radiative transfer modeling as the changes in atmospheric components of one layer/level have both local and global impacts on radiation along the entire vertical column. Furthermore, the bidirectional RNNs achieve the best performance as they can extract information from both upward and downward directions, similar to the radiative transfer processes in the atmosphere

    Quasi-Two-Dimensional Fermi Surface and Heavy Quasiparticles in CeRh2As2

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    The recent discovery of multiple superconducting phases in CeRh2As2 has attracted considerable interest. These rich phases are thought to be related to the locally noncentrosymmetric crystal structure, although the possible role of a quadrupole density wave preceding the superconductivity remains an open question. While measurements of physical properties imply that the Ce 4f electrons could play an essential role, the momentum-resolved electronic structure remains hitherto unreported, hindering an in-depth understanding of the underlying physics. Here, we report a high-resolution angle-resolved photoemission study of CeRh2As2. Our results reveal fine splittings of conduction bands, which are directly related to the locally noncentrosymmetric structure, as well as a quasi-two-dimensional Fermi surface, implying weak interlayer hopping and possible nesting instabilities. Our experiments also uncover the fine structures and pronounced temperature evolution of the Kondo peak, demonstrating strong Kondo effect facilitated by excited crystal electric field states. Our results unveil the salient electronic features arising from the interplay between the crystal structure and strong electron correlation, providing spectroscopic insight for understanding the heavy fermion physics and unconventional quadrupole density wave in this enigmatic compound
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