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China is developing a new generation of geostationary meteorological satellites called Fengyun-4 (FY-4), which is planned for launch beginning in 2016. Following upon the current FY-2 satellite series, FY-4 will carry four new instruments: the Advanced Geosynchronous Radiation Imager (AGRI), the Geosynchronous Interferometric Infrared Sounder (GIIRS), the Lightning Mapping Imager (LMI), and the Space Environment Package (SEP). The first satellite of the FY-4 series launched on 11 December 2016 is experimental, and the following four or more satellites will be operational. The main objectives of the FY-4 series are to monitor rapidly changing weather systems and to improve warning and forecasting capabilities. The FY-4 measurements are aimed at accomplishing 1) high temporal and spatial resolution imaging in 14 spectral bands from the visible, near-infrared, and infrared (IR) spectral regions; 2) lightning imaging; and 3) high-spectral-resolution IR sounding observations over China and adjacent regions. FY-4 will also enhance the space weather monitoring and warning with SEP. Current products from FY-2 will be improved by FY-4, and a number of new products will also be introduced. FY-4’s sounding and imaging data will be used to improve applications in a wide range of ocean, land, and atmosphere monitoring plus forecasting extreme weather (especially typhoons and thunderstorms); overall, FY-4 will contribute to more accurate understanding and forecasting of China’s weather, climate, environment, and natural disasters. This new generation of Chinese geostationary weather satellites is being developed in parallel with the new generation of geostationary meteorological satellite systems from the international community of satellite providers and is intended to be an important contribution to the global observing system.

Marine ships are the transport vehicle in the ocean and instance segmentation of marine ships is an accurate and efficient analysis approach to achieve a quantitative understanding of marine ships, for example, their relative locations to other ships or obstacles. This relative spatial information is crucial for developing unmanned ships to avoid crashing. Visible light imaging, e.g. using our smartphones, is an efficient way to obtain images of marine ships, however, so far there is a lack of suitable open-source visible light datasets of marine ships, which could potentially slow down the development of unmanned ships. To address the problem of insufficient datasets, here we built two instance segmentation visible light datasets of marine ships, MariBoats and MariBoatsSubclass, which could facilitate the current research on instance segmentation of marine ships. Moreover, we applied several existing instance segmentation algorithms based on neural networks to analyze our datasets, but their performances were not satisfactory. To improve the segmentation performance of the existing models on our datasets, we proposed a global and local attention mechanism for neural network models to retain both the global location and semantic information of marine ships, resulting in an average segmentation improvement by 4.3% in terms of mean average precision. Therefore, the presented new datasets and the new attention mechanism will greatly advance the marine ship relevant research and applications.

Objective To assess the effectiveness and safety of enzyme replacement therapy (ERT) for treating Fabry disease in clinical practice. Methods The clinical data of patients with Fabry disease were retrospectively collected and screened according to inclusion and exclusion criteria. The effectiveness of ERT was evaluated by analyzing the improvement in renal dysfunction (decreased estimated glomerular filtration rate (eGFR) and proteinuria), cardiac system injury (mainly increased left ventricular mass index (LVMI)), and neuropathic pain after ERT treatment. The safety of ERT was measured by summarizing the occurrence of adverse events (AE) and adverse drug reactions (ADR) before and after ERT. Results Sixteen patients with Fabry disease who underwent ERT treatment 2–36 times over a period of 2–89 weeks were enrolled in the study. Among them, 13 received symptomatic treatment based on the involvement of various organs, 14 were treated with anti-inflammatory and anti-allergic drugs, and 16 had no AE or ADR. After ERT, there was no significant difference in (eGFR, microalbumin (mALB), 24 h urinary protein quantitation (24 h PRO), urinary albumin/creatinine ratio (ACR), uric acid (UA), and β2 microglobulin (β2MG) ( P  > 0.05), and the renal function remained stable or improved; ERT could significantly reduce left ventricular mass index (LVMI) ( P  = 0.043) and lactate dehydrogenase (LDH) ( P  = 0.031), and other cardiac function indexes had an improvement trend or remained stable, but the difference was not significant ( P  > 0.05). After ERT, the degree of limb pain in three of the four minor patients improved. Conclusions ERT could effectively stabilize or improve renal and cardiac function and relieve neuropathic pain in patients with Fabry disease, and no AE occurred during treatment, and the clinical effectiveness and safety were satisfactory.

