Rheumatoid arthritis (RA), an autoimmune disease, is characterized by inflammatory cell infiltration that damages cartilage, disrupts bone, and impairs joint function. The therapeutic efficacy of RA ...treatments with the severely affected side remains unsatisfactory despite current treatment methods that primarily focus on anti-inflammatory activity, largely because of the complicatedly pathological mechanisms. A recently identified mechanism for RA development involves the interaction of RA autoantibodies with various proinflammatory cytokines to facilitate the formation of neutrophil extracellular traps (NETs), which increased inflammatory responses to express inflammatory cytokines and chemokines. Therefore, NETs architecture digestion may inhibit the positive-feedback inflammatory signal pathway and lessen joint damage in RA. In this work, deoxyribonuclease I (DNase) is connected to oxidized hyaluronic acid (OHA) via Schiff base reaction to extend the half-life of DNase. The modification does not influence the DNase activity for plasmid deoxyribonucleic acid hydrolysis and NETs' architecture disruption. Carboxymethyl chitosan is crosslinked with DNase-functionalised OHA (DHA) to form an injectable, degradable, and biocompatible hydrogel (DHY) to further strengthen the adhesive capability of DHA. Importantly, the collagen-induced arthritis model demonstrates that intra-articular injection of DHY can significantly reduce inflammatory cytokine expression and alleviate RA symptoms, which can be significantly improved by combining methotrexate. Here, a DNase-functionalised hydrogel has been developed for RA treatment by constantly degrading the novel drug target of NETs to decrease inflammatory response in RA.
Atmospheric motion vectors, which can be used to infer wind speed and direction based on the trajectory of cloud movement, are instrumental in enhancing atmospheric wind-field insights, contributing ...notably to wind-field optimization and forecasting. However, a widespread problem with vector data is their inaccuracy, which, when coupled with the mediocre effectiveness of existing correction methods, limits their practical utility in forecasting, often falling short of expectations. Deep-learning techniques are used to refine atmospheric motion vector data from the FY-4A satellite, notably enhancing data quality. Post-training data undergoes a thorough analysis using a quality evaluation function, followed by its integration into a numerical weather prediction system in order to conduct forecasting experiments. Results indicate a marked improvement in data quality post-error correction by the model, characterized by a significant reduction in root mean square error and a notable increase in correlation coefficients. Furthermore, refined data demonstrate a considerable enhancement in the accuracy of meteorological element forecasts, particularly for Asian and Western Pacific regions.
The prediction of chaotic time series systems has remained a challenging problem in recent decades. A hybrid method using Hankel Alternative View Of Koopman (HAVOK) analysis and machine learning ...(HAVOK-ML) is developed to predict chaotic time series. HAVOK-ML simulates the time series by reconstructing a closed linear model so as to achieve the purpose of prediction. It decomposes chaotic dynamics into intermittently forced linear systems by HAVOK analysis and estimates the external intermittently forcing term using machine learning. The prediction performance evaluations confirm that the proposed method has superior forecasting skills compared with existing prediction methods.
As a category-3 typhoon, Hato (2017) experienced the notable rapid intensification (RI) over the hot sea surface before its landfall. The RI process and the influences of local sea surface ...temperature (SST) patterns on the evolution of Hato were well captured and carefully investigated using a high-resolution air–sea coupled model. To further explore the close relationship between the radial distributions of SST and storm evolution, a sensitive experiment with time-fixed SST was also performed. Results showed that the time-fixed SST experiment produced earlier RI following the rapid core structure adjustment, as higher SST in the core region was found favorable to increasing the near-surface water vapor and latent heat flux. Strong updrafts were thus facilitated inside the eyewall, inducing the eyewall contraction and RI of the storm. In contrast, cooler SST inside the core region should account for the delay of RI as the intense convection located in the outer rainbands, inhibiting the transportation of energy into the inner-core. Momentum tendency analysis also proves these mechanisms. Therefore, not only the value of SST but also its radial-gradient, plays an important role in the evolution of tropical cyclones, highlighting the need for an advanced air–sea coupled model.
A cascaded symmetrical dual-taper Mach-Zehnder interferometer structure based on guided-mode and leaky-mode interference is proposed in this paper. Firstly, the interference spectrum characteristics ...of interferometer has been analyzed by the Finite Difference-Beam Propagation Method (FD-BPM). When the diameter of taper waist is 20 μm-30 μm, dual-taper length is 1 mm and taper distance is 4 cm-6 cm, the spectral contrast is higher, which is suitable for sensing. Secondly, experimental research on refractive index sensitivity is carried out. A refractive index sensitivity of 62.78 nm/RIU (refractive index unit) can achieved in the RI range of 1.3333-1.3792 (0%~25% NaCl solution), when the sensor structure parameters meet the following conditions: diameter of taper waist is 24 μm, dual-taper length is 837 μm and taper distance is 5.5 cm. The spectrum contrast is 0.8 and measurement resolution is 1.6 × 10(-5) RIU. The simulation analysis is highly consistent with experimental results. Research shows that the sensor has promising application in low RI fields where high-precision measurement is required due to its high sensitivity and stability.
