Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

Human Parathyroid Hormone (1–34) accelerates skin wound healing through inducing cell migration via up-regulating the expression of Rac1

Abstract Delayed wound healing is a public issue that imposes a significant burden on both society and the patients themselves. To date, although numerous methods have been developed to accelerate the speed of wound closure, the therapeutic effects are partially limited due to the complex procedures, high costs, potential side effects, and ethical concerns. While some studies have reported that the in-vivo application of Human Parathyroid Hormone (1–34) (hPTH(1–34)) promotes the wound-healing process, the definitive role and underlying mechanisms through which it regulates the behavior of fibroblasts and keratinocytes remains unclear. Herein, hPTH(1–34)’s role in cell migration is evaluated with a series of in-vitro and in-vivo studies, whereby hPTH(1–34)’s underlying mechanism in activating the two types of cells was detected. The in-vitro study revealed that hPTH(1–34) enhanced the migration of both fibroblasts and HaCaT cells. Ras-associated C3 botulinum toxin subunit 1 (Rac1), a classical member of the Rho family, was upregulated in hPTH(1–34)-treated fibroblasts and HaCaT cells. Further study by silencing the expression of Rac1 with siRNA reversed the hPTH(1–34)-enhanced cell migration, thus confirming that Rac1 was involved in hPTH(1–34)-induced cell behavior. In-vivo study on rat wound models confirmed the effects of hPTH(1–34) on fibroblasts and keratinocytes, with increased collagen deposition, fibroblasts accumulation, and Rac1 expression in the hPTH(1–34)-treated wounds. In summary, the present study demonstrated that hPTH(1–34) accelerated wound healing through enhancing the migration of cells through the up-regulation of Rac1 expression

FUR-DETR: A Lightweight Detection Model for Fixed-Wing UAV Recovery

Due to traditional recovery systems lacking visual perception, it is difficult to monitor UAVs’ real-time status in communication-constrained or GPS-denied environments. This leads to insufficient ability in decision-making and parameter adjustment and increase uncertainty and risk of recovery. Visual inspection technology can make up for the limitations of GPS and communication and improve the autonomy and adaptability of the system. However, the existing RT-DETR algorithm is limited by single-path feature extraction, a simplified fusion mechanism, and high-frequency information loss, which makes it difficult to balance detection accuracy and computational efficiency. Therefore, this paper proposes a lightweight visual detection model based on transformer architecture to further optimize computational efficiency. Firstly, aiming at the performance bottleneck of existing models, the Parallel Backbone is proposed, which captures local features and global semantic information by sharing the initial feature extraction module and the double-branch structure, respectively, and uses the progressive fusion mechanism to realize the adaptive integration of multiscale features so as to balance the accuracy and lightness of target detection. Secondly, an adaptive multiscale feature pyramid network (AMFPN) is designed, which effectively integrates different scales of information through multi-level feature fusion and information transmission mechanism, alleviates the problem of information loss in small-target detection, and improves the detection accuracy in complex backgrounds. Finally, a wavelet frequency–domain-optimized reverse feature fusion mechanism (WT-FORM) is proposed. By using the wavelet transform to decompose the shallow features into multi-frequency bands and combining the weighted calculation and feature compensation strategy, the computational complexity is reduced, and the representation ability of the global context is further enhanced. The experimental results show that the improved model reduces the parameter size and computational load by 43.2% and 58% while maintaining detection accuracy comparable to the original RT-DETR in three datasets. Even in complex environments with low light, occlusion, or small targets, it can provide more accurate detection results

Machine Learning Algorithms Identify Clinical Subtypes and Cancer in Anti-TIF1γ+ Myositis: A Longitudinal Study of 87 Patients

BackgroundAnti-TIF1γ antibodies are a class of myositis-specific antibodies (MSAs) and are closely associated with adult cancer-associated myositis (CAM). The heterogeneity in anti-TIF1γ+ myositis is poorly explored, and whether anti-TIF1γ+ patients will develop cancer or not is unknown at their first diagnosis. Here, we aimed to explore the subtypes of anti-TIF1γ+ myositis and construct machine learning classifiers to predict cancer in anti-TIF1γ+ patients based on clinical features.MethodsA cohort of 87 anti-TIF1γ+ patients were enrolled and followed up in Xiangya Hospital from June 2017 to June 2021. Sankey diagrams indicating temporal relationships between anti-TIF1γ+ myositis and cancer were plotted. Elastic net and random forest were used to select and rank the most important variables. Multidimensional scaling (MDS) plot and hierarchical cluster analysis were performed to identify subtypes of anti-TIF1γ+ myositis. The clinical characteristics were compared among subtypes of anti-TIF1γ+ patients. Machine learning classifiers were constructed to predict cancer in anti-TIF1γ+ myositis, the accuracy of which was evaluated by receiver operating characteristic (ROC) curves.ResultsForty-seven (54.0%) anti-TIF1γ+ patients had cancer, 78.7% of which were diagnosed within 0.5 years of the myositis diagnosis. Fourteen variables contributing most to distinguishing cancer and non-cancer were selected and used for the calculation of the similarities (proximities) of samples and the construction of machine learning classifiers. The top 10 were disease duration, percentage of lymphocytes (L%), percentage of neutrophils (N%), neutrophil-to-lymphocyte ratio (NLR), sex, C-reactive protein (CRP), shawl sign, arthritis/arthralgia, V-neck sign, and anti-PM-Scl75 antibodies. Anti-TIF1γ+ myositis patients can be clearly separated into three clinical subtypes, which correspond to patients with low, intermediate, and high cancer risk, respectively. Machine learning classifiers [random forest, support vector machines (SVM), extreme gradient boosting (XGBoost), elastic net, and decision tree] had good predictions for cancer in anti-TIF1γ+ myositis patients. In particular, the prediction accuracy of random forest was &amp;gt;90%, and decision tree highlighted disease duration, NLR, and CRP as critical clinical parameters for recognizing cancer patients.ConclusionAnti-TIF1γ+ myositis can be separated into three distinct subtypes with low, intermediate, and high risk of cancer. Machine learning classifiers constructed with clinical characteristics have favorable performance in predicting cancer in anti-TIF1γ+ myositis, which can help physicians in choosing appropriate cancer screening programs.</jats:sec

Growth of quasi-texture in nanostructured magnets with ultra-high coercivity

Controlling the phases and structure of nanostructured rare-earth-based permanent magnets is essential to develop magnets with high performance in clean energy technologies. Importantly, simultaneously controlling the texture and morphology of nano-grains can lead to both high coercivity and high maximum energy product in the nanostructured magnets. Here, we demonstrated the use of electron-beam exposure (EBE) as a rapid heating technique to control the crystallization of the Pr-Fe-B magnet. Due to the effect of the extremely high heating rate, EBE can be adapted to control the phase, grain size, and morphology of the amorphous precursor as well as the texture of Pr-Fe-B nano-grains. A quasi-texture nanostructure with optimal magnetic exchange interactions is thus achieved, leading to a record-high coercivity of 29.1 kOe at room temperature. We further applied the Landau model and Coble-Creep model to explain the positive impact of the EBE on the crystallization and microstructure of the Pr-Fe-B magnet. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved