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The gangue recognition technology is one of the key technologies in the intelligent construction of mines. To address the problem of low accuracy of the gangue recognition model caused by low illumination and high dust environment at the working face and the difficulty of recognizing small target gangue, a coal gangue image recognition model based on CSPNet-YOLOv7 target detection algorithm is proposed. Cross Stage Partial Network (CSPNet) is used to improve the backbone feature extraction network of YOLOv7 model, optimize the gradient information to reduce the network parameters, while Recursive Feature Pyramid (RFP) and Switchable Auto Convolution (SAC) to replace the simple up and down sampling and normal convolution modules in the neck feature extraction network, and to enhance the generalization ability of the network by using three migration training for feature learning of different widths and depths. The experimental results show that the CSPNet-YOLOv7 model has an average accuracy mean of 97.53%, an accuracy rate of 92.24%, a recall rate of 97.91%, an F1 score of 0.95, a model parametric number of 30.85×106, a floating point operation count of 42.15×109, and a frame rate of 24.37 f/s transmitted per second, Compared to the YOLOv7 model, the average mean accuracy is improved by 7.46%, and the number of parameters and floating point operations are reduced by 17.23% and 60.41%, respectively, compared to the FasterRCNN-Resnet50, YOLOv3, YOLOv4, MobileNet V2 -YOLOv4, YOLOv4-VGG, YOLOv5s models. The CSPNet-YOLOv7 model has the highest average accuracy mean for coal gangue identification, while the number of parameters and floating point operations is small, which has a good balance between identification accuracy and speed. Finally, the CSPNet-YOLOv7 model is validated through downhole field tests, providing an effective technical means for accurate coal gangue identification.

This is an article in the field of mineral processing engineering. Three samples of 5#, 9# and Mugua coal from Coal Preparation Plant of Lyulin Energy & Chemical Co., Ltd. were collected as the research objects. Screening, proximate analysis and ultimate analysis of the three coal samples were carried out. The mineral composition and surface functional groups of the three coal samples were analyzed by XRD, FT-IR and XPS. Kerosene, on-site reagent and TY6 developed in the laboratory were used for flotation tests. The collecting effect of the three reagents on different types of coal samples was compared. The results showed that 5# coal sample was easy to float, 9# coal sample showed less floatability than 5# coal, while Mugua coal showed the poorest floatability owing to high oxidation and strong hydrophilicity. For the three coal samples, TY6 and the on-site reagents both showed much better collective ability than kerosene and consume less as well. For easy-to-float coal, TY6 showed better selectivity, while for difficult-to-float coal, TY6 showed better collectivity. Smaller particle size dispersed in water, higher viscosity, and containing oxygen-bearing organic substances were the main characteristics of TY6 and the on-site reagents that made them stand out as compared with kerosene, which resulted in better reaction with coal surface, bigger contact angle and better hydrophobicity. In this point of view, TY6 behaved even a little better than the on-site reagents.

ObjectiveThis study aimed to investigate the relationship between physical exercise and depression among college students, focusing on the mediating role of self-esteem and positive psychological capital.MethodsGroup psychological measurements were conducted on 579 students using various scales, including the Physical Activity Rating Scale (PARS-3), Self-Esteem Scale (SES), Positive Psychological Capital Questionnaire (PPQ), and Self-rating Depression Scale (SDS). The data was analyzed using SPSS 26.0 and bootstrap methods to test and analyze the effects.ResultsA negative correlation between physical exercise and depressive mood, with physical exercise significantly predicting college students' depressive mood. Additionally, physical exercise was found to positively predict self-esteem and positive psychological capital, both of which are negatively predictive of depressive mood. Self-esteem and positive psychological capital were identified as significant mediators between physical exercise and depressive mood, with three mediating paths: physical exercise, self-esteem, and depressed mood (Path 1), exhibited an indirect effect of 0.017, with the bootstrap 95% confidence interval excluding 0 (LLCI = −0.051, ULCI = −0.004) and accounting for 8.30% of the total effect. Physical exercise, positive psychological capital, and depression emotion (Path 2), had an indirect effect of 0.049, with the bootstrap 95% confidence interval also not containing 0 (LLCI = −0.088, ULCI = −0.011) and contributing to 23.90% of the total effect. Physical exercise, self-esteem, positive psychological capital, depressed mood (Path 3), demonstrated an indirect effect of 0.006, with the bootstrap 95% confidence interval excluding 0 (LLCI = −0.011, ULCI = −0.001) and representing 2.90% of the total effect.ConclusionPhysical exercise negatively predicts depressive mood among college students and has a mediating effect through self-esteem and positive psychological capital, creating a chain-like impact on their depressive symptoms.

Monopole time reversal is an advanced noise source localization technique, but its sidelobe level in localizing sound sources is greatly affected by noise, leading to a decrease in the accuracy of sound source localization under a low signal-to-noise ratio. To address this issue, this paper proposes a time-domain monopole time reversal technique that excludes autocorrelation to reduce the impact of noise on sound source localization results. Numerical simulation and experimental results show that, compared to conventional monopole time reversal techniques, the proposed method can effectively reduce the sidelobes in sound source localization results and achieve accurate sound source localization under −20 dB low signal-to-noise ratio conditions.

Abstract Background B-type lamins are critical nuclear envelope proteins that interact with the three-dimensional genomic architecture. However, identifying the direct roles of B-lamins on dynamic genome organization has been challenging as their joint depletion severely impacts cell viability. To overcome this, we engineered mammalian cells to rapidly and completely degrade endogenous B-type lamins using Auxin-inducible degron technology. Results Using live-cell Dual Partial Wave Spectroscopic (Dual-PWS) microscopy, Stochastic Optical Reconstruction Microscopy (STORM), in situ Hi-C, CRISPR-Sirius, and fluorescence in situ hybridization (FISH), we demonstrate that lamin B1 and lamin B2 are critical structural components of the nuclear periphery that create a repressive compartment for peripheral-associated genes. Lamin B1 and lamin B2 depletion minimally alters higher-order chromatin folding but disrupts cell morphology, significantly increases chromatin mobility, redistributes both constitutive and facultative heterochromatin, and induces differential gene expression both within and near lamin-associated domain (LAD) boundaries. Critically, we demonstrate that chromatin territories expand as upregulated genes within LADs radially shift inwards. Our results indicate that the mechanism of action of B-type lamins comes from their role in constraining chromatin motion and spatial positioning of gene-specific loci, heterochromatin, and chromatin domains. Conclusions Our findings suggest that, while B-type lamin degradation does not significantly change genome topology, it has major implications for three-dimensional chromatin conformation at the single-cell level both at the lamina-associated periphery and the non-LAD-associated nuclear interior with concomitant genome-wide transcriptional changes. This raises intriguing questions about the individual and overlapping roles of lamin B1 and lamin B2 in cellular function and disease.

Infrared thermography technology, leveraging its unique ability to capture temperature features, has significantly improved the precision of high-temperature target localization. However, infrared imaging technology is limited by issues such as low image contrast, difficulty in distinguishing object categories, and limited image clarity. To enable intelligent detection of high-temperature objects that may cause fires in warehouses, this paper proposes an innovative method that integrates deep learning image segmentation with infrared and visible light image technology. We developed a new image segmentation model based on improved Fully Convolutional Networks and Deconvolutional Networks, introducing a batch normalization layer to accelerate convergence and employing the PReLU activation function to prevent neuron death, thereby enhancing convergence speed and accuracy. Through a feature dynamic image registration method combining a joint model and a cross-modulation strategy, we achieved efficient image fusion. In addition, a game theory-based strategy was adopted to correct localization results, ensuring accuracy. Experimental results demonstrate that the improved model achieves localization accuracy and precision rates of up to 89.30% and 88.00%, respectively, in real-world warehouse heat source scenarios, representing a significant improvement of 9.90% and 2.85% compared to the pre-improvement model, fully validating its advancement and effectiveness.

