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Abstract Objective To analyze the characteristics of high-frame-rate contrast-enhanced ultrasound (H-CEUS) in solid renal tumors using qualitative and quantitative methods. Methods Seventy-five patients who underwent preoperative conventional ultrasound (US), conventional contrast-enhanced ultrasound (C-CEUS), and H-CEUS examination of renal tumors were retrospectively analyzed, with a total of 89 renal masses. The masses were divided into the benign (30 masses) and malignant groups (59 masses) based on the results of enhanced computer tomography and pathology. The location, diameter, shape, border, calcification, and color doppler blood flow imaging (CDFI) of the lesions were observed by US, and the characteristics of the C-CEUS and H-CEUS images were qualitatively and quantitatively analyzed. The χ² test or Fisher’s exact probability method was used to compare the US image characteristics between the benign and malignant groups, and the image characteristics of C-CEUS and H-CEUS between the benign and malignant groups. Moreover, the nonparametric Mann-Whitney test was used to compare the differences in C-CEUS and H-CEUS time-intensity curve (TIC) parameters. Results Significant differences in gender, surgical approach, echogenicity, and CDFI were observed between the malignant and benign groups (p = 0.003,

Cholangiocarcinoma (CCA) is a bile duct malignancy with a dismal prognosis. This study systematically investigated the role of the ribosomal protein S6 (RPS6) gene, which is dependent in CCA. We found that RPS6 upregulation in CCA tissues was correlated with a poor prognosis. Functional investigations have shown that alterations in RPS6 expression, both gain- and loss-of function could affect the proliferation of CCA cells. In xenograft tumor models, RPS6 overexpression enhances tumorigenicity, whereas RPS6 silencing reduces it. Integration analysis using RNA-seq and proteomics elucidated downstream signaling pathways of RPS6 depletion by affecting the cell cycle, especially DNA replication. Immunoprecipitation followed by mass spectrometry has identified numerous spliceosome complex proteins associated with RPS6. Transcriptomic profiling revealed that RPS6 affects numerous alternative splicing (AS) events, and combined with RNA immunoprecipitation sequencing, revealed that minichromosome maintenance complex component 7 (MCM7) binds to RPS6, which regulates its AS and increases oncogenic activity in CCA. Targeting RPS6 with vivo phosphorodiamidate morpholino oligomer (V-PMO) significantly inhibited the growth of CCA cells, patient-derived organoids, and subcutaneous xenograft tumor. Taken together, the data demonstrate that RPS6 is an oncogenic regulator in CCA and that RPS6-V-PMO could be repositioned as a promising strategy for treating CCA.

Abstract Background To investigate the diagnostic efficacy of high-frame-rate contrast-enhanced ultrasound (H-CEUS) in differentiating between clear cell renal cell carcinoma (CCRCC) and angiomyolipoma (AML). Methods A retrospective study was performed on the clinical data of 79 patients diagnosed with CCRCC and 31 patients diagnosed with AML at the First Affiliated Hospital of Nanchang University between October 2022 and December 2023. Conventional ultrasound (US) and H-CEUS examinations were conducted on all patients prior to surgery, dynamic images were recorded from the US, and the qualitative and quantitative parameters of H-CEUS were collected. The t-test, χ² test and non-parametric Mann-Whitney test were employed to assess differences in clinical data, US characteristics, and qualitative and quantitative parameters of H-CEUS between the CCRCC and AML groups. The independent risk factors of CCRCC were identified using binary logistic regression. The receiver operator characteristic (ROC) curve was constructed to evaluate the diagnostic effectiveness of clinical + US and H-CEUS in differentiating between CCRCC and AML. Results The CCRCC group and the AML group exhibited significant differences in patient gender, operation mode, nodular echo, and nodule blood flow (χ²=11.698, -, -,=10.582; P

The prognosis for Cholangiocarcinoma (CCA) is unfavorable, necessitating the development of new therapeutic approach such as magnetic hyperthermia therapy (MHT) which is induced by magnetic nano-particle (MNPs) drug to bridge the treatment gap. Given the deep location of CCA within the abdominal cavity and proximity to vital organs, accurately predict the individualized treatment effects and safety brought by the distribution of MNPs in tumor will be crucial for the advancement of MHT in CCA. The Mimics software was used in this study to conduct three-dimensional reconstruction of abdominal computed tomography (CT) and magnetic reso-nance imaging images from clinical patients, resulting in the generation of a realistic digital geometric model representing the human biliary tract and its adjacent structures. Subsequently, The COMSOL Multiphysics software was utilized for modeling CCA and calculating the heat transfer law resulting from the multi-regional distribution of MNPs in CCA. The temperature within the central region of irregular CCA measured approximately 46°C, and most areas within the tumor displayed temperatures surpassing 41°C. The temperature of the inner edge of CCA is only 39 ∼ 41℃, however, it can be ameliorated by adjusting the local drug concentration through simulation system. For CCA with diverse morphologies and anatomical locations, the multi-regional distribution patterns of intratumoral MNPs and a slight overlap of drug distribution areas synergistically enhance intratumoral temperature while ensuring treatment safety. The present study highlights the practicality and imperative of incorporating personalized intratumoral MNPs distribution strategy into clinical practice for MHT, which can be achieved through the development of an integrated simulation system which incorporates medical image data and numerical calculations.

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a prevalent condition that often goes unrecognized in the population, and many risk factors for this disease are not well understood. Glyphosate (GLY) is one of the most commonly used herbicides worldwide, and exposure to this chemical in the environment is significant. However, studies exploring the association between GLY exposure and NAFLD remain limited. Therefore, the aim of this study was to assess the association between urinary glyphosate (uGLY) level and fatty liver index (FLI) using data from the National Health and Nutrition Examination Survey (NHANES), which includes uGLY measurements. Methods The log function of uGLY was converted and expressed as Loge(uGLY) with the constant “e” as the base and used for subsequent analysis. The association between Loge(uGLY) (the independent variable) level and FLI (the dependent variable) was assessed by multiple linear regression analysis. Smoothing curve fitting and a generalized additive model were used to assess if there was a nonlinear association between the independent and the dependent variables. A subgroup analysis was used to find susceptible individuals of the association between the independent variable and the dependent variable. Results A final total of 2238 participants were included in this study. Participants were categorized into two groups (< -1.011 and ≥ -1.011 ng/ml) based on the median value of Loge(uGLY). A total of 1125 participants had Loge(uGLY) levels ≥ -1.011 ng/ml and higher FLI. The result of multiple linear regression analysis showed a positive association between Loge(uGLY) and FLI (Beta coefficient = 2.16, 95% CI: 0.71, 3.61). Smoothing curve fitting and threshold effect analysis indicated a linear association between Loge(uGLY) and FLI [likelihood ratio(LLR) = 0.364]. Subgroup analyses showed that the positive association between Loge(uGLY) and FLI was more pronounced in participants who were female, aged between 40 and 60 years, had borderline diabetes history, and without hypertension history. In addition, participants of races/ethnicities other than (Mexican American, White and Black) were particularly sensitive to the positive association between Loge(uGLY) and FLI. Conclusions A positive linear association was found between Loge(uGLY) level and FLI. Participants who were female, 40 to 60 years old, and of ethnic backgrounds other than Mexican American, White, and Black, deserve more attention.

Cloud cover is a significant factor affecting the effectiveness of satellite-based Earth observations. Existing cloud detection algorithms primarily rely on imaging data from satellite sensors in the visible to near-infrared spectral range, making it challenging to achieve day-and-night cloud monitoring. Convolutional neural networks have shown outstanding performance in previous cloud detection algorithms due to their robust ability to extract local information. However, their inherent inductive bias limits their capacity to learn long-range semantic information. To address these challenges, we proposed SwinCloud, a U-shaped semantic segmentation network based on an enhanced Swin Transformer for cloud detection in the thermal infrared spectral range. Specifically, we augment the Swin Transformer's window attention module with a CNN-based parallel pathway to effectively model global-local information. We employ a feature fusion module before the final upsampling module in the decoder to better integrate low-level spatial information and high-level semantic information. On the Landsat-8 cloud detection dataset, our model outperforms state-of-the-art methods. When transferred to the SDGSAT-TIS cloud detection dataset, the mIOU of experiment results reaches 69.9%, demonstrating the strong transferability of SwinCloud across different sensors. We also applied SwinCloud to cloud detection in the visible bands of Landsat-8. The results demonstrated SwinCloud's generalization capability across different bands.

ObjectivesDeep Brain Stimulation (DBS) effectively treats Parkinson’s motor symptoms, but its effects on the urogenital system are debated.MethodsA research was conducted in PubMed, Embase, Cochrane Library, Web of Science, and Scopus until February 27, 2024. We primarily focused on DBS’s impact on Parkinson’s patients’ Urine storage function, voiding function, sexual function, and quality of life.ResultsOur meta-analysis included 14 studies. The main results showed that DBS resulted in fewer instances of urinary urgency (OR = 1.85, 95% CI: 1.26 to 2.70, p = 0.002) and increased maximum bladder capacity (MD = −66.10, 95% CI: −119.37 to −12.82, p = 0.02) in terms of urinary storage function. However, there were no significant differences in first desire to void and strong desire to void. In terms of voiding function, DBS showed significant improvements in maximum flow rate (MD = −0.64, 95% CI: −1.23 to −0.05, p = 0.03), post-void residual (MD = −6.79, 95% CI: 4.54 to 9.05, P

With the rapid development of the aviation industry, the impact of contrails on global warming is becoming increasingly significant. However, due to limitations in sensor technology and data sources, precise, all-weather observations of contrails remain a significant challenge. To address this, this study utilizes thermal imagery data from the SDGSAT-1 to classify newly formed contrails based on their length, creating SDGCONTR, the first three-band thermal infrared contrail dataset with a 30-meter spatial resolution. Based on this dataset, we propose an effective contrail image classification method: the SDGContrail Images Classification Method. This method builds on the existing Multi-axis Vision Transformer deep learning model. In the preprocessing stage, it employs an innovative contrail enhancement method by creating masks based on contrail brightness temperature and brightness temperature ratio percentiles in thermal infrared images. Our method achieved optimal classification performance compared to other methods, with the F1 Score of 95.2%. Using this model, we further analyzed the geographic and diurnal distribution patterns of long and short contrails, validating the mechanism of contrail formation. Our method not only enhances the ability to conduct large-scale, all-weather, and detailed contrail observations, but also sets a new standard for environmental monitoring and aviation safety.

Abstract Background Preclinical and clinical evidence indicates that proton pump inhibitors (PPIs) may indirectly diminish the microbiome diversity, thereby reducing the effectiveness of immune checkpoint inhibitors (ICIs). Conversely, recent publications have shown that PPIs could potentially enhance the response to ICIs. The precise mechanism through which PPIs modulate the ICIs remains unclear. In this study, we discovered a novel molecular function of PPIs in regulating immune invasion, specifically through inducing PD‐L1 translocation in various tumor cells. Methods C57BL/6 mice subcutaneous transplantation model is used to verify the potential efficacy of PPIs and PD‐L1 antibody. Western blotting analysis and phosphorylated chip are used to verify the alteration of PD‐L1‐related pathways after being treated with PPIs. The related gene expression is performed by qRT‐PCR and luciferase reporter analysis. We also collected 60 clinical patients diagnosed with esophageal cancer or reflux esophagitis and then detected the expression of PD‐L1 in the tissue samples by immunohistochemistry. Results We observed that the IC50 of tumor cells in response to PPIs was significantly higher than that of normal epithelial cells. PPIs significantly increased the expression of PD‐L1 on cell membrane at clinically relevant concentrations. Furthermore, pre‐treatment with PPIs appeared to synergize the efficiency of anti‐PD‐L1 antibodies in mouse models. However, PPI administration did not alter the transcription or total protein level of PD‐L1 in multiple tumor cells. Using a phosphorylated protein chip, we identified that PPIs enhanced the phosphorylation of GSK3β, then leading to PD‐L1 protein translocation to the cell membranes. The capacity of PPIs to upregulate PD‐L1 was negated following GSK3β knockout. Furthermore, our clinical data showed that the PPIs use resulted in increased PD‐L1 expression in esophageal cancer patients. Conclusion We mainly address a significant and novel mechanism that the usage of PPIs could directly induce the expression of PD‐L1 by inducing GSK3β phosphorylation and facilitate primary tumor progression and metastasis.

Visible Infrared Reidentification (VI-ReID) on vessels is an important component task in in the application of UAV remote sensing data, aiming to retrieve images with the same identity as a given vessel by retrieving it from image libraries containing vessels of different modalities. One of its main challenges is the huge modality difference between visible (VIS) and infrared (IR) images. Some state-of-the-art methods try to design complex networks or generative methods to mitigate the modality differences, ignoring the highly nonlinear relationship between the two modalities. To solve this problem, we propose a nonlinear Third-Modality Generator (TMG) to generate third-modality images to collaborate the original two modalities to learn together. In addition, in order to make the network focus on the image focus area and get rich local information, a Multidimensional Attention Guidance (MAG) module is proposed to guide the attention in both channel and spatial dimensions. By integrating TMG, MAG and the three designed losses (Generative Consistency Loss, Cross Modality Loss, and Modality Internal Loss) into an end-to-end learning framework, we propose a network utilizing the third-modality to collaborate learning, called third-modality collaborative network (TMCN), which has strong discriminative ability and significantly reduces the modality difference between VIS and IR. In addition, due to the lack of vessel data in the VI-ReID task, we have collected an airborne vessel cross-modality reidentification dataset (AVC-ReID) to promote the practical application of the VI-ReID task. Extensive experiments on the AVC-ReID dataset show that the proposed TMCN outperforms several other state-of-the-art methods.

Automatic driving technology has high accuracy and real-time requirements for pedestrian identification and localization. Pedestrian detection is a basic and necessary function in vision-based pedestrian detection systems and collision warning, which can effectively avoid traffic accidents and improve road driving safety to a certain extent. In this paper, a lightweight solution based on the FCOS-DEFPN model is proposed for real-time pedestrian detection. Based on the FCOS model, this paper proposes the FCOS-DEFPN model, which achieves the lightweight of the network by replacing the ResNet50 backbone network with the MobilenetV3 network and using the depth separable convolution instead of the ordinary convolution for parameter compression. While maintaining the detection accuracy, this paper introduces data enhancement methods such as Random Erasing and Morsia to simulate pedestrian occlusion and small target scenarios to improve the robustness of the model. For the pedestrian occlusion scenario, this paper introduces a lightweight attention network ECA, which helps to extract pedestrian features better. For small-target multi-scale pedestrians, the DEFPN feature pyramid network is proposed, which acquires feature information at multiple scales by attentional fusion of feature layers at different scales from top-down, bottom-up, and front-back. The experimental results show that the proposed model is enhanced in terms of detection accuracy for occluded and small-target pedestrians, and satisfies real-time pedestrian detection under the premise of robustness in complex scenes.

Pedestrian trajectory prediction is a key technology in surveillance systems and autonomous driving. However, due to the high uncertainty and dynamic spatial-temporal dependence of pedestrian movement, timely and accurate pedestrian trajectory prediction, especially long-term prediction, is still an open challenge. However, the existing models lack an effective modeling method for temporal dependence and spatial interaction modeling. To solve these problems, in this paper, we propose a novel paradigm of Sparse Transformer Networks with Spatial-Temporal Graph (STGSTN), which captures complex spatial-temporal interactions by stacking spatial-temporal Transformer blocks and improves the accuracy of pedestrian trajectory prediction by combining dynamic spatial dependence and long-range temporal dependence. We propose a new variant of sparse spatial transformer combined with graph neural networks, which uses the self-attention mechanism to dynamically model spatial dependencies to capture the state of pedestrian movement. The multi-head attention mechanism is also used to jointly model various patterns of spatial dependence. In addition, the sparse temporal transformer is used to learn the sparse attention map for time modeling, and then the long-range temporal dependence between pedestrians is modeled. Compared with the existing work, STGSTN can effectively and efficiently train long-range spatial-temporal dependencies. Experimental results show that STGSTN is competitive with state-of-the-art techniques on the ETH-UCY dataset, especially for long-term prediction. The performance of STGSTN may be limited by the quality of the training data, and its generalization to highly dynamic or crowded environments remains a challenge. Future work will focus on addressing these limitations and exploring additional contextual information to further enhance prediction accuracy.

With the development of Earth observation technology, it becomes easier and easier to acquire multimodal image data at the same time. To improve the performance of a multimodal remote-sensing detection algorithm, a new fusion feature optimization detection network is proposed. The method is designed to solve the problem of performance degradation caused by the unreliability of single-modal data in multimodal remote-sensing data. The key to obtain high-quality fusion features from multimodal data with interference is to suppress single-modal redundant features and fully integrate multimodal features. The proposed method mainly includes two improvements. First, a novel joint expression optimization module is designed to enhance the target features and suppress the redundant and interference features that affect the fusion effect. In addition, we propose a novel specific information enhancement module to further enhance the discriminative feature information of targets within each modal image. Experiments on the DroneVehicle dataset show that our proposed method is state of the art on this dataset.

Abstract Background Paracetamol induces hepatotoxicity and subsequent liver injury, which may increase the risk of liver cancer, but epidemiological evidence remains unclear. We conducted this study to evaluate the association between paracetamol use and the risk of liver cancer. Methods This prospective study included 464,244 participants free of cancer diagnosis from the UK Biobank. Incident liver cancer was identified through linkage to cancer and death registries and the National Health Service Central Register using the International Classification of Diseases (ICD)-10 codes (C22). An overlap-weighted Cox proportional hazards model was utilized to calculate the hazard ratio (HR) and 95% confidence interval (CI) for the risk of liver cancer associated with paracetamol use. The number needed to harm (NNH) was calculated at 10 years of follow-up. Results During a median of 12.6 years of follow-up, 627 cases of liver cancer were identified. Paracetamol users had a 28% higher risk of liver cancer than nonusers (HR 1.28, 95% CI 1.06–1.54). This association was robust in several sensitivity analyses and subgroup analyses, and the quantitative bias analysis indicated that the result remains sturdy to unmeasured confounding factors (E-value 1.88, lower 95% CI 1.31). The NNH was 1106.4 at the 10 years of follow-up. Conclusion The regular use of paracetamol was associated with a higher risk of liver cancer. Physicians should be cautious when prescribing paracetamol, and it is recommended to assess the potential risk of liver cancer to personalize the use of paracetamol.

Hydrothermal carbonization (HTC) is an efficient method for converting biomass into biochar. Hydrochar contains catalytic components such as alkali and alkaline earth metals (AAEMs); however, the mechanisms by which highly active metals such as potassium (K) and sodium (Na) catalyze the conversion of small carbon–water compounds into hydrochar in hydrothermal environments remain unclear. In this study, glucose was used as a small molecule model, and Na+ and K+ were used as catalysts to investigate the catalytic reaction mechanism during the hydrothermal process using density functional theory (DFT). In the presence of different ions at various binding sites, glucose isomerizes into fructose, which subsequently undergoes three consecutive dehydration reactions to form 5-hydroxymethylfurfural (HMF). The results indicate that the catalytic effectiveness of Na+ and K+ in the isomerization of glucose to fructose is optimal when interacting with specific oxygen sites on glucose. For Na+, the interaction with the O1 and O2 oxygens provides the lowest reaction barrier of 37.16 kcal/mol. For K+, the most effective interactions are with the O3 and O4 oxygens and the O5 and O6 oxygens, resulting in reduced reaction barriers of 54.35 and 31.50 kcal/mol, respectively. Dehydration of fructose to HMF catalyzed by Na+ ions, the catalytic effectiveness at different positions is ranked as O5O6 > O1O5, whereas for K+, the ranking is O1O5 > O5O6. This study explores the catalytic effects of Na+ and K+ at different binding sites on the hydrothermal reactions of glucose at the atomic level, offering theoretical support for designing catalysts for the HTC of sludge.

Due to the assumption of acceleration variation in traditional step-by-step integration methods such as Newmark, sufficiently small time steps are required to ensure numerical stability and accuracy in dynamic systems. In contrast, the state-space approach, based on piecewise interpolation of discrete load functions, does not rely on predetermined acceleration assumptions and has demonstrated high efficiency in terms of stability and accuracy. The original state-space method requires the calculation of the inverse of the structural mass in the transition matrix. However, when a lumped mass matrix is used, this computation renders the entire mass matrix singular, resulting in an invalid solution expression. To address this issue, this study proposes an improved state-space approach for the transient analysis of large-scale structural systems with local nonlinearities. In this approach, a nonlinear force corrector is introduced as an external force term applied to the linear elastic system to account for the nonlinear behavior of locally yielding components. Consequently, the original nonlinear dynamic system can be transformed into an equivalent linear elastic transient system. Furthermore, based on the lumped mass matrix, a first-order ordinary differential state-space equation for such an equivalent linear elastic transient system is derived. Simulation results from three transient system examples show that the state-space approach outperforms the Newmark method in terms of accuracy and stability for dynamic systems characterized by high frequency and low damping. The prediction results show that the state-space approach appears to be insignificantly affected by the choice of the consistent or lumped mass matrix. The numerical results show that the root-mean-square errors between the consistent and lumped matrices in the top displacement time histories of a 15-storey plane frame under various seismic intensities are all less than 1%, and in the base reaction time histories responses the discrepancies are only about 0.5%, indicating that the use of lumped mass matrices is quite reliable. When many nodes or degrees of freedom have no assigned mass, the dimensionality of the state-space equation can be significantly reduced using the lumped mass approach. Therefore, the simulation of large-scale systems can be simplified by employing the improved state-space approach with lumped mass matrices, yielding results nearly identical to those obtained using traditional methods. In conclusion, the improved state-space approach has great potential for the simulation of transient behavior in large-scale systems with local nonlinearities.

IntroductionPancreatic cancer (PC) is a malignancy with poor prognosis. This investigation aimed to determine the relevant genes that affect the prognosis of PC and investigate their relationship with immune infiltration.Methods: First, we acquired PC single-cell chip data from the GEO database to scrutinize dissimilarities in immune cell infiltration and differential genes between cancerous and adjacent tissues. Subsequently, we combined clinical data from TCGA to identify genes relevant to PC prognosis. Employing Cox and Lasso regression analyses, we constructed a multifactorial Cox prognostic model, which we subsequently confirmed. The prognostic gene expression in PC was authenticated using RT-PCR. Moreover, we employed the TIMER online database to examine the relationship between the expression of prognostic genes and T and B cell infiltration. Additionally, the expression of GPRC5A and its correlation with B cells infiltration and patient prognosis were ascertained in tissue chips using multiple immune fluorescence staining.ResultsThe single-cell analysis unveiled dissimilarities in B-cell infiltration between cancerous and neighboring tissues. We developed a prognostic model utilizing three genes, indicating that patients with high-risk scores experienced a more unfavorable prognosis. Immune infiltration analysis revealed a significant correlation among YWHAZ, GPRC5A, and B cell immune infiltration. In tissue samples, GPRC5A exhibited substantial overexpression and a robust association with an adverse prognosis, demonstrating a positive correlation with B cell infiltration.ConclusionGPRC5A is an independent risk factor in PC and correlated with B cell immune infiltration in PC. These outcomes indicated that GPRC5A is a viable target for treating PC.

Abstract Background Cholangiocarcinoma (CCA) is a highly aggressive malignant tumor, and its diagnosis is still a challenge. This study aimed to identify a novel bile marker for CCA diagnosis based on proteomics and establish a diagnostic model with deep learning. Methods A total of 644 subjects (236 CCA and 408 non-CCA) from two independent centers were divided into discovery, cross-validation, and external validation sets for the study. Candidate bile markers were identified by three proteomics data and validated on 635 clinical humoral specimens and 121 tissue specimens. A diagnostic multi-analyte model containing bile and serum biomarkers was established in cross-validation set by deep learning and validated in an independent external cohort. Results The results of proteomics analysis and clinical specimen verification showed that bile clusterin (CLU) was significantly higher in CCA body fluids. Based on 376 subjects in the cross-validation set, ROC analysis indicated that bile CLU had a satisfactory diagnostic power (AUC: 0.852, sensitivity: 73.6%, specificity: 90.1%). Building on bile CLU and 63 serum markers, deep learning established a diagnostic model incorporating seven factors (CLU, CA19-9, IBIL, GGT, LDL-C, TG, and TBA), which showed a high diagnostic utility (AUC: 0.947, sensitivity: 90.3%, specificity: 84.9%). External validation in an independent cohort (n = 259) resulted in a similar accuracy for the detection of CCA. Finally, for the convenience of operation, a user-friendly prediction platform was built online for CCA. Conclusions This is the largest and most comprehensive study combining bile and serum biomarkers to differentiate CCA. This diagnostic model may potentially be used to detect CCA.

Abstract Aim The purpose of this study was to explore the association of weight-adjusted-waist index (WWI) with non-alcoholic fatty liver disease (NAFLD) and liver fibrosis. Methods A cross-sectional study including 6587 participants was conducted in the National Health and Nutrition Examination Survey (NHANES). Multiple linear regression was used to validate the association of WWI with NAFLD and liver fibrosis, and smoothed curve fitting and threshold effect models were used to validate non-linear relationships. Subgroup analyses were used to verify the stability of the relationship between the independent and dependent variables in different populations. Results There was a positive association of WWI with NAFLD and liver fibrosis. In the model adjusted for all covariates, the effect values of WWI with NAFLD and liver fibrosis were (OR = 3.44, 95% CI: 3.09–3.82) and (OR = 2.40, 95% CI: 2.05–2.79), respectively. This positive correlation became more significant as WWI increased when WWI was presented in quartiles (P for trend

In recent years, hybrid fuzzing has garnered significant attention by combining the benefits of both fuzzing and concolic execution. However, existing hybrid fuzzing techniques face problems such as the performance overhead of concolic execution and low code coverage in real-world programs. In this study, we observed that concolic execution often falls into repetitive loop structures when analyzing inputs generated by fuzzing, leading to the construction of redundant constraint expressions, which severely impacts its efficiency. Based on this observation, we design a test case trimming approach to remove input fields related to repetitive loop structures. We propose a lightweight taint analysis technique to infer input’s grammatical structure and construct a hierarchical grammar tree. Then, through delta debugging, we identify nodes that do not affect the code coverage and remove the corresponding input fields. We implement a prototype ScissorFuzz. Experimental results show that our approach brings an average of 8.2% improvement in code coverage and triggers 115 more crashes compared to the state-of-the-art hybrid fuzzing technique QSYM. By eliminating redundant fields, our approach reduces the resource and time overhead associated with concolic execution during input processing, thereby enhancing the efficiency of hybrid fuzzing.

Objective: To investigate the effect of ursolic acid extracted from Hippophae rhamnoides L. on the expression of key proteins of hepatic farnesoid X receptor (FXR) signaling pathway in rats with alcoholic liver injury. Methods: The 6-week-old SPF SD rats were divided randomly into 4 groups: The normal control group, the alcohol model group, the ursolic acid control group and the ursolic acid+alcohol group, 9 mice in each group. The rats were administered by intragastric administration for eight consecutive weeks. The pathological changes of hepatic tissues in rats was observed by hematoxylin eosin (H&E) staining. The activities of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and the content of total bile acid (TBA) in serum, and the content of triglyceride (TG) and total cholesterol (TC) in liver of rats were tested. The content of the serum cytokine including tumor necrosis factor-α (TNF-α), interleukin 1β (IL-1β), and interleukin 10 (IL-10) was detected by enzyme linked immunosorbent assay (ELISA). The expression of FXR signal pathway related proteins in liver tissue of rats was detected by Western blotting. Results: Compared with the normal control group, there were fat vacuoles of different sizes and a large number of inflammatory cells in the liver of the alcohol model group. The activities of ALT, AST, the levels of TNF-ɑ, IL-1β, the content of TBA in serum and the content of TG, TC in liver were increased significantly (P

To gain an insight into the lattice and band structure of blue perovskite NC emitters, doping organic cations (FA+) reduces nonradiative recombination at room temperature. The FA+ significantly decreases exciton quenching and phonon energy scattering via manipulating hot carriers and electron filling in band structure.

Pancreatic β-cells are naturally prone to ER stress due to their role in insulin production and secretion. Here, the authors show that chronic ER stress adaptive exhaustion results in an irreversible loss of β-cell function leading to T1D pathogenesis

This paper releases a rotated SAR ship detection dataset, named Rotated Ship Detection Dataset in SAR Images (RSDD-SAR), to address the problem that the existing rotated SAR ship detection datasets are not enough to meet the requirements of algorithm development and practical application. This dataset consists of 84 scenes of GF-3 data slices, 41 scenes of TerraSAR-X data slices, and 2 scenes of large uncropped images, including 7,000 slices and 10,263 ship instances of multi-observing modes, multi-polarization modes, and multi-resolutions. This dataset is effectively annotated by automatic annotation with manual correction. Meanwhile, experiments were conducted for several popular rotated object detection algorithms in optical remote sensing images and rotated ship detection algorithms in SAR images, and the one-stage algorithm S2ANet achieved the highest average precision of 90.06%. When using this dataset, scholars can reference the experimental results, and corresponding analysis can be used. Finally, this paper conducts generalization ability testing experiments on other datasets and large uncropped images to analyze and discuss the performance of the model trained on RSDD-SAR. The experimental results show that the model trained on RSDD-SAR has decent performance and confirms the application value of this dataset. The RSDD-SAR dataset is available at https://radars.ac.cn/web/data/getData?dataType=SDD-SAR.

Proton pump inhibitors (PPIs) are the most used acid-inhibitory drugs, with a wide range of applications in the treatment of various digestive diseases. However, recently, there has been a growing number of digestive complications linked to PPIs, and several studies have indicated that the intestinal flora play an important role in these complications. Therefore, developing a greater understanding of the role of the gut microbiota in PPI-related digestive diseases is essential. Here, we summarize the current research on the correlation between PPI-related digestive disorders and intestinal flora and establish the altered strains and possible pathogenic mechanisms of the different diseases. We aimed to provide a theoretical basis and reference for the future treatment and prevention of PPI-related digestive complications based on the regulation of the intestinal microbiota.

Obesity has been regarded as one of the major health problems worldwide. Studies demonstrated that ursolic acid (UA) can significantly ameliorate the progress of obesity. However, whether the effect of UA on obesity depends on the regulation of gut microbiota and metabolism is uncertain. To investigate the regulatory role of UA in obese mice from the perspective of intestinal microbiome and metabolomics analyses, an obese mice model was established with a high-fat diet, and the effect of UA on obesity was evaluated. The alterations of gut microbiota and metabolism related to obesity were evaluated by bioinformatic analysis. The results of the gut microbiota analysis showed that UA intervention could shift the Firmicutes to Bacteroidetes ratio at the phylum level and increase in the genera of Lactobacillus, Bacteroides, and Akkermansia. Additionally, metabolomics analysis showed that the beneficial influence of UA on obesity partly depended on amino acid metabolism. The current study demonstrated the roles of UA in the anti-obesity process, which depends in part on alterations in the gut microbiota and metabolism. Therefore, our findings highlight the potential therapeutic effect of UA on the improvement of diet-induced obesity in humans.

Introduction: The incidence of cholangiocarcinoma (CCA) has increased worldwide in recent years. Given the poor prognosis associated with the current management approach of CCA, new therapeutic agents are warranted to improve the prognosis of this patient population.Methods: In this study, we extracted five cardiac glycosides (CGs) from natural plants: digoxin, lanatoside A, lanatoside C, lanatoside B, and gitoxin. Follow-up experiments were performed to assess the effect of these five extracts on cholangiocarcinoma cells and compounds with the best efficacy were selected. Lanatoside C (Lan C) was selected as the most potent natural extract for subsequent experiments. We explored the potential mechanism underlying the anticancer activity of Lan C on cholangiocarcinoma cells by flow cytometry, western blot, immunofluorescence, transcriptomics sequencing, network pharmacology and in vivo experiments.Results: We found that Lan C time-dependently inhibited the growth and induced apoptosis of HuCCT-1 and TFK-1 cholangiocarcinoma cells. Besides Lan C increased the reactive oxygen species (ROS) content in cholangiocarcinoma cells, decreased the mitochondrial membrane potential (MMP) and resulted in apoptosis. Besides, Lan C downregulated the protein expression of STAT3, leading to decreased expression of Bcl-2 and Bcl-xl, increased expression of Bax, activation of caspase-3, and initiation of apoptosis. N-acetyl-L-cysteine (NAC) pretreatment reversed the effect of Lan C. In vivo, we found that Lan C inhibited the growth of cholangiocarcinoma xenografts without toxic effects on normal cells. Tumor immunohistochemistry showed that nude mice transplanted with human cholangiocarcinoma cells treated with Lan C exhibited decreased STAT3 expression and increased caspase-9 and caspase-3 expression in tumors, consistent with the in vitro results.Conclusion: In summary, our results substantiates that cardiac glycosides have strong anti-CCA effects. Interestingly the biological activity of Lan C provides a new anticancer candidate for the treatment of cholangiocarcinoma.

Summary: Background: Mechanical lithotripsy produces stone fragments that are not easily detected by cholangiography and is a potential cause of recurrence of common bile duct stones (CBDS). This study aims to clarify whether 100 ml saline irrigation after mechanical lithotripsy reduces the recurrent rate of CBDS. Methods: In this randomized controlled trial performed at the Surgical Endoscopy Center, the First Hospital of Lanzhou University between May 10, 2019, and Dec 31, 2020, patients undergoing endoscopic mechanical lithotripsy were randomly assigned to receive saline irrigation (study group) or no irrigation (control group). The saline irrigation was given 100 ml saline pulse irrigation after cholangiography showed no residual stones. Patients were followed up for at least 24 months after endoscopic stone removal to assess the recurrence of CBDS. This study was registered with ClinicalTrials.gov (NCT03937037). Findings: During the median follow-up period of 35.6 months (interquartile range, 26.0–40.7), 43 of the 180 patients had stone recurrence (24%). The frequency of recurrence of CBD stones was 12.22% in the saline irrigation group and 35.56% in the control group, with a difference of 23.33% between the two groups (95% confidence interval [CI], 11.35%–35.32%, p

Multiple object tracking (MOT) plays an important role in intelligent video-processing tasks, which aims to detect and track all moving objects in a scene. Joint-detection-and-tracking (JDT) methods are thriving in MOT tasks, because they accomplish the detection and data association in a single stage. However, the slow training convergence and insufficient data association limit the performance of JDT methods. In this paper, the anchor-based query (ABQ) is proposed to improve the design of the JDT methods for faster training convergence. By augmenting the coordinates of the anchor boxes into the learnable queries of the decoder, the ABQ introduces explicit prior spatial knowledge into the queries to focus the query-to-feature learning of the JDT methods on the local region, which leads to faster training speed and better performance. Moreover, a new template matching (TM) module is designed for the JDT methods, which enables the JDT methods to associate the detection results and trajectories with historical features. Finally, a new transformer-based MOT method, ABQ-Track, is proposed. Extensive experiments verify the effectiveness of the two modules, and the ABQ-Track surpasses the performance of the baseline JDT methods, TransTrack. Specifically, the ABQ-Track only needs to train for 50 epochs to achieve convergence, while that for TransTrack is 150 epochs.

