TRIM21 is critical in regulating hepatocellular carcinoma growth and response to therapy by altering the MST1/YAP pathway.

Liver cancer is the sixth most common cancer and the third leading cause of cancer-related death globally. Despite efforts being made in last two decades in cancer diagnosis and treatment, the 5-year survival rate of liver cancer remains extremely low. TRIM21 participates in cancer metabolism, glycolysis, immunity, chemosensitivity and metastasis by targeting various substrates for ubiquitination. TRIM21 serves as a prognosis marker for human hepatocellular carcinoma (HCC), but the mechanism by which TRIM21 regulates HCC tumorigenesis and progression remains elusive. In this study, we demonstrated that TRIM21 protein levels were elevated in human HCC. Elevated TRIM21 expression was associated with HCC progression and poor survival. Knockdown of TRIM21 in HCC cell lines significantly impaired cell growth and metastasis and enhanced sorafenib-induced toxicity. Mechanistically, we found that knockdown of TRIM21 resulted in cytosolic translocation and inactivation of YAP. At the molecular level, we further identified that TRIM21 interacted and induced ubiquitination of MST1, which resulted in MST1 degradation and YAP activation. Knockdown of MST1 or overexpression of YAP reversed TRIM21 knockdown-induced impairment of HCC growth and chemosensitivity. Taken together, the current study demonstrates a novel mechanism that regulates the Hippo pathway and reveals TRM21 as a critical factor that promotes growth and chemoresistance in human HCC.

SIRT7 promotes Hippo/YAP activation and cancer cell proliferation in hepatocellular carcinoma via suppressing MST1.

Abnormal activation of the oncogene YAP in the Hippo pathway is a major feature in liver cancer and inactivation of MST1/2 has been shown to be responsible for the overactivation of YAP that led to tumorigenesis. However, mechanisms underlying MST1/2 dysregulation remain poorly understood. RNA-seq analysis and genome (KEGG) pathway enrichment analysis were used to identify genes and pathways that were regulated by SIRT7. qRT-PCR, ChIP, and luciferase assay were used to investigate transcriptional regulation. Mass spectrometry, co-immunoprecipitation and immunoprecipitation were used to exam protein-protein interaction and post-transcriptional modification. A xenograft mouse model was used to confirm the effect of SIRT7 and SIRT7 inhibitors on hepatocellular carcinoma (HCC) proliferation in vivo. We found that SIRT7 suppresses MST1 by both transcriptional regulation and post-transcriptional modification, which in turn promotes YAP nuclear localization and transcriptional activation in liver cancer. Mechanistically, we revealed that SIRT7 suppresses MST1 transcription by binding to the MST1 promoter and inducing H3K18 deacetylation in its promoter region. In addition, SIRT7 directly binds to and deacetylates MST1, which primes acetylation-dependent MST1 ubiquitination and protein degradation. In clinical samples, we confirmed a negative correlation between SIRT7 and MST1 protein levels, and high SIRT7 expression correlated with elevated YAP expression and nuclear localization. In addition, SIRT7 specific inhibitor 2800Z sufficiently inhibited HCC growth by disrupting the SIRT7/MST1/YAP axis. Our data thus revealed the previously undescribed function of SIRT7 in regulating the Hippo pathway in HCC and further proved that targeting SIRT7 might provide novel therapeutic options for the treatment of liver cancer.

Exceptional Nexus in Bose-Einstein Condensates with Collective Dissipation.

In multistate non-Hermitian systems, higher-order exceptional points and exotic phenomena with no analogues in two-level systems arise. A paradigm is the exceptional nexus (EX), a third-order EP as the cusp singularity of exceptional arcs (EAs), that has a hybrid topological nature. Using atomic Bose-Einstein condensates to implement a dissipative three-state system, we experimentally realize an EX within a two-parameter space, despite the absence of symmetry. The engineered dissipation exhibits density dependence due to the collective atomic response to resonant light. Based on extensive analysis of the system's decay dynamics, we demonstrate the formation of an EX from the coalescence of two EAs with distinct geometries. These structures arise from the different roles played by dissipation in the strong coupling limit and quantum Zeno regime. Our Letter paves the way for exploring higher-order exceptional physics in the many-body setting of ultracold atoms.

Associating Peritoneal Metastasis With T2-Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study.

BACKGROUND: The preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision-making. PURPOSE: To investigate the performance of T2 -weighted (T2W) MRI-based deep learning (DL) and radiomics methods for PM evaluation in EOC patients. STUDY TYPE: Retrospective. POPULATION: Four hundred seventy-nine patients from five centers, including one training set (N = 297 [mean, 54.87 years]), one internal validation set (N = 75 [mean, 56.67 years]), and two external validation sets (N = 53 [mean, 55.58 years] and N = 54 [mean, 58.22 years]). FIELD STRENGTH/SEQUENCE: 1.5 or 3 T/fat-suppression T2W fast or turbo spin-echo sequence. ASSESSMENT: ResNet-50 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision-level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated. STATISTICAL TESTS: Receiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two-tailed P < 0.05 was considered significant. RESULTS: The ensemble model had the best AUCs, outperforming the DL model (0.844 vs. 0.743, internal validation set; 0.859 vs. 0.737, external validation set I) and clinical model (0.872 vs. 0.730, external validation set II). After model assistance, all readers had significantly improved sensitivity, especially for those with less experience (junior radiologist1, from 0.639 to 0.820; junior radiologist2, from 0.689 to 0.803; resident1, from 0.623 to 0.803; resident2, from 0.541 to 0.738). One resident also had significantly improved specificity (from 0.633 to 0.789). DATA CONCLUSIONS: T2W MRI-based DL and radiomics approaches have the potential to preoperatively predict PM in EOC patients and assist in clinical decision-making. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Ferritinophagy-mediated ferroptosis facilitates methotrexate-induced hepatotoxicity by high-mobility group box 1 (HMGB1).

BACKGROUND AND AIM: Hepatotoxicity is a well-defined reaction to methotrexate (MTX), a drug commonly used for the treatment of rheumatoid arthritis and various tumours. We sought to elucidate the mechanism underlying MTX-induced hepatotoxicity and establish a potentially effective intervention strategy. METHODS: We administered MTX to liver cells and mice and assessed hepatotoxicity by cell viability assay and hepatic pathological changes. We determined ferroptosis and ferritinophagy by detecting ferroptosis-related markers and autophagic degradation of ferritin heavy chain 1 (FTH1). RESULTS: We have shown that hepatocytes treated with MTX undergo ferroptosis, and this process can be attenuated by ferroptosis inhibitors. Interestingly, NCOA4-mediated ferritinophagy was found to be involved in MTX-induced ferroptosis, which was demonstrated by the relief of ferroptosis through the inhibition of autophagy or knockdown of Ncoa4. Furthermore, MTX treatment resulted in the elevation of high-mobility group box 1 (HMGB1) expression. The depletion of Hmgb1 in hepatocytes considerably alleviated MTX-induced hepatotoxicity by limiting autophagy and the subsequent autophagy-dependent ferroptosis. It is noteworthy that glycyrrhizic acid (GA), a precise inhibitor of HMGB1, effectively suppressed autophagy, ferroptosis and hepatotoxicity caused by MTX. CONCLUSION: Our study shows the significant roles of autophagy-dependent ferroptosis and HMGB1 in MTX-induced hepatotoxicity. It emphasizes that the inhibition of ferritinophagy and HMGB1 may have potential as a therapeutic approach for preventing and treating MTX-induced liver injury.

Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography.

OBJECTIVES: We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. METHODS: We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. RESULTS: The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. CONCLUSION: The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. CLINICAL RELEVANCE STATEMENT: The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. KEY POINTS: • We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI.

Cold EMR vs. Hot EMR for the removal of sessile serrated polyps larger than 10 mm: a systematic review and meta-analysis.