IntroductionChimeric antigen receptor natural killer (CAR-NK) cells have been found to be successful in treating hematologic malignancies and present potential for usage in solid tumors.MethodsIn this study, we created CD276-targeted CAR-expressing NK cells from pluripotent stem cells (iPSC CD276-targeted CAR-NK cells) and evaluated their cytotoxicity against esophageal squamous cell carcinoma (ESCC) using patient-specific organoid (PSO) models comprising of both CD276-positive and CD276-negative adjacent epithelium PSO models (normal control PSO, NC PSO) as well as primary culture of ESCC cell models. In addition, in vitro and in vivo models such as KYSE-150 were also examined. iPSC NK cells and NK-free media were used as the CAR-free and NK-free controls, respectively.ResultsThe positive CD276 staining was specifically detected on the ESCC membrane in 51.43% (54/105) of the patients of all stages, and in 51.35% (38/74) of stages III and IV. The iPS CD276-targeted CAR-NK cells, comparing with the iPS NK cells and the NK-free medium, exhibited specific and significant cytotoxic activity against CD276-positive ESCC PSO rather than CD276-negative NC PSO, and exhibited significant cytotoxicity against CD276-expressing cultured ESCC cells, as well as against CD276-expressing KYSE-150 in vitro and in BNDG mouse xenograft.DiscussionThe efficacy of the iPSC CD276-targeted CAR-NK cells demonstrated by their successful treatment of CD276-expressing ESCC in a multitude of pre-clinical models implied that they hold tremendous therapeutic potential for treating patients with CD276-expressing ESCC.

Background: There is currently no consensus regarding the optimal perioperative antiplatelet strategy for carotid artery surgery. This multicentre study aimed to analyse the association between preoperative aspirin monotherapy following postoperative dual antiplatelet therapy (DAPT) and the risk for stroke and death after carotid endarterectomy (CEA). Methods: This cohort study included 821 patients with carotid artery stenosis who underwent CEA. Primary outcomes included any stroke or death up to the one-month postoperative follow-up. Multilevel multivariate regression analyses and descriptive statistics were performed. Results: Patients were predominantly male (53 %), with a mean age of 66.2 years. The primary outcome occurred in 1.6 % of patients. Univariate and multivariate analyses revealed that patients with chronic obstructive pulmonary disease (COPD) exhibited a high risk for stroke or death (P = 0.011). The occurrence of any local complications in the neck was accompanied by an increase in diastolic blood pressure (DBP) (P = 0.007). Patients with a high systolic blood pressure (SBP) (P = 0.002) experienced a longer operative duration. The length of hospital stay was longer in the patients with COPD (P = 0.020), minor stroke (P = 0.011), and major stroke (P = 0.001). A positive linear correlation was found between SBP and operative duration in the overall population (β 0.4 [95 % confidence interval (CI) 0.1–0.7]; P = 0.002). The resultant curve for DBP and any local complications in the neck exhibited a two-stage change and one breakpoint in the entire population (k = 68 mmHg,

Introduction: Early predictive pathological complete response (pCR) is beneficial for optimizing neoadjuvant chemotherapy (NAC) strategies for breast cancer. The hematoxylin and eosin (HE)-stained slices of biopsy tissues contain a large amount of information on tumor epithelial cells and stromal. The fusion of pathological image features and clinicopathological features is expected to build a model to predict pCR of NAC in breast cancer.Methods: We retrospectively collected a total of 440 breast cancer patients from three hospitals who underwent NAC. HE-stained slices of biopsy tissues were scanned to form whole-slide images (WSIs), and pathological images of representative regions of interest (ROI) of each WSI were selected at different magnifications. Based on several different deep learning models, we propose a novel feature extraction method on pathological images with different magnifications. Further, fused with clinicopathological features, a multimodal breast cancer NAC pCR prediction model based on a support vector machine (SVM) classifier was developed and validated with two additional validation cohorts (VCs).Results: Through experimental validation of several different deep learning models, we found that the breast cancer pCR prediction model based on the SVM classifier, which uses the VGG16 model for feature extraction of pathological images at ×20 magnification, has the best prediction efficacy. The area under the curve (AUC) of deep learning pathological model (DPM) were 0.79, 0.73, and 0.71 for TC, VC1, and VC2, respectively, all of which exceeded 0.70. The AUCs of clinical model (CM), a clinical prediction model established by using clinicopathological features, were 0.79 for TC, 0.73 for VC1, and 0.71 for VC2, respectively. The multimodal deep learning clinicopathological model (DPCM) established by fusing pathological images and clinicopathological features improved the AUC of TC from 0.79 to 0.84. The AUC of VC2 improved from 0.71 to 0.78.Conclusion: Our study reveals that pathological images of HE-stained slices of pre-NAC biopsy tissues can be used to build a pCR prediction model. Combining pathological images and clinicopathological features can further enhance the predictive efficacy of the model.

