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Abstract The three-stranded DNA-RNA triplex hybridization is involved in various biological processes, including gene expression regulation, DNA repair, and chromosomal stability. However, the DNA-RNA triplex mediating mechanisms underlying tumorigenesis remain to be fully elucidated. Here, we show that peptidylprolyl isomerase A (PPIA) serves as anchor to recruit GAU1 lncRNA by interacting with exon 4 of GAU1 and enhances the formation of SENP5/GAU1 DNA-lncRNA triplex. Intriguingly, TFR4 region of GAU1 exon 3 and TTS4 region of SENP5 promoter DNA constitute fragments forming the SENP5/GAU1 triplex. The SENP5/GAU1 triplex subsequently triggers the recruitment of the methyltransferase SET1A to exon 1 of GAU1, leading to the enrichment of H3K4 trimethylation and the activation of SENP5 transcription for driving the tumorigenesis of gastric cancer in vitro and in vivo. Our study reveals a mechanism of PPIA-guided SENP5/GAU1 DNA-lncRNA triplex formation in tumorigenesis and providing a concept in the dynamics of isomerase assisted DNA-RNA hybridization.

Abstract The spatial co-presence of aberrant long non-coding RNAs (lncRNAs) and abnormal coding genes contributes to malignancy development in various tumors. However, precise coordinated mechanisms underlying this phenomenon in tumorigenesis remains incompletely understood. Here, we show that Prohibitin 2 (PHB2) orchestrates the transcription of an oncogenic CASC15-New-Isoform 2 (CANT2) lncRNA and the coding tumor-suppressor gene CCBE1, thereby accelerating melanoma tumorigenesis. In melanoma cells, PHB2 initially accesses the open chromatin sites at the CANT2 promoter, recruiting MLL2 to augment H3K4 trimethylation and activate CANT2 transcription. Intriguingly, PHB2 further binds the activated CANT2 transcript, targeting the promoter of the tumor-suppressor gene CCBE1. This interaction recruits histone deacetylase HDAC1 to decrease H3K27 acetylation at the CCBE1 promoter and inhibit its transcription, significantly promoting tumor cell growth and metastasis both in vitro and in vivo. Our study elucidates a PHB2-mediated mechanism that orchestrates the aberrant transcription of lncRNAs and coding genes, providing an intriguing epigenetic regulatory model in tumorigenesis.

Abstract Multiple gene abnormalities are major drivers of tumorigenesis. NF-κB p65 overactivation and cGAS silencing are important triggers and genetic defects that accelerate tumorigenesis. However, the simultaneous correction of NF-κB p65 and cGAS abnormalities remains to be further explored. Here, we propose a novel Induced Dual-Target Rebalance (IDTR) strategy for simultaneously correcting defects in cGAS and NF-κB p65. By using our IDTR approach, we showed for the first time that oncolytic adenovirus H101 could reactivate silenced cGAS, while silencing GAU1 long noncoding RNA (lncRNA) inhibited NF-κB p65 overactivation, resulting in efficient in vitro and in vivo antitumor efficacy in colorectal tumors. Intriguingly, we further demonstrated that oncolytic adenoviruses reactivated cGAS by promoting H3K4 trimethylation of the cGAS promoter. In addition, silencing GAU1 using antisense oligonucleotides significantly reduced H3K27 acetylation at the NF-κB p65 promoter and inhibited NF-κB p65 transcription. Our study revealed an aberrant therapeutic mechanism underlying two tumor defects, cGAS and NF-κB p65, and provided an alternative IDTR approach based on oncolytic adenovirus and antisense oligonucleotides for efficient therapeutic efficacy in tumors.

