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Differential code bias (DCB) of satellite is an error that cannot be ignored in precise positioning, timing, ionospheric modeling, satellite clock correction, and ambiguity resolution. The completion of the third generation of BeiDou Navigation Satellite System (BDS-3) has extended DCB to multichannel code bias observations and observable-specific signal bias (OSB). In this paper, the DCB and OSB products provided by the Chinese Academy of Sciences (CAS) are analyzed and compared. The DCB parameters for the BDS satellites are applied in both single- and dual-frequency single point positioning (SPP), and the results are intensively investigated. Based on the satellite DCB parameters of the BDS, the performance of precise point positioning (PPP) with different frequency combinations is also analyzed in terms of positioning accuracy and convergence time. The standard deviations (STDs) of DCBs at each signal pair fluctuate from 0.2 ns to 1.5 ns. The DCBs of BDS-2 are slightly more stable than those of BDS-3. The mean values and STDs of C2I and C7I OSBs for BDS-2 are at the same level and are numerically smaller than their counterparts for the C6I OSBs. The mean OSBs for each signal of the BDS-3, excluding C2I, fluctuate between 12.35 ns and 12.94 ns, and the STD fluctuates between 2.11 ns and 3.10 ns. The DCBs and OSBs of the BDS-3 of the IGSO satellites are more stable than those of the MEO satellites. The corrections for TGD and DCB are able to improve the accuracy of single-frequency SPP by 44.09% and 44.07%, respectively, and improve the accuracy of dual-frequency SPP by 6.44% and 12.85%, respectively. The most significant improvements from DCB correction are seen in single-frequency positioning with B1I and dual-frequency positioning with B2a+B3I. DCB correction can improve the horizontal and three-dimensional positioning accuracy of the dual-frequency PPP of different ionosphere-free combinations by 13.53% and 13.84% on average, respectively, although the convergence is slowed. [ABSTRACT FROM AUTHOR]

Functional principal component analysis (FPCA) has been widely used to capture major modes of variation and reduce dimensions in functional data analysis. However, standard FPCA based on the sample covariance estimator does not work well if the data exhibits heavy‐tailedness or outliers. To address this challenge, a new robust FPCA approach based on a functional pairwise spatial sign (PASS) operator, termed PASS FPCA, is introduced. We propose robust estimation procedures for eigenfunctions and eigenvalues. Theoretical properties of the PASS operator are established, showing that it adopts the same eigenfunctions as the standard covariance operator and also allows recovering ratios between eigenvalues. We also extend the proposed procedure to handle functional data measured with noise. Compared to existing robust FPCA approaches, the proposed PASS FPCA requires weaker distributional assumptions to conserve the eigenspace of the covariance function. Specifically, existing work are often built upon a class of functional elliptical distributions, which requires inherently symmetry. In contrast, we introduce a class of distributions called the weakly functional coordinate symmetry (weakly FCS), which allows for severe asymmetry and is much more flexible than the functional elliptical distribution family. The robustness of the PASS FPCA is demonstrated via extensive simulation studies, especially its advantages in scenarios with nonelliptical distributions. The proposed method was motivated by and applied to analysis of accelerometry data from the Objective Physical Activity and Cardiovascular Health Study, a large‐scale epidemiological study to investigate the relationship between objectively measured physical activity and cardiovascular health among older women. [ABSTRACT FROM AUTHOR]

In an effort to enhance the precision of wind power prediction, this study proposes a wind power prediction model with a secondary-weighted attention mechanism, which is based on feature selection. During the pre-processing stage, wavelet denoising is employed on the original dataset to eliminate noise and enhance the convergence rate of the model. As for model improvement, a secondary-weighted time attention mechanism-LSTM (STAM-LSTM) model is proposed. Additionally, the random forest algorithm is employed to analyse the feature correlation, leading to the best feature combination for the construction of the final input vector. In the comparative experiments, the STAM-LSTM model shows good performance and stability compared to the other nine models and three reference methods. In addition, to validate the effectiveness of feature selection, different combinations of features are entered for prediction. The results show that the model metrics reach a better level after feature selection. Finally, the effects of the STAM mechanism and the Random Forest feature screening assistance strategy, on the model are analysed through ablation experiments. [ABSTRACT FROM AUTHOR]

Deep learning has substantially pushed forward remote sensing image change detection through extracting discriminative hierarchical features. However, as the increasingly high-resolution remote sensing images have abundant spatial details but limited spectral information, the use of conventional backbone networks would give rise to blurry boundaries between different semantics among hierarchical features. This explains why most false alarms in the final predictions distribute around change boundaries. To alleviate the problem, we pay attention to feature refinement and propose deep learning networks that deliver improved separability (ISNet). Our ISNet reaps the advantages from two strategies applied to refining bitemporal feature hierarchies: 1) margin maximization that clarifies the gap between changed and unchanged semantics and 2) targeted arrangement of attention mechanisms that direct the use of channel attention (CA) and spatial attention (SA) for highlighting semantic and positional information, respectively. Specifically, we insert CA modules into share-weighted backbone networks to facilitate semantic-specific feature extraction. The semantic boundaries in the extracted bitemporal hierarchical features are then clarified by margin maximization modules, followed by SA modules to enhance positional change responses. A top–down fusion pathway makes the final refined features cover multiscale representations and have strong separability for remote sensing image change detection. Extensive experimental evaluations demonstrate that our ISNet achieves state-of-the-art performance on the LEVIR-CD, SYSU-CD, and Season-Varying datasets in terms of overall accuracy (OA), Intersection-of-Union (IoU), and F1 score. Code is available at https://github.com/xingronaldo/ISNet. [ABSTRACT FROM AUTHOR]