Deep-learning-based SAR ship classification has become a research hotspot in the military and civilian fields and achieved remarkable performance. However, the volume of available SAR ship classification data is relatively small, meaning that previous deep-learning-based methods have usually struggled with overfitting problems. Moreover, due to the limitation of the SAR imaging mechanism, the large intraclass diversity and small interclass similarity further degrade the classification performance. To address these issues, we propose a label smoothing auxiliary classifier generative adversarial network with triplet loss (LST-ACGAN) for SAR ship classification. In our method, an ACGAN is introduced to generate SAR ship samples with category labels. To address the model collapse problem in the ACGAN, the smooth category labels are assigned to generated samples. Moreover, triplet loss is integrated into the ACGAN for discriminative feature learning to enhance the margin of different classes. Extensive experiments on the OpenSARShip dataset demonstrate the superior performance of our method compared to the previous methods.

Abstract Background Renal cell carcinoma (RCC) is a heterogeneous group of kidney cancers. Renal capsule invasion is an essential factor for RCC staging. To develop radiomics models from CT images for the preoperative prediction of capsule invasion in RCC patients. Methods This retrospective study included patients with RCC admitted to the Chongqing University Cancer Hospital (01/2011–05/2019). We built a radiomics model to distinguish patients grouped as capsule invasion versus non-capsule invasion, using preoperative CT scans. We evaluated effects of three imaging phases, i.e., unenhanced phases (UP), corticomedullary phases (CMP), and nephrographic phases (NP). Five different machine learning classifiers were compared. The effects of tumor and tumor margins are also compared. Five-fold cross-validation and the area under the receiver operating characteristic curve (AUC) are used to evaluate model performance. Results This study included 126 RCC patients, including 46 (36.5%) with capsule invasion. CMP exhibited the highest AUC (AUC = 0.81) compared to UP and NP, when using the forward neural network (FNN) classifier. The AUCs using features extracted from the tumor region were generally higher than those of the marginal regions in the CMP (0.81 vs. 0.73) and NP phase (AUC = 0.77 vs. 0.76). For UP, the best result was obtained from the marginal region (AUC = 0.80). The robustness analysis on the UP, CMP, and NP achieved the AUC of 0.76, 0.79, and 0.77, respectively. Conclusions Radiomics features in renal CT imaging are associated with the renal capsule invasion in RCC patients. Further evaluation of the models is warranted.

ObjectiveImmunity and inflammation are key mediators of carcinoma development, invasion and metastasis. However, it remains unknown whether the systemic immune-inflammation index (SII) can be used as a prognostic indicator for cholangiocarcinoma. In this study, we investigated the association and predictive value of the SII with the prognosis of advanced perihilar cholangiocarcinoma (pCCA) after interventional therapy.MethodsA retrospective cohort of patients with advanced pCCA treated with interventional therapy at the First Hospital of Shanxi Medical University enrolled in this study from January 2019 through January 2021 was examined. Cox regression models were used to analyze the relationship between the SII and overall survival (OS) of patients with advanced pCCA. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive power of SII.ResultsPreoperative SII was positively associated with poor OS of pCCA after interventional therapy, with corresponding hazard ratios (HR) of 1.57 (95% CI: 1.17 - 2.10) for an inter-quartile range increase. The predictive power of SII was higher than that of other inflammation indexes based on ROC analysis (AUC = 0.835 [95% CI (0.731 - 0.940)]). The optimal cut-off values, sensitivity, and specificity with SII were 700, 0.774 and 0.846, respectively. An SII ≥ 700 was significantly associated with lymph node metastasis and high carbohydrate antigen199 (CA199) level. In multivariate analyses, total bilirubin, carbohydrate antigen 199, vascular invasion, and SII independently predicted overall survival (P < 0.05).ConclusionThis is the first study demonstrating that an increase in the SII is associated with poor advanced pCCA prognosis, and could serve as a reliable prognostic indicator of pCCA after interventional therapy.

Abstract Background We previously reported that expression of a miR-138 mimic or knockdown of SIN3A in primary cultures of cystic fibrosis (CF) airway epithelia increased ΔF508-CFTR mRNA and protein levels, and partially restored CFTR-dependent chloride transport. Global mRNA transcript profiling in ΔF508-CFBE cells treated with miR-138 mimic or SIN3A siRNA identified two genes, SYVN1 and NEDD8, whose inhibition significantly increased ΔF508-CFTR trafficking, maturation, and function. Little is known regarding the dynamic changes in the CFTR gene network during such rescue events. We hypothesized that analysis of condition-specific gene networks from transcriptomic data characterizing ΔF508-CFTR rescue could help identify dynamic gene modules associated with CFTR biogenesis. Methods We applied a computational method, termed M-module, to analyze multiple gene networks, each of which exhibited differential activity compared to a baseline condition. In doing so, we identified both unique and shared gene pathways across multiple differential networks. To construct differential networks, gene expression data from CFBE cells were divided into three groups: (1) siRNA inhibition of NEDD8 and SYVN1; (2) miR-138 mimic and SIN3A siRNA; and (3) temperature (27 °C for 24 h, 40 °C for 24 h, and 27 °C for 24 h followed by 40 °C for 24 h). Results Interrogation of individual networks (e.g., NEDD8/SYVN1 network), combinations of two networks (e.g., NEDD8/SYVN1 + temperature networks), and all three networks yielded sets of 1-modules, 2-modules, and 3-modules, respectively. Gene ontology analysis revealed significant enrichment of dynamic modules in pathways including translation, protein metabolic/catabolic processes, protein complex assembly, and endocytosis. Candidate CFTR effectors identified in the analysis included CHURC1, GZF1, and RPL15, and siRNA-mediated knockdown of these genes partially restored CFTR-dependent transepithelial chloride current to ΔF508-CFBE cells. Conclusions The ability of the M-module to identify dynamic modules involved in ΔF508 rescue provides a novel approach for studying CFTR biogenesis and identifying candidate suppressors of ΔF508.

Abstract Sparse LiDAR depth completion is a beneficial task for many robotic applications. It commonly generates a dense depth prediction from a sparse depth map and its corresponding aligned RGB image. This image‐guided depth completion task mainly has two challenges: sparse data processing and multi‐modality data fusion. In this letter, they are dealt with by two novel solutions: (1) To efficiently process sparse depth input, a Depth Aware Non‐local Convolution (DAN‐Conv) is proposed. It augments the spatial sampling locations of a convolution operation. Specifically, DAN‐Conv constructs a non‐local neighbourhood relationship by exploiting the intrinsic correlation among observable depth pixels. In particular, it can readily replace standard convolution without introducing additional network parameters. (2) A Symmetric Co‐attention Module (SCM) is proposed to fuse and enhance features from depth and image domain. It estimates the importance of complementary features by the co‐attention mechanism. Finally, a neural network built on DAN‐Conv and SCM is proposed. It achieves competitive performance on the challenging KITTI depth completion benchmark. Comparing to approaches with approximate accuracy, this lightweight network requires significantly fewer learnable parameters.

Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor that originates from bile duct’s epithelial cells and is usually characterized by insidious symptoms and poor prognosis. Cinobufotalin (CB), an active ingredient obtained from the Traditional Chinese Medicine ChanSu, is purported to exhibit a wide range of antitumorigenic activities. However, the mechanism by which it achieves such pharmacological effects remains elusive. Here, we disclosed the mechanism of action by which CB inhibits ICC cells. Initial experiments revealed that the proliferation of RBE and HCCC-9810 cells was significantly inhibited by CB with IC50 values of 0.342 μM and 0.421 μM respectively. CB induced the expression of caspase-3 subsequently leading to the apoptosis of ICC cells. Phosphoproteomics revealed that the phosphorylation of many proteins associated with DNA damage response increased. Kinase-substrate enrichment analysis revealed that ATM was activated after CB treatment, while CDK1 was inactivated. Activated ATM increased p-CHK2-T68 and p-p53-S15, which promoted the expression of FAS, DR4 and DR5 and triggered cell apoptosis. In summary, this work reveals the role of CB in inducing DNA damage and cell apoptosis involved in the activation of the ATM/CHK2/p53 signaling pathway, and indicates that CB may serve as a chemotherapeutic drug candidate for ICC treatment.

BackgroundPrevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential.ObjectiveAn XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM.MethodsAll data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort.ResultsA total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa].ConclusionsXGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.

Recently, unsupervised domain adaptation (UDA) has attracted increasing attention to address the domain shift problem in the semantic segmentation task. Although previous UDA methods have achieved promising performance, they still suffer from the distribution gaps between source and target domains, especially the resolution discrepancy in the remote sensing images. To address this problem, this study designs a novel end-to-end semantic segmentation network, namely, Super-Resolution Domain Adaptation Network (SRDA-Net). SRDA-Net can simultaneously achieve the super-resolution task and the domain adaptation task, thus satisfying the requirement of semantic segmentation for remote sensing images, which usually involve various resolution images. The proposed SRDA-Net includes three parts: a super-resolution and segmentation (SRS) model, which focuses on recovering high-resolution image and predicting segmentation map, a pixel-level domain classifier (PDC) for determining which domain the pixel belongs to, and an output-space domain classifier (ODC) for distinguishing which domain the pixel contribution is from. By jointly optimizing SRS with two classifiers, the proposed method can not only eliminate the resolution difference between source and target domains but also improve the performance of the semantic segmentation task. Experimental results on two remote sensing datasets with different resolutions demonstrate that SRDA-Net performs favorably against some state-of-the-art methods in terms of accuracy and visual quality. Code and models are available at https://github.com/tangzhenjie/SRDA-Net.

Silicon-based Lidar is an ideal way to reduce the volume of the Lidar and realize monolithic integration. It removes the moving parts in the conventional device and realizes solid-state beam steering. The advantages of low cost, small size, and high beam steering speed have attracted the attention of many researchers. In order to facilitate researchers to quickly understand the research progress and direction, this paper mainly describes the research progress of silicon-based integrated Lidar, including silicon-based optical phased array Lidar, silicon-based optical switch array Lidar, and continuous frequency-modulated wave Lidar. In addition, we also introduced the scanning modes and working principles of other kinds of Lidar, such as the Micro-Electro-Mechanical System, mechanical Lidar, etc., and analyzed the characteristics of the Lidars above. Finally, we summarized this paper and put forward the future expectations of silicon-based integrated Lidar.

In order to improve energy storage efficiency and promote the early achievement of global carbon neutrality goals, this paper proposes a spherical thermal storage unit filled with a composite phase change material (CPCM) comprising myristic acid (MA) and expanded graphite (EG). The effects of EG content and Stefan number (Ste) on the melting performance were investigated through a combination of experiments and numerical simulations. The results show that an increase in EG content (especially for ≥4 wt.% EG) leads to a temperature profile that assumes a concentric ring shape, while the melting rate increases with an increase in both the EG mass fraction and the Ste number. Compared to pure MA, the time required to complete melting was reduced by 82.2%, 85.6%, and 88.0% at EG contents of 4 wt.%, 5 wt.%, and 6 wt.%, respectively. Notably, the Ste value has a greater effect on melting when the EG content is ≤3 wt.%. The optimal EG content in the spherical cell was determined to be 4 wt.%, and a dimensionless analysis established a general correlation between the liquid mass fraction and the Fo, Ste, and Gr numbers.

Abstract It has been recently reported that CD38 expressed on tumor cells of multiple murine and human origins could be upregulated in response to PD-L1 antibody therapy, which led to dysfunction of tumor-infiltrating CD8+ T immune cells due to increasing the production of adenosine. However, the role of tumor expressed-CD38 on neoplastic formation and progression remains elusive. In the present study, we aimed to delineate the molecular and biochemical function of the tumor-associated CD38 in lung adenocarcinoma progression. Our clinical data showed that the upregulation of tumor-originated CD38 was correlated with poor survival of lung cancer patients. Using multiple in vitro assays we found that the enzymatic activity of tumor expressed-CD38 facilitated lung cancer cell migration, proliferation, colony formation, and tumor development. Consistently, our in vivo results showed that inhibition of the enzymatic activity or antagonizing the enzymatic product of CD38 resulted in the similar inhibition of tumor proliferation and metastasis as CD38 gene knock-out or mutation. At biochemical level, we further identified that cADPR, the mainly hydrolytic product of CD38, was responsible for inducing the opening of TRPM2 iron channel leading to the influx of intracellular Ca2+ and then led to increasing levels of NRF2 while decreasing expression of KEAP1 in lung cancer cells. These findings suggested that malignant lung cancer cells were capable of using cADPR catalyzed by CD38 to facilitate tumor progression, and blocking the enzymatic activity of CD38 could be represented as an important strategy for preventing tumor progression.

Seawater intrusion is a major concern commonly found in coastal aquifers worldwide. Because of the intense aquifer exploitation and land-based marine aquaculture in the coastal area of Beihai City, Guangxi Zhuang Autonomous Region, China, numerous underground aquifers in this area have been affected by seawater intrusion. However, the microbial communities in freshwater aquifers and their response to seawater intrusion are still unclear. In this study, groundwater from three aquifers was collected from three monitoring sites at different distances from the coastline in the coastal area of Beihai City, and the hydrochemical characteristics of these groundwater samples and the structure of the associated microbial communities were analyzed. The Cl− concentration of the samples indicated that seawater intrusion had occurred in the research area up to 1.5 km away from the coastline, but the monitoring site 2 km away from the coastline had yet to be affected. Statistical analysis showed that the bacterial communities in different groundwater aquifers were significantly correlated with the Cl− concentration, thereby suggesting that the extent of seawater intrusion might be one of the primary factors shaping bacterial composition in groundwater of this area, but the composition and distribution of archaea did not show a significant response to seawater intrusion and presented no apparent correlation with the Cl− concentration. α-, γ-Proteobacteria and Bacteroidota were the dominant bacterial lineages, accounting for about 58–95% of the bacterial communities. Meanwhile, the predominant archaeal taxa were mainly composed of Crenarchaeota, Nanoarchaeota, and Thermoplasmatota, as accounting for 83–100%. Moreover, there was significant spatial heterogeneity of microbial communities in the aquifers affected by varying degrees of seawater intrusion. The microbial communities inhabiting the unconfined aquifer were influenced by the geochemical fluctuation caused by seawater infiltration from land-based marine aquaculture ponds and the diffusion of eutrophic surface water. In contrast, changes in microbial community structure in the confined aquifers were closely related to the environmental gradient caused by different degrees of seawater intrusion. In addition, we also found that the tidal cycle did not significantly affect the structure of microbial communities inhabiting confined aquifers that had been long affected by seawater intrusion.