Background & Aims: Distinct vascular patterns, including microvascular invasion (MVI) and vessels encapsulating tumour clusters (VETC), are associated with poor outcomes of hepatocellular carcinoma (HCC). Imaging surrogates of these vascular patterns potentially help to predict post-resection recurrence. Herein, a prognostic model integrating imaging-based surrogates of these distinct vascular patterns was developed to predict postoperative recurrence-free survival (RFS) in patients with HCC. Methods: Clinico-radiological data of 1,285 patients with HCC from China undergoing surgical resection were retrospectively enrolled from seven medical centres between 2014 and 2020. A prognostic model using clinical data and imaging-based surrogates of MVI and VETC patterns was developed (n = 297) and externally validated (n = 373) to predict RFS. The surrogates (i.e. MVI and VETC scores) were individually built from preoperative computed tomography using two independent cohorts (n = 360 and 255). Whether the model's stratification was associated with postoperative recurrence following anatomic resection was also evaluated. Results: The MVI and VETC scores demonstrated effective performance in their respective training and validation cohorts (AUC: 0.851–0.883 for MVI and 0.834–0.844 for VETC). The prognostic model incorporating serum alpha-foetoprotein, tumour multiplicity, MVI score, and VETC score achieved a C-index of 0.748–0.764 for the developing and external validation cohorts and generated three prognostically distinct strata. For patients at model-predicted medium risk, anatomic resection was associated with improved RFS (p 0.05). Conclusions: The proposed model integrating imaging-based surrogates of distinct vascular patterns enabled accurate prediction for RFS. It can potentially be used to identify HCC surgical candidates who may benefit from anatomic resection. Impact and implications: MVI and VETC are distinct vascular patterns of HCC associated with aggressive biological behaviour and poor outcomes. Our multicentre study provided a model incorporating imaging-based surrogates of these patterns for preoperatively predicting RFS. The proposed model, which uses imaging detection to estimate the risk of MVI and VETC, offers an opportunity to help shed light on the association between tumour aggressiveness and prognosis and to support the selection of the appropriate type of surgical resection.

Broken gangue has been extensively used in rockfill dams, subgrade, embankment, foundation cushion and other engineering construction. The deformation characteristics of broken gangue under the bearing compression play a decisive role in the firmness, stability and safety of these structures (buildings), and the meso-fabric change of broken gangue under the bearing compression significantly affects its macro deformation. In this study, the transparent characterization and quantitative analysis of 3D fabric of broken gangue under the bearing compression were performed through CT scanning test, image processing and 3D reconstruction technology, and the influence mechanism of internal fabric of broken gangue on its macro deformation was revealed. The results show that: In the loading stage of 0–2 MPa, the sharp corners, thin edges on the blocks and the bar-shaped and blade-shaped blocks with poor regularity are broken first under the bearing compression; in the loading stage of 2–8 MPa, a large number of larger particles in the sample are crushed in the mode of fragmentation; in the loading stage of 8–10 MPa, the breakage degree of samples is relieved. The axial displacement of the block inside the sample occurs, as well as the lateral displacement of the block converging to the central axis of the sample. In the rapid deformation stage, the macro deformation of the broken gangue is mainly caused by the rearrangement and adjustment of the block structure and the breakage of the block; in the slow deformation stage, it is mainly caused by the breakage of the block; in the stable deformation stage, it is mainly caused by the optimization and adjustment of the bearing skeleton in the sample.

Abstract The current COVID-19 pandemic has heavily burdened the global public health system and may keep simmering for years. The frequent emergence of immune escape variants have spurred the search for prophylactic vaccines and therapeutic antibodies that confer broad protection against SARS-CoV-2 variants. Here we show that the bivalency of an affinity maturated fully human single-domain antibody (n3113.1-Fc) exhibits exquisite neutralizing potency against SARS-CoV-2 pseudovirus, and confers effective prophylactic and therapeutic protection against authentic SARS-CoV-2 in the host cell receptor angiotensin-converting enzyme 2 (ACE2) humanized mice. The crystal structure of n3113 in complex with the receptor-binding domain (RBD) of SARS-CoV-2, combined with the cryo-EM structures of n3113 and spike ecto-domain, reveals that n3113 binds to the side surface of up-state RBD with no competition with ACE2. The binding of n3113 to this novel epitope stabilizes spike in up-state conformations but inhibits SARS-CoV-2 S mediated membrane fusion, expanding our recognition of neutralization by antibodies against SARS-CoV-2. Binding assay and pseudovirus neutralization assay show no evasion of recently prevalent SARS-CoV-2 lineages, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2) for n3113.1-Fc with Y58L mutation, demonstrating the potential of n3113.1-Fc (Y58L) as a promising candidate for clinical development to treat COVID-19.

Abstract Background The presence of microvascular invasion (MVI) in intrahepatic cholangiocarcinoma (ICC) is a significant adverse prognostic factor. This study sought to investigate the correlation between preoperative imaging parameters and MVI in ICC. Methods A total of 108 patients with surgically resected single ICC tumors (34 MVI-positive and 74 MVI-negative lesions) who underwent MRI examination, including T1WI, T2WI, DWI, and dynamic enhancement imaging, were enrolled in this retrospective study. The following qualitative and quantitative characteristics were evaluated: tumor morphology, signal features on T1WI and T2WI, intrahepatic duct dilatation, hepatic capsule retraction, target sign on DWI, dynamic enhancement pattern, arterial phase enhancement pattern, dot−/band-like enhancement inside the tumor, visible vessel penetration inside the tumor (hepatic artery, portal vein, or hepatic vein), integrity of the enhancement edge of the arterial phase, peripheral hepatic enhancement, tumor size, maximum enhancement edge thickness, arterial edge enhancement ratio, and delayed phase enhancement ratio. Other clinicopathological features were also used to predict and evaluate MVI in ICC. Chi-square test, Fisher’s exact test, and independent t-test were used for univariate analysis to determine the relationships among the presence of MVI and these MR parameters. Logistic regression analysis was used to identify predictors of MVI among these MR parameters. Results Among MRI characteristics, tumor morphology, intrahepatic duct dilatation, arterial phase enhancement pattern, visible hepatic artery penetration sign, maximum diameter of the tumor and the arterial phase edge enhancement ratio were correlated with MVI (P = 0.007, 0.003, 0.008, 0.000, 0.003, and 0.002, respectively). Furthermore, higher CA19–9 levels (≥37 U/ml) and pathological tumor grade III were also related to MVI (P = 0.014 and 0.004, respectively). However, multivariate logistic regression analysis demonstrated that none of the parameters were independent risk factors for the diagnosis of MVI in ICCs. Conclusion For the preoperative prediction of MVI in ICC, six qualitative and quantitative data obtained on preoperative MRI, as well as one tumorigenic marker and the pathological tumor grade, were statistically significant. More research is needed to identify MR characteristics that can be used as independent risk factors.

BackgroundIntrahepatic cholangiocarcinoma (ICC) is the second most common primary liver cancer with increasing incidence in the last decades. Microvascular invasion (MVI) is a poor prognostic factor for patients with ICC, which correlates early recurrence and poor prognosis, and it can affect the selection of personalized therapeutic regime.PurposeThis study aimed to develop and validate a radiomics-based nomogram for predicting MVI in ICC patients preoperatively.MethodsA total of 163 pathologically confirmed ICC patients (training cohort: n = 130; validation cohort: n = 33) with postoperative Ga-DTPA-enhanced MR examination were enrolled, and a time-independent test cohort (n = 24) was collected for external validation. Univariate and multivariate analyses were used to determine the independent predictors of MVI status, which were then incorporated into the MVI prediction nomogram. Least absolute shrinkage and selection operator logistic regression was performed to select optimal features and construct radiomics models. The prediction performances of models were assessed by receiver operating characteristic (ROC) curve analysis. The performance of the MVI prediction nomogram was evaluated by its calibration, discrimination, and clinical utility.ResultsLarger tumor size (p = 0.003) and intrahepatic duct dilatation (p = 0.002) are independent predictors of MVI. The final radiomics model shows desirable and stable prediction performance in the training cohort (AUC = 0.950), validation cohort (AUC = 0.883), and test cohort (AUC = 0.812). The MVI prediction nomogram incorporates tumor size, intrahepatic duct dilatation, and the final radiomics model and achieves excellent predictive efficacy in training cohort (AUC = 0.953), validation cohort (AUC = 0.861), and test cohort (AUC = 0.819), fitting well in calibration curves (p > 0.05). Decision curve and clinical impact curve further confirm the clinical usefulness of the nomogram.ConclusionThe nomogram incorporating tumor size, intrahepatic duct dilatation, and the final radiomics model is a potential biomarker for preoperative prediction of the MVI status in ICC patients.

Coronary artery disease (CAD) is a major contributor to morbidity and mortality worldwide. Myocardial ischemia may occur in patients with normal or non-obstructive CAD on invasive coronary angiography (ICA). The comprehensive evaluation of coronary CT angiography (CCTA) integrated with fractional flow reserve derived from CCTA (CT-FFR) to CAD may be essential to improve the outcomes of patients with non-obstructive CAD. China CT-FFR Study-2 (ChiCTR2000031410) is a large-scale prospective, observational study in 29 medical centers in China. The primary purpose is to uncover the relationship between the CCTA findings (including CT-FFR) and the outcome of patients with non-obstructive CAD. At least 10,000 patients with non-obstructive CAD but without previous revascularization will be enrolled. A 5-year follow-up will be performed. The primary endpoint is the occurrence of major adverse cardiovascular events (MACE), including all-cause mortality, non-fatal myocardial infarct, unplanned revascularization, and hospitalization for unstable angina. Clinical characteristics, laboratory and imaging examination results will be collected to analyze their prognostic value.

The formation of tactile perception is related to skin receptors and the cerebral cortex. In order to systematically study the tactile perception from finger friction to the brain response, a 32-channel Brain Products system and two tri-axial force sensors were used to obtain electroencephalograph (EEG) and friction signals during fingers exploring grating surfaces. A finite element finger model was established to analyze the stress changes of the skin receptors during tactile perception. Samples with different grating widths and spaces were chosen. The results indicated that different gratings induced different stress concentrations within skin that stimulated Meissner and Merkel receptors. Skin friction was affected by gratings during the tactile perception. It was also found that P300 evoked by gratings was related with the skin deformation, contact area, friction force, and stress around cutaneous mechanoreceptors. The wider grating width generated larger skin deformation, friction force, and stress, which induced stronger tactile stimulation. The smaller grating spacing generated higher vibration frequency, inducing stronger tactile stimulation. The latency of the P300 peak was related to the difference between the textured target stimulus and the smooth non-target stimulus. This study proofed that there was a relationship between the activation in brain regions, surface friction, and contact conditions of skin during the tactile perception. It contributes to understanding the formation process and cognitive mechanism of tactile perception of different surface textures. Keywords: Tactile perception, Brain activation, Friction, Gratings, Finite element analysis, P300

In this paper, we investigate the channel estimation and decoding methods exploiting the channel sparsity in pilot-assisted Multiple-Input Multiple-Output (MIMO) Vector Orthogonal Frequency Division Multiplexing (V-OFDM) systems. Based on the sparse multipath channels, we utilize orthogonal and non-orthogonal pilot schemes to design the compressed sensing (CS) measurement process. For the optimization of the sensing matrix, we discuss the influence of pilot search algorithms and evaluation criteria and propose a particle swarm optimization (PSO) based pilot search algorithm with the simplified evaluation criterion to improve the pilot design procedure. Meanwhile, the effect of pilot insertion on the Peak-to-Average Power Ratio (PAPR) is reduced by a particular precoding matrix method without affecting the decoding complexity. Simulation data are used to evaluate the classical sparsity adaptive matching (SAMP) algorithms and the proposed Variable Threshold SAMP (VTSAMP) algorithm, and the results show that the improved method has higher channel estimation accuracy with unknown sparsity. On the other hand, to overcome the complexity of CS-based decoding, we design the fully connected Deep Neural Network (FC-DNN) decoders, which combine the results of channel estimation results with the prevalent neural network technology. We observe that when the sparse channels are estimated accurately by CS methods, the proposed FC-DNN can achieve the same performance as the high-precision linear decoder by using the time-domain pilots and channel estimation results.

The triangular ridged surface can improve the grip reliability of products, but the sharp edge of triangular ridge induces sharp and uncomfortable feeling. To study the effect of edge shape (sharp, round, and flat shape) of triangular ridges on brain activity during touching, electroencephalograph (EEG) signals during tactile perception were evaluated using event-related potentials (ERP) and non-linear analysis methods. The results showed that the early component of P100 and P200, and the late component of P300 were successfully induced during perceiving the ridged texture. The edge shape features affect the electrical activity of brain during the tactile perceptions. The sharp shape feature evoked fast P100 latency and high P100 amplitude. The flat texture with complex (sharp and flat) shape feature evoked fast P200 latency, high P200 amplitude and RQA parameters. Both of the sharp shape and complex shape feature tended to evoke high peak amplitude of P300. The large-scale structures of recurrence plots (RPs) and recurrence quantification analysis (RQA) parameters can visually and quantitatively characterize the evolution regulation of the dynamic behavior of EEG system along with the tactile process. This study proved that RPs and RQA were protential methods for the feature extraction and state recognition of EEG during tactile perception of textured surface. This research contributes to optimize surface tactile characteristics on products, especially effective surface textures design for good grip.