Background: Alopecia has become an exceedingly prevalent dermatological disorder. Etiologically, infection (bacterial and fungal infection), inflammation, and immune dysregulation are the main causes of immune-mediated hair loss. Treating hair loss has remained challenging as the available therapies are limited. Exosomes from adipose-derived stem cells (ADSC-Exos) have been used for treating neurodegenerative diseases and autoimmune diseases and in wound-healing treatments. However, the function and mechanism of ADSC-Exos in alopecia treatment remain unclear. This study is aimed at investigating the effects of ADSC-Exos on hair growth in vitro and in vivo for potentially treating immune-mediated alopecia and further exploring the underlying mechanism.Methods: Cell proliferation, migration, and apoptosis of dermal papilla cells (DPCs) that were treated with ADSC-Exos were detected using the cell counting kit-8 (CCK-8) assay, scratch wound-healing assay, and flow cytometry assay, respectively. A C57BL/6 hair-depilated mouse model was established in vivo; then, ADSC-Exos were subcutaneously injected alone or in combined with minoxidil. The effects of ADSC-Exos on hair growth, pathological changes, and the related mechanism were investigated by HE staining, quantitative real-time PCR (qRT-PCR), western blotting, and RNA sequencing (RNA-seq).Results: ADSC-Exos significantly promoted DPC proliferation and migration while also reducing apoptosis. In addition, compared with the control group, ADSC-Exos-treated mice had better hair growth, more hair follicles (HFs) and thicker dermis. RNA-seq revealed that the miR-22 and TNF-α signaling pathways were markedly downregulated in DPCs after ADSC-Exos treatment. In addition, according to qRT-PCR and western blotting results, the Wnt/β-catenin signaling pathway was activated in the skin of ADSC-Exos-treated mice.Conclusion: ADSC-Exos therapy positively affected the promotion of hair regrowth by regulating miR-22, the Wnt/β-catenin signaling pathway, and the TNF-α signaling pathway, implying that ADSC-Exos could be a promising cell-free therapeutic strategy for immune-mediated alopecia. [ABSTRACT FROM AUTHOR]

Gastric cancer, one of the most common malignant tumors that can affect the digestive system, poses a serious threat to human life. The survival rate of gastric cancer patients depends on early detection and treatment. The widespread adoption of endoscopy has improved the detection rate of early gastric cancer. Accurate preoperative diagnosis of early gastric cancer is key to developing individualized treatment strategies. Here, nonlinear optical microscopy (NLOM) is used to differentiate between normal gastric mucosae and those with early gastric cancer. Furthermore, the quantitative relationship between submucosal infiltration depth and collagen signals in early gastric cancer is explored. First, the two-dimensional collagen direction angle was measured as an indicator to identify cancerous tissue. The orientation indexes of collagen fibers in normal and cancerous tissues were found to be 0.8511 ± 0.0839 and 0.6466 ± 0.07429 (P < 0.0001), respectively, indicating a significant decrease for the cancerous site. The backscattered second harmonic generation (SHG) signal corresponding to the collagen content and the three-dimensional collagen fiber orientation were then studied for early gastric cancer at different infiltration depths. The backscattered collagen SHG signal (a.u.) in the infiltrated lamina propria, muscularis mucosa, and submucosa were found to be 0.1850 ± 0.0393, 0.0870 ± 0.0189, and 0.0435 ± 0.0163, respectively. The 3D directional variance of collagen corresponding to the three infiltration depth sites were 0.6108 ± 0.0707, 0.6794 ± 0.0610, and 0.8200 ± 0.0618 (P < 0.05), respectively. Significant differences between the early gastric cancer collagen signals at different infiltration depths were observed. Our results indicate that NLOM can differentiate cancerous tissue from normal tissue, and thus diagnose early gastric cancer based on infiltration depth. NLOM provides a new evaluation method for the real-time in situ diagnosis of early gastric cancer and has important clinical significance for preparing accurate individualized treatment guidance. [ABSTRACT FROM AUTHOR]