BACKGROUND: Endoscopic mucosal resection (EMR) appears to be a promising technique for the removal of sessile serrated polyps (SSPs) ≥ 10 mm. To assess the effectiveness and safety of EMR for removing SSPs ≥ 10 mm, we conducted this systematic review and meta-analysis. METHODS: We conducted a thorough search of Embase, PubMed, Cochrane, and Web of Science databases for relevant studies reporting on EMR of SSPs ≥ 10 mm, up until December 2023. Our primary endpoints of interest were rates of technical success, residual SSPs, and adverse events (AE). RESULTS: Our search identified 426 articles, of which 14 studies with 2262 SSPs were included for analysis. The rates of technical success, AEs, and residual SSPs were 100%, 2.0%, and 3.1%, respectively. Subgroup analysis showed that the technical success rates were the same for polyps 10-19 and 20 mm, and en-bloc and piecemeal resection. Residual SSPs rates were similar in en-bloc and piecemeal resection, but much lower in cold EMR (1.0% vs. 4.2%, P = 0.034). AEs rates were reduced in cold EMR compared to hot EMR (0% vs. 2.9%, P = 0.168), in polyps 10-19 mm compared to 20 mm (0% vs. 4.1%, P = 0.255), and in piecemeal resection compared to en-bloc (0% vs. 0.7%, P = 0.169). CONCLUSIONS: EMR is an effective and safe technique for removing SSPs ≥ 10 mm. The therapeutic effect of cold EMR is superior to that of hot EMR, with a lower incidence of adverse effects. PROSPERO REGISTRATION NUMBER: CRD42023388959.

Research on Time Series Monitoring of Surface Deformation in Tongliao Urban Area Based on SBAS-PS-DS-InSAR.

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were -90.78 mm and -83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are -35.38 mm/y and -30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning.

Video Abnormal Behavior Recognition and Trajectory Prediction Based on Lightweight Skeleton Feature Extraction.

Video action recognition based on skeleton nodes is a highlighted issue in the computer vision field. In real application scenarios, the large number of skeleton nodes and behavior occlusion problems between individuals seriously affect recognition speed and accuracy. Therefore, we proposed a lightweight multi-stream feature cross-fusion (L-MSFCF) model to recognize abnormal behaviors such as fighting, vicious kicking, climbing over the wall, et al., which could obviously improve recognition speed based on lightweight skeleton node calculation, and improve recognition accuracy based on occluded skeleton node prediction analysis in order to effectively solve the behavior occlusion problem. The experiments show that our proposed All-MSFCF model has a video action recognition average accuracy rate of 92.7% for eight kinds of abnormal behavior recognition. Although our proposed lightweight L-MSFCF model has an 87.3% average accuracy rate, its average recognition speed is 62.7% higher than the full-skeleton recognition model, which is more suitable for solving real-time tracing problems. Moreover, our proposed Trajectory Prediction Tracking (TPT) model could real-time predict the moving positions based on the dynamically selected core skeleton node calculation, especially for the short-term prediction within 15 frames and 30 frames that have lower average loss errors.

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm-Convolutional Neural Network-Long Short-Term Memory Model.

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm-convolutional neural network-long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research.

Leu promotes C2C12 cell differentiation by regulating the GSK3ß/ß-catenin signaling pathway through facilitating the interaction between SESN2 and RPN2.

BACKGROUND: Leucine (Leu) is an essential amino acid that facilitates skeletal muscle satellite cell differentiation, yet its mechanism remains underexplored. Sestrin2 (SESN2) serves as a Leu sensor, binding directly to Leu, while ribophorin II (RPN2) acts as a signaling factor in multiple pathways. This study aimed to elucidate Leu's impact on mouse C2C12 cell differentiation and skeletal muscle injury repair by modulating RPN2 expression through SESN2, offering a theoretical foundation for clinical skeletal muscle injury prevention and treatment. RESULTS: Leu addition promoted C2C12 cell differentiation compared to the control, enhancing early differentiation via myogenic determinant (MYOD) up-regulation. Sequencing revealed SESN2 binding to and interacting with RPN2. RPN2 overexpression up-regulated MYOD, myogenin and myosin heavy chain 2, concurrently decreased p-GSK3ß and increased nuclear ß-catenin. Conversely, RPN2 knockdown yielded opposite results. Combining RPN2 knockdown with Leu rescued increased p-GSK3ß and decreased nuclear ß-catenin compared to Leu absence. Hematoxylin and eosin staining results showed that Leu addition accelerated mouse muscle damage repair, up-regulating Pax7, MYOD and RPN2 in the cytoplasm, and nuclear ß-catenin, confirming that the role of Leu in muscle injury repair was consistent with the results for C2C12 cells. CONCLUSION: Leu, bound with SESN2, up-regulated RPN2 expression, activated the GSK3ß/ß-catenin pathway, enhanced C2C12 differentiation and expedited skeletal muscle damage repair. © 2024 Society of Chemical Industry.