In the earth sciences, numerical weather prediction (NWP) is the primary method of predicting future weather conditions, and its accuracy is affected by the initial conditions. Data assimilation (DA) ...can provide high-precision initial conditions for NWP. The hybrid 4DVar-EnKF is currently an advanced DA method used by many operational NWP centres. However, it has two major shortcomings: The complex development and maintenance of the tangent linear and adjoint models and the empirical combination of the results of 4DVar and EnKF. In this paper, a new hybrid DA method based on machine learning (HDA-ML) is presented to overcome these drawbacks. In the new method, the tangent linear and adjoint models in the 4DVar part of the hybrid algorithm can be easily obtained by using a bilinear neural network to replace the forecast model, and a CNN model is adopted to fuse the analysis of 4DVar and EnKF to adaptively obtain the optimal coefficient of combination rather than the empirical coefficient as in the traditional hybrid DA method. The hybrid DA methods are compared with the Lorenz-96 model using the true values as labels. The experimental results show that HDA-ML improves the assimilation performance and significantly reduces the time cost. Furthermore, using observations instead of the true values as labels in the training system is more realistic. The results show comparable assimilation performance to that in the experiments with the true values used as the labels. The experimental results show that the new method has great potential for application to operational NWP systems.
Precise center-detection of tropical cyclones (TCs) is critical for dynamic analysis in high resolution model data. The existence of both smaller scale perturbations and larger scale circulations ...could reduce the accuracy of center positioning. In this study, an objective center-finding algorithm is developed based on a two-dimensional Fourier filter and a vorticity centroid algorithm. This proposed algorithm is able to automatically adjust its parameters according to the scale of the target vortex instead of using artificially prescribed parameters in previous research. What’s more, this new algorithm has been optimized and validated by a hundred idealized vortexes with different sizes and small-scale perturbations. A high-resolution simulation of Typhoon Soudelor (2015) was used to evaluate the performance of the new algorithm, and the proposed objective center-finding algorithm was found able to detect a precise and reliable center.
Variations in both symmetric wind components and asymmetric wave amplitudes of a tropical cyclone depend on the location of its center. Because the radial structure of asymmetries is critical to the ...wave–mean interaction, this study, under idealized conditions, examines the influences of a center location on the radial structure of the diagnosed asymmetries. It has been found that the amplitudes of aliasing asymmetries are mainly affected by the initial symmetric fields. Meanwhile, the radial structure of asymmetry is controlled by the aliasing direction. Sensitivity tests on the location of the center were employed to emphasize the importance of the aliasing direction using angular momentum equations. With a small displacement, the tendencies of azimuthal tangential wind are found to reverse completely when the center shifts to a different direction. This work concludes that the diagnostic results related to asymmetric decomposition should be treated rigorously, as they are prone to inaccuracies, which in turn affect cyclone prediction.
Tropical cyclone (TC) size is an important parameter for estimating TC risks such as wind damage, rainfall distribution, and storm surge. Satellite observation data are the primary data used to ...estimate TC size. Traditional methods of TC size estimation rely on
a priori
knowledge of the meteorological domain and emerging deep learning-based methods do not consider the considerable blurring and background noise in TC cloud systems and the application of multisource observation data. In this paper, we propose TC-Resnet, a deep learning-based model that estimates 34-kt wind radii (R34, commonly used as a measure of TC size) objectively by combining infrared and microwave satellite data. We regarded the resnet-50 model as the basic framework and embedded a convolution layer with a 5 × 5 convolution kernel on the shortcut branch in its residual block for downsampling to avoid the information loss problem of the original model. We also introduced a combined channel-spatial dual attention mechanism to suppress the background noise of TC cloud systems. In an R34 estimation experiment based on a global TC dataset containing 2003–2017 data, TC-Resnet outperformed existing methods of TC size estimation, obtaining a mean absolute error of 11.287 nmi and a Pearson correlation coefficient of 0.907.
Introduction
Severe typhoons, as extreme weather events, can cause a large number of casualties and property damage in coastal areas. There are mainly three kinds of methods for the prediction of ...severe typhoon formation, which are the numerical-based methods, the statistical-based methods, and the machine learning-based methods. However, existing methods do not consider the unbalance between the number of ordinary typhoon samples and severe typhoon samples, which makes the accuracies of existing methods in the prediction of severe typhoons much lower than that of ordinary typhoons.
Methods
In this paper, we propose an unbalanced severe typhoon formation prediction (USFP) framework based on transfer learning. We first propose a severe typhoon pre-learning model which is used to learn prior knowledge from a constructed balanced dataset. Then, we propose an unbalanced severe typhoon re-learning model which utilizes the prior knowledge learning from the pre-learning model. Our USFP framework fuses three different variables, which are atmospheric variables, sea surface variables, and ocean hydrographic variables.
Results
Extensive experiments based on datasets of three different regions show that our USFP framework outperforms the numerical model IFS of ECMWF and existing machine learning methods.
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