Tumor angiogenesis is a complex process that is unamenable to intravital whole‐process monitoring, especially on microscopic assessment of tumor microvessel and quantifying microvascular hemodynamics before and after the nanotherapeutics, which hinder the understanding of nanotheranostics outcomes in tumor treatment. Herein, a new photoacoustic (PA) imaging‐optical coherence tomography angiography (OCTA)‐laser speckle (LS) multimodal imaging strategy is first proposed, which is not only able to precisely macro guide the thermo‐chemotherapy of tumor by monitoring blood oxygen saturation (SaO2) and hemoglobin content (HbT), but also capable of long‐term microscopic investigating the microvessel morphology (microvascular density) and hemodynamics changes (relative blood flow) before and after the nanotherapeutics in vivo. Moreover, to realize the tumor thermo‐chemotherapy treatment based on this novel multimodal imaging strategy, a 2D 5‐fluorouracil silicon nanosheets (5‐Fu‐Si NSs) therapeutic agent is designed. Furthermore, 2D high‐resolution tumor microvascular images in different stage display that tendency of the thermo‐chemotherapy effect is closely associated with tumor angiogenesis. Taken together, the investigations establish the fundamental base in theory and technology for further tailoring the novel specific diagnosis and treatment strategy in tumor. More importantly, this technique will be beneficial to evaluate the tumor microvascular response to nanotherapeutics at microscale. [ABSTRACT FROM AUTHOR]

Tumor angiogenesis is a complex process that is unamenable to intravital whole‐process monitoring, especially on microscopic assessment of tumor microvessel and quantifying microvascular hemodynamics before and after the nanotherapeutics, which hinder the understanding of nanotheranostics outcomes in tumor treatment. Herein, a new photoacoustic (PA) imaging‐optical coherence tomography angiography (OCTA)‐laser speckle (LS) multimodal imaging strategy is first proposed, which is not only able to precisely macro guide the thermo‐chemotherapy of tumor by monitoring blood oxygen saturation (SaO2) and hemoglobin content (HbT), but also capable of long‐term microscopic investigating the microvessel morphology (microvascular density) and hemodynamics changes (relative blood flow) before and after the nanotherapeutics in vivo. Moreover, to realize the tumor thermo‐chemotherapy treatment based on this novel multimodal imaging strategy, a 2D 5‐fluorouracil silicon nanosheets (5‐Fu‐Si NSs) therapeutic agent is designed. Furthermore, 2D high‐resolution tumor microvascular images in different stage display that tendency of the thermo‐chemotherapy effect is closely associated with tumor angiogenesis. Taken together, the investigations establish the fundamental base in theory and technology for further tailoring the novel specific diagnosis and treatment strategy in tumor. More importantly, this technique will be beneficial to evaluate the tumor microvascular response to nanotherapeutics at microscale. [ABSTRACT FROM AUTHOR]

Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions. Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning. Multiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT). The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively. The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The objects in remote sensing images are normally densely packed, arbitrarily oriented, and surrounded by complex backgrounds. Great efforts have been devoted to developing oriented object detection models to accommodate such data characteristics. We argue that an effective detection model hinges on three aspects: feature enhancement, feature decoupling for classification and localization, and an appropriate bounding box regression scheme. In this article, we instantiate the three aspects on top of the classical Faster R-CNN, with three novel components proposed. First, we propose a weighted fusion and refinement (WFR) module, which adaptively weighs multi-level features and leverages the attention mechanism to refine the fused features. Second, we decouple the RoI (region of interest) features for the subsequent classification and localization via a lightweight affine transformation-based feature decoupling (ATFD) module. Third, we propose a post-classification regression (PCR) module for generating the desired quadrilateral bounding boxes. Specifically, PCR predicts the precise vertex location on each side of a predicted horizontal box, by simply learning the following: (i) classify the discretized regression range of the vertex, and (ii) revise the vertex location with an offset. We conduct extensive experiments on the DOTA, DIOR-R, and HRSC2016 datasets to evaluate our method. [ABSTRACT FROM AUTHOR]

The broadcast ionospheric model is mainly used to correct the ionospheric delay error for single-frequency users. Since the BeiDou global ionospheric delay correction model (BDGIM) is a novel broadcast ionospheric model for BDS-3, its performance was analyzed through single point positioning (SPP) in this study. Twenty-two stations simultaneously receiving B1C, B2a, B1I and B3I signals were selected from the International GNSS Service (IGS) and the International GNSS Monitoring and Assessment System (iGMAS) tracking networks for the SPP experiments. The differential code bias (DCB) parameters were used to correct the hardware delays in the signals of B1C and B2a. The results showed that the BDGIM performs the best in high-latitude areas, and can effectively improve the positioning accuracy compared with the Klobuchar model. The average 3D positioning accuracy of the four civil signals can reach 3.58 m in high-latitude areas. The positioning accuracies with the BDGIM in the northern hemisphere are better than those in the southern hemisphere, and the global average 3D positioning accuracy of the four civil signals is 4.60 m. The performance of the BDGIM also shows some seasonal differences. The BDGIM performs better than the Klobuchar model on the days of spring equinox and winter solstice, while the opposite is true on the days of summer solstice and autumn equinox. On the day of winter solstice, the average 3D accuracies with the BDGIM on the signals of B1C, B2a, B1I and B3I are 4.13 m, 5.32 m, 4.40 m and 4.49 m, respectively. Although the SPP accuracies are to some extent affected by the geomagnetic storm, the BDGIM generally performs better and are more resistant to the geomagnetic storm than the Klobuchar model. [ABSTRACT FROM AUTHOR]