ABSTRACTAtherosclerosis (AS) is a prevalent cardiovascular disease that greatly increases mortality in the aging population and imposes a heavy burden on global healthcare systems. The purpose of this study is to examine the research structure and current trends of monoclonal antibodies (mAbs) against AS from a bibliometric perspective, since the development of these drugs is currently booming. This study collected articles and reviews on mAbs against AS from the Web of Science Core Collection, spanning from 2003 to 2022. Biblioshiny was utilized to analyze and visualize the characteristics of countries, regions, authors, institutions, and journals included in this collection. We used VOS viewer to illustrate the frequency of country co-occurrence, and CiteSpace to visualize co-cited reference, keywords co-occurrence, keywords citation bursts, keywords clustering and timeline plots. The study included 1325 publications, with the United States emerging as a leading contributor to the field. ATHEROSCLEROSIS, CIRCULATION and ARTERIOSCLEROSISTHROMBOSIS AND VASCULAR BIOLOGY are core journals that publish high-quality literature on the latest advances in the field. Noteworthy authors with numerous high-quality publications include Witztum JL and Tsimikas S. Currently, lipid metabolism and inflammation are the main research areas of interest in this field. The mAbs against AS is an evolving field, and ongoing research continues to advance our understanding. This paper provides a comprehensive overview of the current state of knowledge in this area, highlighting two primary research directions: inflammation and lipid metabolism. Additionally, the paper identifies emerging research hotspots, which will provide researchers with useful insights to guide future investigations and anticipate research directions.

OBJECTIVE: The objective is to explore the correlation between carotid artery calcification score and cerebral hyperperfusion syndrome (CHS) after carotid artery stenting (CAS) in patients with carotid artery stenosis. METHODS: Consecutive patients treated for carotid artery stenosis from October 2016 to August 2021 from two centers were studied. Carotid artery calcification score was manually segmented on multi-detector row spiral computed tomography angiography scans before CAS. The associations between carotid artery calcification score and the occurrence of CHS during the in-hospital time were assessed. RESULTS: A total of 175 patients were enrolled in the study. The mean age was 72.12 ± 7.51 years. All patients were undergone CAS treatment successfully. There were 46 cases that occurred CHS (26.29%). Univariate logistic regression analyses showed that for every 1 unit (100-score) increase in carotid artery calcification score, the risk of CHS increased by 1937% (odds ratio [OR] = 20.37, 95% confidence interval [CI]: 7.54–55.03, P < 0.01). In addition, the increasing rate was significantly different between the open-cell group (OR = 261.7, P < 0.01) and the closed-cell group (OR = 11.4, P < 0.01). The area under curve of predictiong model of carotid artery calcification score to CHS was 0.84 (95% CI, 0.75–0.93). CONCLUSION: Carotid artery calcification score was an independent risk factor for CHS in patients who underwent CAS. The application of carotid artery calcification score as risk factor in clinical screening of carotid artery stenosis patients may have beneficial effects on the management of CAS.

Multiagent systems face numerous challenges due to environmental uncertainty, with scalability being a critical issue. To address this, we propose a novel multi-agent cooperative model based on a graph attention network. Our approach considers the relationship between agents and continuous action spaces, utilizing graph convolution and recurrent neural networks to define these relationships. Graph convolution is used to define the relationship between agents, while recurrent neural networks define continuous action spaces. We optimize and model the multiagent system by encoding the interaction weights among agents using the graph neural network and the weights between continuous action spaces using the recurrent neural network. We evaluate the performance of our proposed model by conducting experimental simulations using a 3D wargame engine that involves several unmanned air vehicles (UAVs) acting as attackers and radar stations acting as defenders, where both sides have the ability to detect each other. The results demonstrate that our proposed model outperforms the current state-of-the-art methods in terms of scalability, robustness, and learning efficiency.