In the remote sensing field, synthetic aperture radar (SAR) is a type of active microwave imaging sensor working in all-weather and all-day conditions, providing high-resolution SAR images of objects such as marine ships. Detection and instance segmentation of marine ships in SAR images has become an important question in remote sensing, but current deep learning models cannot accurately quantify marine ships because of the multi-scale property of marine ships in SAR images. In this paper, we propose a multi-scale feature pyramid network (MS-FPN) to achieve the simultaneous detection and instance segmentation of marine ships in SAR images. The proposed MS-FPN model uses a pyramid structure, and it is mainly composed of two proposed modules, namely the atrous convolutional pyramid (ACP) module and the multi-scale attention mechanism (MSAM) module. The ACP module is designed to extract both the shallow and deep feature maps, and these multi-scale feature maps are crucial for the description of multi-scale marine ships, especially the small ones. The MSAM module is designed to adaptively learn and select important feature maps obtained from different scales, leading to improved detection and segmentation accuracy. Quantitative comparison of the proposed MS-FPN model with several classical and recently developed deep learning models, using the high-resolution SAR images dataset (HRSID) that contains multi-scale marine ship SAR images, demonstrated the superior performance of MS-FPN over other models.

Perfluorooctane sulfonic acid (PFOS), a persistent organic pollutant, significantly disrupts thyroid function. This study presented an integrated computational approach, combining network toxicology, molecular docking, and molecular dynamics simulations to systematically elucidate the molecular mechanisms underlying PFOS induced thyroid toxicity. Through integrated analysis of the Comparative Toxicogenomics Database (CTD), GeneCards, and Online Mendelian Inheritance in Man (OMIM) databases, we identified 205 potential thyroid toxicity-related targets. Protein-protein interaction network analysis revealed 34 hub targets, with TP53, JUN, ESR1, AKT1, and CTNNB1 emerging as central nodes in the toxicity network. Functional enrichment analysis demonstrated significant enrichment in the PPAR signaling pathway, fatty acid metabolism, AGE-RAGE pathway, and AMPK pathway, indicating that PFOS influences thyroid function through multiple signaling pathways. Molecular docking studies showed that PFOS forms stable complexes with core target proteins, with binding energies ranging from − 4.9 to -9.7 kcal/mol. Molecular dynamics simulations further validated the structural stability of these complexes, with PFOS-AKT1 and PFOS-TP53 exhibiting the highest conformational stability. This study revealed the multi-target and multi-pathway characteristics of PFOS-induced thyroid toxicity, providing novel insights into its toxicological mechanisms.

Currently, teleoperated robots, with the operator’s input, can fully perceive unknown factors in a complex environment and have strong environmental interaction and perception abilities. However, physiological tremors in the human hand can seriously affect the accuracy of processes that require high-precision control. Therefore, this paper proposes an EEMD-IWOA-LSTM model, which can decompose the physiological tremor of the hand into several intrinsic modal components (IMF) by using the EEMD decomposition strategy and convert the complex nonlinear and non-stationary physiological tremor curve of the human hand into multiple simple sequences. An LSTM neural network is used to build a prediction model for each (IMF) component, and an IWOA is proposed to optimize the model, thereby improving the prediction accuracy of the physiological tremor and eliminating it. At the same time, the prediction results of this model are compared with those of different models, and the results of EEMD-IWOA-LSTM presented in this study show obvious superior performance. In the two examples, the MSE of the prediction model proposed are 0.1148 and 0.00623, respectively. The defibrillation model proposed in this study can effectively eliminate the physiological tremor of the human hand during teleoperation and improve the control accuracy of the robot during teleoperation.

Environmental intelligent perception technology is among the key technologies for intelligent mobile robots. This paper proposes an environment intelligent perception scheme based on depth cameras to address mobile robot environments intelligent perception challenges. Firstly, the system initiates with direct filtering and point cloud gridding filtering to eliminate noise interference. Secondly, we employ Euclidean clustering extraction to identify non-ground objects and convex hull method to get their location and size. Finally, for 3D scene reconstruction, utilizing Scale-Invariant Feature Transform (SIFT) operator to extract features and match adjacent intensity images to find the corresponding 3D point set. The Iterative Closest Point (ICP) Algorithm is used to compute camera motion matrices to alignment adjacent point clouds.

Ciprofol is a novel short-acting intravenous anaesthetic developed in China that is mainly metabolized by cytochrome P450 2B6 (CYP2B6) and uridine diphosphate glucuronosyltransferase 1A9 (UGT1A9). Currently, insufficient evidence is available to support drug‒drug interactions between ciprofol and CYP2B6 inactivators. Here, we established a high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method to assess the concentration of ciprofol and investigated the effects of psoralen and clopidogrel on the metabolism of ciprofol in liver microsomes and rats. In rat and human liver microsomes, the median inhibitory concentration ( IC 50 ) values of psoralen were 63.31 μmol·L -1 and 34.05 μmol·L -1 , respectively, showing mild inhibitory effects on ciprofol metabolism, whereas the IC 50 values of clopidogrel were 6.380 μmol·L -1 and 2.565 μmol·L -1 , respectively, with moderate inhibitory effects. SD rats were randomly divided into three groups: psoralen (27 mg·kg -1 ), clopidogrel (7.5 mg·kg -1 ), and the same volume of 0.5% carboxy methyl cellulose. After 7 days, all rats were injected with 2.4 mg·kg -1 ciprofol. Compared with the control group, the AUC and MRT values of ciprofol in the psoralen and clopidogrel groups were significantly greater, whereas the CL values were significantly lower. In addition, the durations of loss of righting reflex (LORR) in the psoralen and clopidogrel groups were 16.1% and 23.0% longer than that in the control group, respectively. In conclusion, psoralen and clopidogrel inhibit ciprofol metabolism to different degrees and prolong the duration of LORR in rats.

An update of the hadronic vacuum polarisation contributions to the muon magnetic anomaly and to the running of the electromagnetic couplings constant at the Z -boson mass is presented. Newest e + e − → hadrons cross-section data mainly from the BABAR and VEPP-2000 experiments have been included. For the muon ( g −2)/2, the lowest-order hadronic contribution is evaluated to be (693.1 ± 3.4) · 10 −10 , improving the precision of our previous evaluation by 21%. The full Standard Model prediction differs by 3.5 σ from the experimental value. The five-quark hadronic contribution to$ \\alpha (\\mathop m\\nolimits_z^2 ) $is evaluated to be (276.0 ± 0.9) · 10 −4 .

Black Carbon (BC), the second-largest contributor to global warming, has detrimental effects on human health and the environment. However, the accurate quantification of BC poses a significant challenge, impeding the comprehensive assessment of its impacts. Therefore, this paper aims to critically review three quantitative methods for measuring BC: Thermal Optical Analysis (TOA), the Optical Method, and Laser-Induced Incandescence (LII). The determination principles, available commercial instruments, sources of deviation, and correction approaches associated with these techniques are systematically discussed. By synthesizing and comparing the quantitative results reported in previous studies, this paper aims to elucidate the underlying relationships and fundamental disparities among Elemental Carbon (EC), Equivalent Black Carbon (eBC), and Refractory Black Carbon (rBC). Finally, based on the current advancements in BC quantification, recommendations are proposed to guide future research directions.