B Thermo-Chemotherapy b A promising biocompatible nanotheranostics agent based on chemotherapeutic drug 5-fluorouracil and 2D silicon nanosheets is developed for synergistic thermo-chemotherapy strategy. The tumor tissue functional parameters, including SaO SB 2 sb , HbT, micro microvascular morphology, and hemodynamics, are dynamically monitored by multimodal PA/OCTA/LS imaging strategy to accurately evaluate overall therapeutic effects. Intravital Whole-Process Monitoring Thermo-Chemotherapy Via 2D Silicon Nanoplatform: A Macro Guidance and Long-Term Microscopic Precise Imaging Strategy (Adv. Sci. 16/2021). [Extracted from the article]

Intravital Whole-Process Monitoring Thermo-Chemotherapy Via 2D Silicon Nanoplatform: A Macro Guidance and Long-Term Microscopic Precise Imaging Strategy (Adv. Sci. 16/2021) B Thermo-Chemotherapy b A promising biocompatible nanotheranostics agent based on chemotherapeutic drug 5-fluorouracil and 2D silicon nanosheets is developed for synergistic thermo-chemotherapy strategy. The tumor tissue functional parameters, including SaO SB 2 sb , HbT, micro microvascular morphology, and hemodynamics, are dynamically monitored by multimodal PA/OCTA/LS imaging strategy to accurately evaluate overall therapeutic effects. [Extracted from the article]

Global navigation satellite system (GNSS) provides users with all-weather, continuous, high-precision positioning, navigation, and timing (PNT) services. In the operation and use of GNSS, the influence of the space environment is a factor that must be considered. For example, during geomagnetic storms, a series of changes in the Earth's magnetosphere, ionosphere, and upper atmosphere affect GNSS's positioning performance. To investigate the positioning performance of global satellite navigation systems during geomagnetic storms, this study selected three geomagnetic storm events that occurred from September to December 2023. Utilizing the global positioning system (GPS)/Beidou navigation satellite system (BDS) dual-system, kinematic precise point positioning (PPP) experiments were conducted, and the raw observational data from 100 stations worldwide was analyzed. The experimental results show that the positioning accuracy of some stations in high-latitude areas decreases significantly when using the conventional Geometry Free (GF) cycle-slip detection threshold during geomagnetic storms, which means that the GF is no longer applicable to high-precision positioning services. Meanwhile, there is no significant change in the satellite signal strengths received at the stations during the period of the decrease in positioning accuracy. Analyzing the cycle-slip rates for stations where abnormal accuracy occurred, it was observed that stations experiencing a significant decline in positioning accuracy exhibited serious cycle-slip misjudgments. To improve the kinematic PPP accuracy during magnetic storms, this paper proposes an adaptive threshold for cycle-slip detection and designs five experimental strategies. After using the GF adaptive threshold, the station positioning accuracy improved significantly. It achieved the accuracy level of the quiet period, while the cycle-slip incidence reached the average level. During magnetic storms, the ionosphere changes rapidly, and the use of the traditional GF constant threshold will cause serious cycle-slip misjudgments, which makes the dynamic accuracy in high latitude areas and some mid-latitude areas uncommon, while the use of the GF adaptive threshold can alleviate this phenomenon and improve the positioning accuracy in the high-latitude regions and some of the affected mid-latitude areas during the magnetic storms. [ABSTRACT FROM AUTHOR]

Verticillium wilt is a severe plant disease that causes massive losses in multiple crops. Increasing the plant resistance to Verticillium wilt is a critical challenge worldwide. Here, we report that the hemibiotrophic Verticillium dahliae -secreted Asp f2-like protein VDAL causes leaf wilting when applied to cotton leaves in vitro but enhances the resistance to V. dahliae when overexpressed in Arabidopsis or cotton without affecting the plant growth and development. VDAL protein interacts with Arabidopsis E3 ligases plant U-box 25 (PUB25) and PUB26 and is ubiquitinated by PUBs in vitro. However, VDAL is not degraded by PUB25 or PUB26 in planta. Besides, the pub25 pub26 double mutant shows higher resistance to V. dahliae than the wild-type. PUBs interact with the transcription factor MYB6 in a yeast two-hybrid screen. MYB6 promotes plant resistance to Verticillium wilt while PUBs ubiquitinate MYB6 and mediate its degradation. VDAL competes with MYB6 for binding to PUBs, and the role of VDAL in increasing Verticillium wilt resistance depends on MYB6. Taken together, these results suggest that plants evolute a strategy to utilize the invaded effector protein VDAL to resist the V. dahliae infection without causing a hypersensitive response (HR); alternatively, hemibiotrophic pathogens may use some effectors to keep plant cells alive during its infection in order to take nutrients from host cells. This study provides the molecular mechanism for plants increasing disease resistance when overexpressing some effector proteins without inducing HR, and may promote searching for more genes from pathogenic fungi or bacteria to engineer plant disease resistance. [ABSTRACT FROM AUTHOR]

Summary: Background: Liver fibrosis is the strongest histological risk factor for liver‐related complications and mortality in metabolic dysfunction‐associated fatty liver disease (MAFLD). Second harmonic generation/two‐photon excitation fluorescence (SHG/TPEF) is a powerful tool for label‐free two‐dimensional and three‐dimensional tissue visualisation that shows promise in liver fibrosis assessment. Aim: To investigate combining multi‐photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD. Methods: AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy‐confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre‐processed images and test data sets. Multi‐layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts. Results: AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3‐4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3‐4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts. Conclusion: AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD. [ABSTRACT FROM AUTHOR]

Tropospheric delay (TD) parameter estimation is a critical issue underlying high-precision data processing for global navigation satellite systems (GNSSs). The most widely used TD parameter estimation methods are the random walk (RW) and piece-wise constant (PWC). The RW method can effectively track rapid variations of tropospheric delay, but it may introduce excessive noise. In contrast, the PWC method introduces less noise, but it is less adaptable to cases of large variations of tropospheric delay. To address the problem of how to choose the optimal TD parameter estimation method, this paper investigates the variation patterns of international GNSS service zenith tropospheric delay (IGS ZTD) products and proposes a combined strategy model for TD parameter estimation. Firstly, this paper avoids the day-boundary jumps problem of IGS ZTD products by grouping based on single-day data. Secondly, this paper introduces discrete point areas (DPAs) to measure the magnitude of the ZTD values and uses comprehensive indicators to reflect the variation of ZTD. Next, based on the Köppen-Geiger climate classification, this study selected five different climate classifications with a total of 20 IGS stations as experimental data. The data assessed span from day of year (DOY) 001 to DOY 365 in 2022. This paper then applied 26 different parameter estimation strategies for static precise point positioning (PPP) data processing, and the parameter estimation strategies that were used include the RW and PWC (with the piece-wise constant ranging from twenty minutes to five hundred minutes at twenty-minute intervals). Finally, ZTD and positioning results were obtained using various parameter estimation methods, and a combined strategy model was established. We selected five different climate classifications of IGS stations as validation data and designed three sets of comparative experiments: RW, PWC120, and the combined strategy model, to verify the effectiveness of the combined strategy model. The experimental results revealed that: RW and the combined strategy model have a comparable ZTD accuracy and both are superior to PWC120. The combined strategy model improves the positioning accuracy in the U direction compared to RW and PWC120. In arid (B) and polar (E) regions with a small variation of TD, the PWC120 strategy displayed a better positioning accuracy than the RW strategy; in equatorial (A) and warm-temperate (C) regions, where there are large variations of TD, the RW strategy exhibited a better positioning accuracy than the PWC120 strategy. The combined strategy model can flexibly select the optimal parameter estimation method according to the comprehensive indicator while ensuring ZTD estimation accuracy; it enhances positioning accuracy. [ABSTRACT FROM AUTHOR]

Background: Rowell syndrome (RS) is an uncommon condition characterized by erythema multiforme (EM)‐like lesions and lupus erythematosus. It is more common in females, and EM may be the first manifestation of the disease with positive autoantibodies, such as antinuclear antibody (ANA), SSA, SSB and rheumatoid factor. The pathogenesis of RS is unknown and is likely caused by drug induction, ultraviolet exposure and infection. Method: We describe a case of RS from China which presented as characteristic targetoid‐like lesions and chilblain‐like erythema on hands and feet. This is a case of RS in a female patient from the inpatient department of dermatology. Results: A 41‐year‐old female with systemic lupus erythematosus exhibited chilblain‐like erythema and characteristic EM lesions on her extremities. She tested positive for serum ANA (1:320) and anti‐double‐stranded DNA, as well as other autoantibodies. Systemic glucocorticoids and hydroxychloroquine worked effectively for her. Conclusion: The present case met diagnostic criteria of RS. Notably, there was a co‑occurrence of facial butterfly erythema, chilblain‐like erythema and EM lesions distributed on the limbs in this case. [ABSTRACT FROM AUTHOR]

The meiotic entry of undifferentiated germ cells is sexually specific and strictly regulated by the testicular or ovarian environment. Germline stem cells with a set of abnormal sex chromosomes and associated autosomes undergo defective meiotic processes and are eventually eliminated by yet to be defined post‐transcriptional modifications. Herein, we report the role of gsdf, a member of BMP/TGFβ family uniquely found in teleost, in the regulation of meiotic entry in medaka (Oryzias latipes) via analyses of gametogenesis in gsdf‐deficient XX and XY gonads in comparison with their wild‐type siblings. Several differentially expressed genes, including the FKB506‐binding protein 7 (fkbp7), were significantly upregulated in pubertal gsdf‐deficient gonads. The increase in alternative pre‐mRNA isoforms of meiotic synaptonemal complex gene sycp3 was visualized using Integrative Genomics Viewer and confirmed by real‐time qPCR. Nevertheless, immunofluorescence analysis showed that Sycp3 protein products reduced significantly in gsdf‐deficient XY oocytes. Transmission electron microscope observations showed that normal synchronous cysts were replaced by asynchronous cysts in gsdf‐deficient testis. Breeding experiments showed that the sex ratio deviation of gsdf−/− XY gametes in a non‐Mendelian manner might be due to the non‐segregation of XY chromosomes. Taken together, our results suggest that gsdf plays a role in the proper execution of cytoplasmic and nuclear events through receptor Smad phosphorylation and Sycp3 dephosphorylation to coordinate medaka gametogenesis, including sex‐specific mitotic divisions and meiotic recombination. [ABSTRACT FROM AUTHOR]