IntroductionAlthough there is extended research on the response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines in adult cancer patients (ACP), the immunogenicity to the variants of concern (VOCs) in childhood cancer patients (CCP) and safety profiles are now little known.MethodsA prospective, multi-center cohort study was performed by recruiting children with a solid cancer diagnosis and childhood healthy control (CHC) to receive standard two-dose SARS-CoV-2 vaccines. An independent ACP group was included to match CCP in treatment history. Humoral response to six variants was performed and adverse events were followed up 3 months after vaccination. Responses to variants were compared with ACP and CHC by means of propensity score-matched (PSM) analysis.ResultsThe analysis included 111 CCP (27.2%, median age of 8, quartile 5.5–15 years), 134 CHC (32.8%), and 163 ACP (40.0%), for a total 408 patients. Pathology included carcinoma, neural tumors, sarcoma, and germ cell tumors. Median chemotherapy time was 7 (quartile, 5–11) months. In PSM sample pairs, the humoral response of CCP against variants was significantly decreased, and serology titers (281.8 ± 315.5 U/ml) were reduced, as compared to ACP (p< 0.01 for the rate of neutralization rate against each variant) and CHC (p< 0.01 for the rate of neutralization against each variant) groups. Chemotherapy time and age (Pearson r ≥ 0.8 for all variants) were associated with the humoral response against VOCs of the CHC group. In the CCP group, less than grade II adverse events were observed, including 32 patients with local reactions, and 29 patients had systemic adverse events, including fever (n = 9), rash (n = 20), headache (n = 3), fatigue (n = 11), and myalgia (n = 15). All reactions were well-managed medically.ConclusionsThe humoral response against VOCs after the CoronaVac vaccination in CCP was moderately impaired although the vaccine was safe. Age and chemotherapy time seem to be the primary reason for poor response and low serology levels.

Summary: Background: Cancer-related cognitive decline is a serious problem in long-term survival but no pivotal study has investigated whether checkpoint inhibitors (ICI) may be associated with cognitive adverse events. Methods: This propensity score-matched analysis recruited non-small cell lung cancer (NSCLC) patients prescribed with or without ICI monotherapy from three Chinese tertiary hospitals. Patients were excluded from study who developed brain metastasis or had disorders severely affecting cognitive abilities. Primary outcomes were changes in neuropsychological battery test (NBT) at baseline, 6- and 12-month sessions, and any NBT score changes that exceeded 3∗SD of baseline scores would be marked as objective cognitive adverse events (CoAE). Secondary endpoint was the 20-item Perceived Cognitive Impairment (PCI) sub-scale score change in Functional Assessment of Cancer Therapy-Cognitive Function questionnaire, administered at baseline, 3-, 6-, 9-, 12-, and 15-month follow-up session. Per-protocol ICI and control arms were matched with propensity scores that incorporated baseline variables to compare both NBT and PCI assessment results. Patients participating in PCI assessments were analysed in intention-to-treat analysis. Kaplan–Meier survival curves with log-rank tests were adopted to analyse incidence of perceived cognitive decline events (PCDE). Findings: Between March 12, 2020, and March 28, 2021, 908 participants were enrolled. Compared to control, 3 of 4 subtest of NBT scores in ICI arm showed significant cognitive decline in 6- and 12-month sessions, in which Trail Making Test score change (13.56 ± 11.73) reached threshold of cognitive deficit diagnosis in the 12-month session. In 1:1 matched 292 pairs from 908 patients, PCI score changes in ICI arms were −4.26 ± 8.54 (3rd month), −4.72 ± 11.83 (6th month), −6.16 ± 15.41 (9th month), −6.07 ± 15.71 (12th month), and −7.96 ± 13.97 (15th month). The scores were significantly lower than control arm in 3-, 6-, and 12-session follow-up. The result was validated after adjusting quality of life scores and in intention-to-treat analysis. Mean PCI change exceeded 1/2 SD of baseline PCI score (5.81) in 9-, 12-, and 15-month sessions in ICI arm, but not in control arm. PCDE incidence/prevalence was significantly higher in ICI arm (incidence 26.4% vs. 5.1%, and prevalence 16.2% vs. 1.7%). Immune-related adverse events related to incidence of PCDE after adjusting for baseline variables. Interpretation: ICI monotherapy seemed to relate to higher cognitive decline represented by score changes and incidence/prevalence rates. The decline deteriorated as treatment progressed, and immune-related adverse events seemed to be associated with higher cognitive adverse events incidence in the ICI treatment. Funding: The Fellowship of China Postdoctoral Science Foundation and National Natural Science Foundation of China Youth Science Fund Project.