BACKGROUND Multiple islet autoantibodies (AAbs) predict the development of type 1 diabetes (T1D) and hyperglycemia within 10 years. By contrast, T1D develops in only approximately 15% of individuals who are positive for single AAbs (generally against glutamic acid decarboxylase [GADA]); hence, the single GADA+ state may represent an early stage of T1D.METHODS Here, we functionally, histologically, and molecularly phenotyped human islets from nondiabetic GADA+ and T1D donors.RESULTS Similar to the few remaining β cells in the T1D islets, GADA+ donor islets demonstrated a preserved insulin secretory response. By contrast, α cell glucagon secretion was dysregulated in both GADA+ and T1D islets, with impaired glucose suppression of glucagon secretion. Single-cell RNA-Seq of GADA+ α cells revealed distinct abnormalities in glycolysis and oxidative phosphorylation pathways and a marked downregulation of cAMP-dependent protein kinase inhibitor β (PKIB), providing a molecular basis for the loss of glucose suppression and the increased effect of 3-isobutyl-1-methylxanthine (IBMX) observed in GADA+ donor islets.CONCLUSION We found that α cell dysfunction was present during the early stages of islet autoimmunity at a time when β cell mass was still normal, raising important questions about the role of early α cell dysfunction in the progression of T1D.FUNDING This work was supported by grants from the NIH (3UC4DK112217-01S1, U01DK123594-02, UC4DK112217, UC4DK112232, U01DK123716, and P30 DK019525) and the Vanderbilt Diabetes Research and Training Center (DK20593).

In this study, a 100-kHz narrow-linewidth burst-mode laser system combined with a master oscillator power amplifier (MOPA) structure was reported with a stable pulse burst profile over 2 ms duration. A theoretical model was established to characterize and compensate the variation characteristics of coefficient of variation (COV) within a burst envelope for the double-pass three-stage amplification system, in terms of different parameter factors. Simulated results yielded similar tendency with the measured ones. For a stable burst envelope with a COV of 2.72% and 2.93%, output pulse energy at a 100 kHz repetition rate was scaled up to 1.08 and 4.38 mJ in the case of an input average pulse energy of 8.6 and 116.51 nJ, respectively. Corresponding Mx2 and My2 values with 2.2 and 2.4 were endowed with 1.08 mJ output single-pulse energy and 2.7 and 2.1 for 4.38 mJ pulse energy. The spectral linewidth of amplified pulses was measured to be less than 83.1 and 67.9 MHz, with respect to output pulse duration of 10.3 and 96.1 ns, respectively.

Senecavirus A (SVA) is an oncolytic RNA virus, and it is the ideal oncolytic virus that can be genetically engineered for editing. However, there has not been much exploration into creating SVA viruses that carry antitumor genes to increase their oncolytic potential. The construction of SVA viruses carrying antitumor genes that enhance oncolytic potential has not been fully explored. In this study, a recombinant SVA-CH-01-2015 virus (p15A-SVA-clone) expressing the human p16INK4A protein, also known as cell cycle-dependent protein kinase inhibitor 2A (CDKN2A), was successfully rescued and characterized. The recombinant virus, called SVA-p16, exhibited similar viral replication kinetics to the parent virus, was genetically stable, and demonstrated enhanced antitumor effects in Ishikawa cells. Additionally, another recombinant SVA virus carrying a reporter gene (iLOV), SVA-iLOV, was constructed and identified using the same construction method as an auxiliary validation. Collectively, this study successfully created a new recombinant virus, SVA-p16, that showed increased antitumor effects and could serve as a model for further exploring the antitumor potential of SVA as an oncolytic virus.

Seneca Valley virus (SVV), a member of the Picornaviridae family, is an oncolytic RNA virus that can cause idiopathic vesicular disease and increase mortality in newborn piglets. Although research on the pathogenic characteristics, epidemiology, pathogenic mechanism, and clinical diagnosis of SVA has increased due to its emergence and prevalence, the interaction between SVA and its host lncRNA has not been fully studied. This study used qualcomm sequencing to analyze differentially expressed lncRNAs and found that during SVA infection, lncRNA 8244 was significantly down-regulated in both PK-15 cells and piglets. Further analysis through quantitative real-time PCR and dual luciferase experiments demonstrated that lncRNA8244 could compete with ssc-miR-320 to regulate the expression of CCR7. The lncRNA824-ssc-miR-320-CCR7 axis activated the TLR-mediated signaling pathway, which recognized viral molecules and induced the expression of IFN-β. These findings provide new insight into the interaction between lncRNA and SVA infection, which could lead to a better understanding of SVA pathogenesis and contribute to the prevention and control of SVA disease.

When piglets are infected by virulent and avirulent strains of swine acute diarrhea syndrome coronavirus (SADS−CoV), there are obvious differences in their clinical symptoms; however, the specific mechanisms of pathogenicity and the immune regulation of highly pathogenic and low pathogenic strains are unknown. We collected intestinal tissues from SADS−CoV−infected piglets, performed a whole transcriptome sequencing analysis, including mRNA, miRNA, lncRNA, cicrRNA, and TUCP, and performed functional and correlation analyses of differentially expressed RNAs. Our results showed that the differentially expressed RNAs in group A versus group B (AvsB), group A versus group C (AvsC), and group B versus group C (BvsC) were relevant to immune and disease−related signaling pathways that participate in the organisms’ viral infection and immune regulation. Furthermore, data obtained from the HAllA analysis suggested that there was a strong correlation between the differentially expressed RNAs. Specifically, LNC_011487 in the P set was significantly negatively correlated with ssc−miR−215, and LNC_011487 was positively correlated with PI3. Moreover, we also constructed a differentially expressed RNA association network map. This study provides a valuable resource for studying the SADS−CoV transcriptome and pathogenic mechanism from the perspective of RNA to understand the differences in and consistency of the interaction between virulent and attenuated SADS−CoV strains and hosts.

Multiple kernel clustering algorithms achieve promising performances by exploring the complementary information from kernel matrices corresponding to each data view. Most of the existing methods aim to construct a consensus kernel for the afterward clustering. However, they ignore that the desired kernel is supposed to reveal the cluster structure among samples and thus to be low rank. As a consequence, the corresponding clustering performance could decrease. To address this issue, we propose a low-rank kernel learning approach for multiple kernel clustering. Specifically, instead of regularizing the consensus kernel with low-rank constraints, we use a re-parameterize scheme for the kernel matrix. Meanwhile, the consensus kernel is located in the neighborhood area of the linear combination of base kernels. An alternate optimization strategy is designed to solve the resulting optimization problem. We evaluate the proposed method on 13 benchmark datasets with 9 state-of-the-art algorithms. As is demonstrated by experimental results, our proposed algorithm achieves superior clustering scores against the compared algorithms on the reported popular multiple kernel datasets.