Considering the complexity of the urban rail transit system (URTS), it is necessary to systematically identify the critical factors and their relationships to avoid or prevent urban rail transit accidents. In response to the problems few previous studies have modeled networks for the whole system components of URTS and have focused too much on the identification of specific component states or behaviors without quantitative analysis of inter-component relationships. This paper proposed a risk network construction and analysis method based on complex network theory to identify the critical accident causative factors and their relationships of the URTS system. Firstly, the accident is defined as the combination of a sequence of risk incidents occurring in a certain order and their resulting consequences, and the definitions of risk point and risk incident were given. Secondly, a risk network construction and analysis method based on the combination of accident reports and complex network theory was proposed to obtain the critical factors and their relationships in the process of the accident. At last, this paper constructed the urban rail transit risk point set containing 39 risk points, built the risk network based on 201 accident reports of URTS in China and analyzed the critical risk points and risk relationships. The results show that this study can provide a new perspective for identifying the critical causative factors of URTS accidents and their relationships for practical application in risk analysis and accident prevention. • The new definitions of risk points and incidents are put forward. • A critical risk analysis framework are proposed by complex network theory. • Critical risk points and relationships are identified in the urban rail transit system by the risk network. [ABSTRACT FROM AUTHOR]

Precise point positioning (PPP) can measure ground motions with a centimetre-level accuracy using only one receiver. It has been widely used in earthquake monitoring and earthquake early warning (EEW) systems. However, traditional PPP highly depends on well-established and robust internet infrastructure for data communication. Along with the broadcast ephemeris, the orbit and clock corrections transmitted by the B2b signals of BeiDou Navigation Satellite System (BDS) can be used to recover the precise products of GPS and BDS. Since the B2b products, namely the satellite state parameter messages, are accessible even without internet infrastructure, PPP with B2b signals is more suitable for real-time applications. This study witnesses the application of PPP with B2b products in seismic monitoring for the first time. The shake table experiments demonstrate that PPP with B2b can reach a millimetre-level accuracy in horizontal in earthquake monitoring for GPS-only, BDS-only and GPS/BDS combination. The application to the 2021 M w 7.4 Maduo earthquake shows that the accuracies of seismic displacements derived from GPS-only PPP with B2b products are 1.7, 2.4 and 1.7 cm in east, north and vertical components, compared with those from GPS-only PPP with final precise products. We therefore conclude that PPP with B2b products has a promising prospect in seismogeodesy and related applications such as EEW and rapid hazard response. [ABSTRACT FROM AUTHOR]

Background: Delta‐like homologue 1 (DLK1), a transmembrane protein, is highly expressed in hepatocellular carcinoma (HCC). We explored whether DLK1‐directed chimeric antigen receptor (CAR) T cells can specifically eliminate DLK1‐positive HCC cells and serve as a therapeutic strategy for HCC immunotherapy. Methods: We first characterized a homemade anti‐human DLK1 monoclonal antibody, sequenced the single‐chain Fragment variable (scFv) and integrated it into the second‐generation CAR lentiviral vector, and then developed the DLK1‐directed CAR‐T cells. The cytotoxic activities of DLK1‐directed CAR‐T cells against different HCC cells were evaluated in vitro and in vivo. Results: The genetically modified human T cells with the DLK1‐directed CARs produced cytotoxic activity against DLK1‐positive HCC cells. Additionally, the DLK1‐directed CARs enhanced T cell proliferation and activation in a DLK1‐dependent manner. Interestingly, the DLK1‐targeted CAR‐T cells significantly inhibited both subcutaneous and peritoneal xenograft tumours derived from human liver cancer cell lines HepG2 or Huh‐7. Conclusion: DLK1‐directed CAR‐T cells specifically suppresses DLK1‐positive HCC cells in vitro and in vivo. This study provides a novel transmembrane antigen DLK1 as a potential therapeutic target appropriate for CAR‐T cell therapy, which may be further developed as a clinical therapeutic strategy for HCC immunotherapy. [ABSTRACT FROM AUTHOR]

• CS IM-CT (collagen score of the tumor margin minus tumor center) was proposed. • Collagen score was analyzed using multiphoton imaging. • CS IM-CT was an effective prognostic predictor in advanced rectal cancer. • Nomogram with the CS IM-CT can individually predict prognosis in rectal cancer. Little is known about the relationship between collagen and the prognosis of patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT). This study aimed to quantitatively analyze collagen alterations, establish a collagen score (CS) in the tumor microenvironment, and evaluate and validate the relationship of the CS with prognosis in these patients. A total of 365 primary patients diagnosed with LARC after nCRT between 2011 and 2018 were retrospectively reviewed (training cohort: 210; independent validation cohort: 155). Multiple collagen features of two fields in the tumor microenvironment, the core of the tumor (CT) and the invasive margin (IM), were derived from multiphoton imaging, and the CS IM-CT was generated using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The CS IM-CT was created based on 3 features: collagen area, number of collagen fibers and a Gabor textural feature. In the training cohort, the CS IM-CT predicted 3-year disease-free survival (DFS) with an area under the receiver operating characteristic curve (AUROC) of 0.765 (0.675–0.854) and an overall survival (OS) with AUROC of 0.822 (0.734–0.909). Additionally, the CS IM-CT was significantly associated with DFS and OS in the two cohorts. A nomogram with the CS IM-CT was developed and showed good prognostic value predicting a 3-year DFS with an AUROC of 0.826 (0.748–0.905) and an OS with AUROC of 0.882 (0.803–0.960). The CS IM-CT is an effective prognostic marker in patients with LARC after nCRT, and the nomogram with the CS IM-CT can be used to accurately predict the individual prognosis of these patients. [ABSTRACT FROM AUTHOR]