The Princess Elizabeth Land landscape in East Antarctica was shaped by a complex process, involving the supercontinent’s breakup and convergence cycle. However, the lack of geological knowledge about the subglacial bedrock has made it challenging to understand this process. Our study aimed to investigate the structural characteristics of the subglacial bedrock in the Mount Brown region, utilizing airborne geophysical data collected from the China Antarctic Scientific Expedition in 2015–2017. We reconstructed bedrock density contrast and magnetic susceptibility models by leveraging Tikhonov regularized gravity and magnetic inversions. The deep bedrock in the inland direction exhibited different physical properties, indicating the presence of distinct basement sources. The east–west discontinuity of bedrock changed in the inland areas, suggesting the possibility of large fault structures or amalgamation belts. We also identified several normal faults in the western sedimentary basin, intersected by the southwest section of these survey lines. Furthermore, lithologic separators and sinistral strike-slip faults may exist in the northeast section, demarcating the boundary between Princess Elizabeth Land and Knox Valley. Our study provides new insights into the subglacial geological structure in this region, highlighting the violent impact of the I-A-A-S (Indo-Australo-Antarctic Suture) on the subglacial basement composition. Additionally, by identifying and describing different bedrock types, our study redefines the potential contribution of this region to the paleocontinent splicing process and East Antarctic basement remodeling.

BackgroundCarotid body tumor (CBT) is the most common head and neck paraganglioma. Whether preoperative embolization benefits CBT patients who will receive surgical resection is still controversial.MethodsIn this multi-center retrospective study, we collected data from patients with CBT who received surgical treatment without (group A) or with preoperative embolization (group B) from 2011 to 2019. The primary outcome was the rate of death or stroke after 3 years of follow-up. The secondary outcomes of the study were length of operation (LOO), intraoperative blood loss (IBL), length of stay (LOS), rate of recurrence, and rate of cranial nerve (CN) injuries. Descriptive statistics were used to analyze the demographics, clinical characteristics, complications, and follow-up results of the patients.ResultsBetween January 2011 and October 2019, 261 consecutive patients (107 male and 154 female) entered analysis. After 3 years of follow-up, no patient died in both groups. Only three patients with stroke were detected: 2/226 (0.9%) in group A vs. 1/35 (2.9%) in group B (p = .308). The LOO in group A was 132.6 ± 64.6 min compared with 152.9 ± 40.4 min in group B (p = .072). IBL in group A was 375.4 ± 497.8 ml compared with 448.0 ± 270.8 ml in group B (p = .400). LOS in group A was 8.3 ± 2.0 days compared with 7.4 ± 1.7 days in group B (p = .016). Seventy-two CN injuries were detected: 65/226 (28.8%) in group A vs. 7/35 (20.0%) in group B (p = .281). There were 65 temporary CN injuries (59 in group A vs. 6 in group B) (p = .254) and seven permanent CN injuries (6 in group A vs. 1 in group B) (p = .945). Three most frequently injured cranial nerves were the pharyngeal branch and superior laryngeal nerve (12.3%), recurrent laryngeal nerve (7.7%) and vagus nerve (7.3%).ConclusionThere was insufficient evidence to support the efficacy of preoperative embolization. CBT resection alone had a similar rate of stoke, recurrence, and CN injuries when compared with CBT resection with preoperative arterial embolization. Meanwhile, CBT resection alone did not increase LOO and IBL.