[Sinomenine inhibits PDGF/PDGFR signaling pathway to reduce RA-FLS migration induced by NETs].

This study aims to decipher the mechanism of sinomenine in inhibiting platelet-derived growth factor/platelet-derived growth factor receptor(PDGF/PDGFR) signaling pathway in rheumatoid arthritis-fibroblast-like synoviocyte(RA-FLS) migration induced by neutrophil extracellular traps(NETs). RA-FLS was isolated from the synovial tissue of 3 RA patients and cultured. NETs were extracted from the peripheral venous blood of 4 RA patients and 4 healthy control(HC). RA-FLS was classified into control group, HC-NETs group, RA-NETs group, RA-NETs+sinomenine group and RA-NETs+sinomenine+CP-673451 group. RNA-sequencing(RNA-seq) was conducted to identify the differentially expressed genes between HC-NETs and RA-NETs groups. Sangerbox was used to perform the Gene Ontology(GO) function and the Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment. Cytoscape was employed to build the protein-protein interaction(PPI) network. AutoDock Vina and PyMOL were used for molecular docking of sinomenine with PDGFß and PDGFRß. The cell proliferation and migration were determined by the cell counting kit-8(CCK-8) and cell scratch assay, respectively. Western blot was employed to determine the protein level of PDGFRß. Real-time quantitative polymerase chain reaction(RT-qPCR) was carried out to determine the mRNA levels of matrix metalloproteinases(MMPs). The results revealed that neutrophils in RA patients were more likely to produce NETs. Compared with HC-NETs group, RA-NETs group showed up-regulated expression of PDGFß and PDGFRß. Compared with control group, RA-NETs group showed increased cell proliferation and migration and up-regulated protein level of PDGFRß and mRNA levels of PDGFß, PDGFRß, MMP1, MMP3, and MMP9(P<0.05). Compared with RA-NETs group, RA-NETs+sinomenine group presented decreased cell proliferation and migration and down-regulated protein and mRNA level of PDGFRß and mRNA levels of MMP1, MMP3, and MMP9(P<0.05). Compared with RA-NETs+sinomenine group, the proliferation ability of RA-NETs+sinomenine+CP-673451 group decreased(P<0.05). The findings prove that sinomenine reduces the RA-NETs-induced RA-FLS migration by inhibiting PDGF/PDGFR signaling pathway, thus mitigating RA.

Predictive value of Gd-EOB-DTPA -enhanced magnetic resonance imaging for post-hepatectomy liver failure: a systematic review and meta-analysis.

BACKGROUND: Accurate preoperative diagnosis of post-hepatectomy liver failure (PHLF) is particularly important to improve the prognosis of patients. PURPOSE: To evaluate the predictive value of Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) for post-hepatectomy liver failure. MATERIAL AND METHODS: A systematic search was performed in the PubMed, Embase, the Cochrane Library, and Web of Science databases to find relevant original articles published up to December 2021. The included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The bivariate random-effects model was used to assess the diagnostic authenticity. Meta-regression analyses were performed to analyze the potential heterogeneity. RESULTS: In total, 13 articles were included. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the area under the summary receiver operating characteristic curves were 88% (95% confidence interval [CI] = 0.80-0.94), 80% (95% CI = 0.73-0.86), 4.4 (95% CI = 3.3-5.9), 0.14 (95% CI = 0.08-0.25), 31 (95% CI = 17-57), and 0.91 (95% CI = 0.89-0.94), respectively. There was no publication bias and threshold effect in our study. CONCLUSION: Gd-EOB-DTPA-enhanced MRI is a potentially useful for the prediction of PHLF after major hepatectomy.

A Second-Order Method for Removing Mixed Noise from Remote Sensing Images.