Objective: The primary aim was to assess associations between total steps per day and incident diabetes, whereas the secondary aim was to assess whether the intensity and/or cadence of steps is associated with incident diabetes.Research Design and Methods: Women without physician-diagnosed diabetes (n = 4,838; mean [SD] age 78.9 [6.7] years) were followed up to 6.9 years; 395 developed diabetes. Hip-worn ActiGraph GT3X+ accelerometers worn for 1 week enabled measures of total, light-intensity, and moderate- to vigorous-intensity (MV-intensity) steps per day. Using Cox proportional hazards analysis we modeled adjusted change in the hazard rate for incident diabetes associated with total, light-intensity, and MV-intensity steps per day. We further estimated the proportion of the steps-diabetes association mediated by BMI.Results: On average, participants took 3,729 (SD 2,114) steps/day, of which 1,875 (791) were light-intensity steps and 1,854 ± 1,762 were MV-intensity. More steps per day were associated with a lower hazard rate for incident diabetes. Confounder-adjusted models for a 2,000 steps/day increment yielded hazard ratio (HR) 0.88 (95% CI 0.78-1.00; P = 0.046). After further adjustment for BMI, HR was 0.90 (95% CI 0.80-1.02; P = 0.11). BMI did not significantly mediate the steps-diabetes association (proportion mediated = 17.7% [95% CI -55.0 to 142.0]; P = 0.09]). The relationship between MV-intensity steps per day (HR 0.86 [95% CI 0.74-1.00]; P = 0.04) and incident diabetes was stronger than for light-intensity steps per day (HR 0.97 [95% CI 0.73-1.29]; P = 0.84).Conclusions: These findings suggest that for older adults, more steps per day are associated with lower incident diabetes and MV-intensity steps are most strongly associated with a lower hazard of diabetes. This evidence supports that regular stepping is an important risk factor for type 2 diabetes prevention in older adults. [ABSTRACT FROM AUTHOR]

This study aimed to propose a new convolutional neural network (CNN)-based model for spatial resolution enhancement of Landsat images without finer resolution bands for training the CNN-based model. Its performance was assessed using Landsat 8 images from eight different regions that were dominated by various topographic features, and land use and land cover types by comparison with bicubic (BIC), nearest neighbor (NN) and Lanczos (LZ) resampling. It was found that for spatial resolution enhancement of 30 m resolution images to 15 m, 10 m and 7.5 m, the proposed model increased the average peak-signal-to-noise-ratio (PSNR) values by 1.7% to 11.5% compared with the compared methods. The PSNR increases were statistically significantly different from zero at the significant level of.05, but the improvement decreased as the spatial resolution of the input images became finer. Moreover, the deeper the CNN model, the better the performance, but after nine layers, the gain of performance slowed down. This indicates that the proposed algorithm is promising for spatial resolution enhancement of optical images without input of finer spatial resolution images. [ABSTRACT FROM AUTHOR]

Delineating accurate flowlines using digital elevation models is a critical step for overland flow modeling. However, extracting surface flowlines from high‐resolution digital elevation models (HRDEMs) can be biased, partly due to the absence of information on the locations of anthropogenic drainage structures (ADS) such as bridges and culverts. Without the ADS, the roads may act as "digital dams" that prevent accurate delineation of flowlines. However, it is unclear what variables for terrain‐based hydrologic modeling can be used to mitigate the effect of "digital dams." This study assessed the impacts of ADS locations, spatial resolution, depression processing methods, and flow direction algorithms on hydrologic connectivity in an agrarian landscape of Nebraska. The assessment was conducted based on the offset distances between modeled drainage crossings and actual ADS on the road. Results suggested that: (a) stream burning in combination with the D8 or D‐Infinity flow direction algorithm is the best option for modeling surface flowlines from HRDEMs in an agrarian landscape; (b) increasing the HRDEM resolution was found significant for facilitating accurate drainage crossing near ADS locations; and (c) D8 and D‐Infinity flow direction algorithms resulted in similar patterns of drainage crossing at ADS locations. This research is expected to result in improved parameter settings for HRDEMs‐based hydrologic modeling. [ABSTRACT FROM AUTHOR]