Recent reports indicate that immune cells in solid cancers have significant predictive and therapeutic value. IgG4 is a subclass of IgG and we recently found that it exerted an inhibitory effect in tumor immunity. We aimed to assess the significance of IgG4 and T cell subtypes in tumor prognosis. We investigated the density, distribution and relationship of five immune markers CD4, CD8, Foxp3, IL-10 and IgG4 with multiple immunostaining method in 118 esophageal squamous cell carcinoma (ESCC) together with clinical data. The relationship among different immune cell types and with clinical data were analyzed with Kaplan-Meier survival analysis and Cox proportional hazards model to identify independent risk factors among immune and clinicopathological parameters. Five-year survival rate of these patients treated with surgery reached 61%. Higher number of CD4+ plus CD8+ T cells predicted better prognosis (p=0.01) in tertiary lymphoid structure (TLS) and could add to the value of TNM staging. Density of the newly identified immune inhibitor IgG4+ B lymphocytes was found positively correlated to that of CD4+ cells (p=0.02) and IL-10+ cells (p=0.0005), but number of infiltrating IgG4+ cells by itself was not an independent factor for prognosis. However, increased serum concentration of IgG4 indicated a poor prognosis of ESCC (p=0.03). 5-year survival rate of esophageal cancer after surgery has been significantly improved. Increased T cells in TLS predicted better survival, suggesting that T cells in TLS may actively participate in anti-tumor immunity. Serum IgG4 could be a useful predictor of prognosis.

Abstract Background Aortic dissection is one of the most common emergency condition leading to internal organs or lower limb ischemia and aortic rupture. Herein, we described a reverse “cheese wire” endovascular fenestration repair (CWFER) in a patient with complicated abdominal aortic dissection which had never been reported. Case presentation A 62-year-old male presented abdominal tear-like pain and acute ischemia of the right lower extremity during the endovascular treatment of celiac trunk aneurysms. Computed tomography angiography (CTA) and digital subtraction angiography (DSA) showed abdominal aortic type B dissection with acute ischemia of the right lower extremity preoperatively. After a detailed preoperative examination, the patient then was performed a reverse CWFER. So far, the patient has been followed-up for 6 months, postoperative CTA demonstrated good stent-graft expansion and perfusion of bilateral common iliac arteries; also, no endoleak was detected. Conclusions The right iliac artery in this patient supplied by false lumen, which lead to acute ischemia of the right lower extremity, needed to be treated as an emergency and dealt with promptly. CWFER is a very high-risk treatment that requires the rich experience of vascular surgeon and accurate assessment of aortic dissection. After interventional treatment, the patient recovered uneventfully after 6 months’ follow-up.

The direction-of-arrival (DOA) estimation is predominantly influenced by the antenna’s aperture size. However, space constraints on flight platforms often necessitate the use of antennas with smaller apertures and fewer array elements. This inevitably imposes limitations on the DOA estimation’s resolution and degrees of freedom. To address these precision constraints, we introduce an accurate DOA estimation method based on spatial synthetic aperture model. This method adopts a two-stage strategy to ensure both efficiency and precision in DOA estimation. Initially, the orthogonal matching pursuit (OMP) reconstruction algorithm processes the original aperture data, providing a rough estimate of target angles that guides the aircraft’s flight direction. Subsequently, the early estimations merge with the aircraft’s motion space samples, forming equivalent spatially synthesized array samples. The refined angle estimation then employs the OMP-RELAX algorithm. Moreover, with the off-grid issue in mind, we devise an estimation method integrating Bayesian parameter estimation with dictionary sequence refinement. The proposed technique harnesses the spatial synthetic aperture for pinpoint estimation, effectively addressing the challenges of atomic orthogonality and angular off-grid on estimation accuracy. Importantly, the efficiency of deploying sparse reconstruction for angle estimation is bolstered by our phased strategy, eliminating the necessity for fine grid analysis across the entire observation scene. Moreover, the poor estimation accuracy caused by coherent source targets and angular-flickering targets is improved by sparse reconstruction. Through simulation and experiment, we affirm the proposed method’s efficacy in angle estimation. The results indicate that target angle estimation errors are limited to within 1°. Furthermore, we assess the impact of variables such as target state, heading angle, spatial sampling points, and target distance on the estimation accuracy of our method, showcasing its resilience and adaptability.