Remote sensing image denoising is of great significance for the subsequent use and research of images. Gaussian noise and salt-and-pepper noise are prevalent noises in images. Contemporary denoising algorithms often exhibit limitations when addressing such mixed noise scenarios, manifesting in suboptimal denoising outcomes and the potential blurring of image edges subsequent to the denoising process. To address the above problems, a second-order removal method for mixed noise in remote sensing images was proposed. In the first stage of the method, dilated convolution was introduced into the DnCNN (denoising convolutional neural network) network framework to increase the receptive field of the network, so that more feature information could be extracted from remote sensing images. Meanwhile, a DropoutLayer was introduced after the deep convolution layer to build the noise reduction model to prevent the network from overfitting and to simplify the training difficulty, and then the model was used to perform the preliminary noise reduction on the images. To further improve the image quality of the preliminary denoising results, effectively remove the salt-and-pepper noise in the mixed noise, and preserve more image edge details and texture features, the proposed method employed a second stage on the basis of adaptive median filtering. In this second stage, the median value in the original filter window median was replaced by the nearest neighbor pixel weighted median, so that the preliminary noise reduction result was subjected to secondary processing, and the final denoising result of the mixed noise of the remote sensing image was obtained. In order to verify the feasibility and effectiveness of the algorithm, the remote sensing image denoising experiments and denoised image edge detection experiments were carried out in this paper. When the experimental results are analyzed through subjective visual assessment, images denoised using the proposed method exhibit clearer and more natural details, and they effectively retain edge and texture features. In terms of objective evaluation, the performance of different denoising algorithms is compared using metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and mean structural similarity index (MSSIM). The experimental outcomes indicate that the proposed method for denoising mixed noise in remote sensing images outperforms traditional denoising techniques, achieving a clearer image restoration effect.

A Hybrid Genetic Algorithm Based on Imitation Learning for the Airport Gate Assignment Problem.

Airport gates are the main places for aircraft to receive ground services. With the increased number of flights, limited gate resources near to the terminal make the gate assignment work more complex. Traditional solution methods based on mathematical programming models and iterative algorithms are usually used to solve these static situations, lacking learning and real-time decision-making abilities. In this paper, a two-stage hybrid algorithm based on imitation learning and genetic algorithm (IL-GA) is proposed to solve the gate assignment problem. First of all, the problem is defined from a mathematical model to a Markov decision process (MDP), with the goal of maximizing the number of flights assigned to contact gates and the total gate preferences. In the first stage of the algorithm, a deep policy network is created to obtain the gate selection probability of each flight. This policy network is trained by imitating and learning the assignment trajectory data of human experts, and this process is offline. In the second stage of the algorithm, the policy network is used to generate a good initial population for the genetic algorithm to calculate the optimal solution for an online instance. The experimental results show that the genetic algorithm combined with imitation learning can greatly shorten the iterations and improve the population convergence speed. The flight rate allocated to the contact gates is 14.9% higher than the manual allocation result and 4% higher than the traditional genetic algorithm. Learning the expert assignment data also makes the allocation scheme more consistent with the preference of the airport, which is helpful for the practical application of the algorithm.

Protein 4.1R negatively regulates P815 cells proliferation by inhibiting C-Kit-mediated signal transduction.

The proliferation of mast cells (MCs) plays a crucial role in either physiological or pathological progression of human physical. C-Kit-mediated signaling pathway has been confirmed to play a key role in MCs proliferation, and the regulatory mechanisms of C-Kit-mediated MCs proliferation need to be further explored. Our previous study found that protein 4.1R could negatively regulate T cell receptor (TCR) mediated signal pathways in CD4+ T cells. Little is known about the function of 4.1R in C-Kit-mediated proliferation of MCs. In this study, P815-4.1R-/- cells were constructed by using CRISPR/Cas9 technique. Lack of 4.1R significantly enhanced P815 cells proliferation by accelerating the progression of cell cycle. 4.1R could also significantly alleviate the clinical symptoms of systemic mastocytosis (SM) and improve the overall survival of SM mice. Further study showed that 4.1R could interact directly with C-Kit to inhibit the activation of C-Kit-mediated Ras-Raf-MAPKs and PI3K-AKT signal pathways. Taken together, our findings demonstrate that protein 4.1R, a novel negative regulator, negatively regulates MCs proliferation by inhibiting C-Kit-mediated signal transduction, which maybe provide a potential target to the prevention and treatment of abnormal MCs proliferation-related diseases.

Adult-onset leukoencephalopathy with axonal spheroids and pigmented glia by a novel mutation of the CSF1R gene.

Adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP) is a rare autosomal dominant disorder caused by mutations in the colony-stimulating factor 1 receptor (CSF1R) gene. As of 2022, more than 100 different CSF1R mutations were reported in patients with CSF1R-related leukoencephalopathy. In this case report, we describe ALSP in a previously healthy 46-year-old woman who presented with memory impairment, poor interpersonal behavior, and decreased verbal fluency. Brain magnetic resonance imaging (MRI) showed confluent white matter changes and atrophy of the corpus callosum. Whole-exome sequencing identified a novel splice-site mutation (C.1858 + 5G > A) in intron 13 of the CSF1R gene, resulting in an intron 12 retention and an exon 13 deletion of CSF1R mRNA.

NeuralTree: A 256-Channel 0.227-µJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC.

Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel counts and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227µJ/class energy efficiency in a compact area of 0.014mm2/channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In-vivo neural recordings using soft µECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease (PD) from human local field potentials (LFPs) was demonstrated.

Photoelectric conversion on Earth's surface via widespread Fe- and Mn-mineral coatings.

Sunlight drives photosynthesis and associated biological processes, and also influences inorganic processes that shape Earth's climate and geochemistry. Bacterial solar-to-chemical energy conversion on this planet evolved to use an intricate intracellular process of phototrophy. However, a natural nonbiological counterpart to phototrophy has yet to be recognized. In this work, we reveal the inherent "phototrophic-like" behavior of vast expanses of natural rock/soil surfaces from deserts, red soils, and karst environments, all of which can drive photon-to-electron conversions. Using scanning electron microscopy, transmission electron microscopy, micro-Raman spectroscopy, and X-ray absorption spectroscopy, Fe and Mn (oxyhydr)oxide-rich coatings were found in rock varnishes, as were Fe (oxyhydr)oxides on red soil surfaces and minute amounts of Mn oxides on karst rock surfaces. By directly fabricating a photoelectric detection device on the thin section of a rock varnish sample, we have recorded an in situ photocurrent micromapping of the coatings, which behave as highly sensitive and stable photoelectric systems. Additional measurements of red soil and powder separated from the outermost surface of karst rocks yielded photocurrents that are also sensitive to irradiation. The prominent solar-responsive capability of the phototrophic-like rocks/soils is ascribed to the semiconducting Fe- and Mn (oxyhydr)oxide-mineral coatings. The native semiconducting Fe/Mn-rich coatings may play a role similar, in part, to photosynthetic systems and thus provide a distinctive driving force for redox (bio)geochemistry on Earth's surfaces.

VISTA-Ig ameliorates OXA-induced allergic dermatitis symptoms in mice.

BACKGROUND: Allergic dermatitis (AD) is a chronic inflammatory skin disease that a variety of immune cells are involved in the progression of AD. Among them, T cells are one of major players of AD pathogenesis. The V-domain Ig suppressor of T cell activation (VISTA) has been reported that it has a potential immunomodulatory for T cell response. OBJECTIVE: This study aimed to investigate immunomodulatory of recombinant VISTA-Ig fusion protein in AD mice model. METHODS: The model of AD was built with oxazolone (OXA) in BALB/c mice, then VISTA-Ig was used to treat AD by intraperitoneal (IP) injection. The ear thickness was measured by a digital thickness gauge. The ears tissues were collected and subjected to hematoxylin-eosin (H&E) and toluidine blue (TB) staining. The secretion levels of IL-4 and IgE in the serum were measured by enzyme linked immunosorbent assay (ELISA). The mRNA expression levels of inflammatory cytokines (IL-1ß, IL-6, IL-12, and INF-γ) in ear tissues were measured using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: Treatment with VISTA-Ig successfully alleviated the symptoms of AD, such as erythema, horny substance, and swelling. The infiltration of inflammatory cells was significantly reduced following VISTA-Ig therapy. The secretion levels of IL-4 and IgE in the serum were significantly attenuated following treatment with VISTA-Ig. Additionally, VISTA-Ig observably down-regulated inflammatory cytokines expression in ear tissues. CONCLUSIONS & CLINICAL RELEVANCE: Taken together, our results showed that VISTA-Ig possessed the potential to be a novel immunomodulatory candidate drug against AD.