Elevated expression and genetic aberration of IRTKS, also named as BAIAP2L1, have been observed in many tumors, especially in tumor progression. however, the molecular and cellular mechanisms involved in the IRTKS-enhanced tumor progression are obscure. Here we show that higher IRTKS level specifically increases histone H3 lysine 9 trimethylation (H3K9me3) by promoting accumulation of the histone methyltransferase SETDB1. Furthermore, we reveal that IRTKS recruits the deubiquitinase OTUD4 to remove Lys48-linked polyubiquitination at K182/K1050 sites of SETDB1, thus blocking SETDB1 degradation via the ubiquitin-proteasome pathway. Interestingly, the enhanced IRTKS–OTUD4–SETDB1–H3K9me3 axis leads to a general decrease in chromatin accessibility, which inhibits transcription of CDH1 encoding E-cadherin, a key molecule essential for maintaining epithelial cell phenotype, and therefore results in epithelial-mesenchymal transition (EMT) and malignant cell metastasis. Clinically, the elevated IRTKS levels in tumor specimens correlate with SETDB1 levels, but negatively associate with survival time. Our data reveal a novel mechanism for the IRTKS-enhanced tumor progression, where IRTKS cooperates with OTUD4 to enhance SETDB1-mediated H3K9 trimethylation that promotes tumor metastasis via suppressing E-cadherin expression. This study also provides a potential approach to reduce the activity and stability of the known therapeutic target SETDB1 possibly through regulating IRTKS or deubiquitinase OTUD4. • Higher levels of IRTKS increase H3K9me3 by promoting SETDB1 accuumulation. • IRTKS recruits the deubiquitinase OTUD4 to blocking SETDB1 degradation. • The enhanced IRTKS–OTUD4–SETDB1–H3K9me3 axis decreases chromatin accessibility, and inhibits E-cadherin transcription, resulting in EMT and tumor metastasis. • Clinically, elevated IRTKS levels in tumor correlate with SETDB1 levels, and negatively associate with survival time. [ABSTRACT FROM AUTHOR]

The degradation of permafrost in the Earth's cold regions due to climate warming and human activities is of increasing concern because of its impact on ecosystems and land management, thus, monitoring the degradation becomes critical. This study aimed to demonstrate a method in which Sentinel-1 DInSAR and MODIS data were combined to estimate and map seasonal and long-term land surface deformation (subsidence/uplifting) and permafrost active layer thickness (ALT) in Donnelly Training Area (DTA) in Alaska. The coherence (similarity) between the paired SAR images was analyzed for their applicability. A sensitivity analysis and accuracy assessment of the estimates were conducted to account for their quality. Results showed that the estimated seasonal subsidence mainly occurring in June and July was in the range of 0 to −0.43 m, while the estimated uplifting mainly happening from September to May of next year was as great as +0.34 m. Moreover, the estimated long-term (2015 to 2018) subsidence and uplifting were mostly distributed in the southern and northern parts of DTA, respectively. The spatially variable time delays led to the ALT estimates greater along the east river and in the west and south parts, and smaller in the north parts. At the significance level of 0.05, the coherence estimates of the paired images were significantly different from zero and compared with the referenced predictions from a widely used annual prediction model, the average residual of the ALT estimates did not significantly differ from zero at the significance level of 0.05. The spatial distributions of the uncertainties for the seasonal surface deformation estimates were similar to those of the input uncertainties from the estimates of model coefficients, the phase change, the modelling error, and the image pairs. Thus, the DInSAR image based method coupled with MODIS data offered the potential of mapping and monitoring the dynamics of permafrost environment for the cold regions such as DTA in which collecting field observations is difficult and costly. This study also enhanced understanding spatiotemporal variability of permafrost deformation in DTA and provided guidelines for developing a near real-time monitoring system of the permafrost environment. • Permafrost degradation due to climate warming and human activity is of great concern. • Land surface deformation of an installation in Alaska is mapped by SAR&MODIS images. • A quality assessment of the land surface deformation estimates is conducted. • The accuracy of the estimates is compatible with that from a widely used model. • This study provides guidelines for developing a near real-time monitoring system. [ABSTRACT FROM AUTHOR]

For the same antenna type, numerical differences among the phase center correction (PCC) models released by different institutes can reach several millimeters, which is far beyond the nominal calibration accuracy. This contribution introduces a new method to evaluate the consistency of these PCC models. We first investigated the coupling of phase center offset (PCO) and phase center variation (PCV) through simulation experiments, and the results show that the calibrated PCO values under different strategies may result in large differences, and so do PCV values due to strong coupling with PCO. This is further confirmed by field calibration experiments. Moreover, a new datum parameter is introduced to equivalently transform the PCC models to assess the consistency of PCC models of the same antenna type calibrated under different strategies or by different facilities. It is also essential to perform consistency analysis of PCC models in the coordinate domain. We further investigated these PCC models through a simulated positioning experiment. The results show that millimeter-level consistency of PCC models will lead to the same level of positioning precision in the coordinate domain. Moreover, as a comparison, both baseline positioning and PPP were performed with an antenna-type JAV_RINGANT_G3T NONE based on real observations. Multiday results showed that the average RMS of the positioning differences between PCC models from robot and anechoic chamber calibration is less than 1 mm for the baseline solution and 4 mm for the PPP solution, although the PCC model differences can reach 6 mm in L1 and 4 mm in L2, respectively. Finally, we also investigated the distribution of position biases without PCC or with inaccurate PCC. Considering the actual GPS constellation, we found that position biases have a strong correlation with latitude, if PCV values fluctuate greatly with the users' elevation angle. [ABSTRACT FROM AUTHOR]