The Enhanced Flight Vision System (EFVS) plays a significant role in the Next-Generation low visibility aircraft landing technology, where the involvement of optical sensing systems increases the visual dimension for pilots. This paper focuses on deploying infrared and visible image fusion systems in civil flight, particularly generating integrated results to contend with registration deviation and adverse weather conditions. The existing enhancement methods push ahead with metrics-driven integration, while the dynamic distortion and the continuous visual scene are overlooked in the landing stage. Hence, the proposed visual enhancement scheme is divided into homography estimation and image fusion based on deep learning. A lightweight framework integrating hardware calibration and homography estimation is designed for image calibration before fusion and reduces the offset between image pairs. The transformer structure adopting the self-attention mechanism in distinguishing composite properties is incorporated into a concise autoencoder to construct the fusion strategy, and the improved weight allocation strategy enhances the feature combination. These things considered, a flight verification platform accessing the performances of different algorithms is built to capture image pairs in the landing stage. Experimental results confirm the equilibrium of the proposed scheme in perception-inspired and feature-based metrics compared to other approaches.

In the field of ultrasonic nondestructive testing (NDT), robust and accurate detection of defects is a challenging task because of the attenuation and noising of the ultrasonic wave from the structure. For determining the reflection characteristics representing the position and amplitude of ultrasonic detection signals, sparse blind deconvolution methods have been implemented to separate overlapping echoes when the ultrasonic transducer impulse response is unknown. This letter introduces the ℓ1/ℓ2 ratio regularization function to model the deconvolution as a nonconvex optimization problem. The initialization influences the accuracy of estimation and, for this purpose, the alternating direction method of multipliers (ADMM) combined with blind gain calibration is used to find the initial approximation to the real solution, given multiple observations in a joint sparsity case. The proximal alternating linearized minimization (PALM) algorithm is embedded in the iterate solution, in which the majorize-minimize (MM) approach accelerates convergence. Compared with conventional blind deconvolution algorithms, the proposed methods demonstrate the robustness and capability of separating overlapping echoes in the context of synthetic experiments.

An ultrasonic sensors system is commonly used to measure the wall thickness of buried pipelines in the transportation of oil and gas. The key of the system is to precisely measure time-of-flight difference (TOFD) produced by the reflection of ultrasonic on the inner and outer surfaces of the pipelines. In this paper, based on deep learning, a novel method termed Wave-Transform Network is proposed to tackle the issues. The network consists of two parts: part 1 is designed to separate the potential overlapping ultrasonic echo signals generated from two surfaces, and part 2 is utilized to divide the sample points of each signal into two types corresponding to before and after the arrival time of ultrasonic echo, which can determine the time-of-flight (TOF) of each signal and calculate the thickness of pipelines. Numerical simulation and actual experiments are carried out, and the results show satisfactory performances.

The time-difference method is a common one for measuring wind speed ultrasonically, and its core is the precise arrival-time determination of the ultrasonic echo signal. However, because of background noise and different types of ultrasonic sensors, it is difficult to measure the arrival time of the echo signal accurately in practice. In this paper, a method based on the wavelet transform (WT) and Bayesian information criteria (BIC) is proposed for determining the arrival time of the echo signal. First, the time-frequency distribution of the echo signal is obtained by using the determined WT and rough arrival time. After setting up a time window around the rough arrival time point, the BIC function is calculated in the time window, and the arrival time is determined by using the BIC function. The proposed method is tested in a wind tunnel with an ultrasonic anemometer. The experimental results show that, even in the low-signal-to-noise-ratio area, the deviation between mostly measured values and preset standard values is mostly within 5 μs, and the standard deviation of measured wind speed is within 0.2 m/s.

Remote Field Eddy Current Testing (RFECT) has broad applications in ferromagnetic pipe testing due to the same testing sensitivity to inner and outer wall defects. However, how to quantify wall thickness in the RFECT of pipes is still a big problem. According to researchers’ studies, a linear relationship exists between the wall thickness, permeability and conductivity of a pipe and the phase of the RFECT signal. Aiming to quantify wall thickness by using this linear function, it is necessary to further study the effects of pipe permeability and conductivity on the phase of the RFECT signal. When the product value of the permeability and the conductivity of a pipe remains constant, the univariate analysis and Finite Element Analysis (FEA) are employed to analyze the variations among the phase of the RFECT signal caused by different couples of permeability and conductivity. These variations are calibrated by using a nonlinear fitting method. Moreover, Multi-Frequency Eddy Current Testing (MFECT) is applied to inverse the permeability and conductivity of a pipe to compensate for the quantification analysis of wall thickness. The methods proposed in this paper are validated by analyzing the simulation signals and can improve the practicality of RFECT of ferromagnetic pipes.