Abstract Background Tertiary lymphoid structures (TLSs) play key roles in tumour adaptive immunity. However, the prognostic value and molecular properties of TLSs in oesophageal squamous cell carcinoma (ESCC) patients have not been studied. Methods The prognostic values of the presence and maturation status of tumour‐associated TLSs were determined in 394 and 256 ESCC patients from Sun Yat‐sen University Cancer Center (Centre A) and the Cancer Hospital of Shantou University Medical College (Centre B), respectively. A deep‐learning (DL) TLS classifier was established with haematoxylin and eosin (H&E)‐stained slides using an inception‐resnet‐v2 neural network. Digital spatial profiling was performed to determine the cellular and molecular properties of TLSs in ESCC tissues. Results TLSs were observed in 73.1% of ESCCs from Centre A via pathological examination of H&E‐stained primary tumour slides, among which 42.9% were TLS‐mature and 30.2% were TLS‐immature tumours. The established DL TLS classifier yielded favourable sensitivities and specificities for patient TLS identification and maturation evaluation, with which 55.1%, 39.5% and 5.5% of ESCCs from Centre B were identified as TLS‐mature, TLS‐immature and TLS‐negative tumours. Multivariate analyses proved that the presence of mature TLSs was an independent prognostic factor in both the Centre A and Centre B cohorts (p < .05). Increased proportions of proliferative B, plasma and CD4+ T helper (Th) cells and increased B memory and Th17 signatures were observed in mature TLSs compared to immature ones. Intratumoural CD8+ T infiltration was increased in TLS‐mature ESCC tissues compared to mature TLS‐absent tissues. The combination of mature TLS presence and high CD8+ T infiltration was associated with the best survival in ESCC patients. Conclusions Mature TLSs improve the prognosis of ESCC patients who underwent complete resection. The use of the DL TLS classifier would facilitate precise and efficient evaluation of TLS maturation status and offer a novel probability of ESCC treatment individualization.

Inspired by the parallel visual pathway model of the human neural system, we propose an efficient and high-precision point cloud registration method based on complex network theory (PointCNT). A deep learning network (DNN) design method based on complex network theory is proposed, and a multipath feature extraction network, namely, Complex Kernel Point Convolution Neural Network (ComKP-CNN) for point clouds is designed based on the design method. Self-supervision is introduced to improve the feature extraction ability of the model. A feature embedding module is proposed to explicitly embed the transformation-variant coordinate information and transformation-invariant distance information into features. A feature fusion module is proposed to enable the source and template point clouds to perceive each other’s nonlocal features. Finally, a Multilayer Perceptron (MLP) with prominent fitting characteristics is utilized to estimate the transformation matrix. The experimental results show that the Registration Recall (RR) of PointCNT on ModelNet40 dataset reached 96.4%, significantly surpassing one-stage methods such as Feature-Metric Registration (FMR) and approaching two-stage methods such as Geometric Transformer (GeoTransformer). The computation speed is faster than two-stage methods, and the registration run time is 0.15 s. In addition, ComKP-CNN is universal and can improve the registration accuracy of other point cloud registration methods.

BackgroundCarotid body tumor (CBT) is a rare paraganglioma located at the carotid bifurcation. The red blood cell count, hemoglobin, and hematocrit are indexes to be evaluated in blood routine tests. The purpose of this study was to clarify their predictive value for temporary postoperative complications in patients that had undergone CBT surgery.MethodsThis retrospective trial included data from 169 patients received surgical treatment for CBT from October 2008 to September 2018 in this retrospective study. Postoperative follow-up was conducted under the guidance of both vascular surgeon and neurologist. The symptoms existed less than 2 years postoperatively were regarded as temporary injuries. The red blood cell count, hemoglobin, and hematocrit were obtained from the complete blood count results of the participants. Analyses of multilevel multivariable regression and descriptive statistics were conducted.ResultsThe baseline data showed no significant difference. Patients were predominantly women (53.8%), with a mean age of 42.6 years. The total incidence of temporary postoperative complications was 22 (13.0%), including transient ischemic attack (8, 4.7%), tongue bias (7, 4.1%), dysphagia (2, 1.2%), hoarseness (4, 1.8%), and eyelid ptosis (1, 2.4%). The univariate and multivariate regression analysis results revealed that the occurrence of temporary postoperative complications was increased with age [odd ratio (OR, 0.09; 95% CI (CI), 0.9–1.0; P = 0.014], length of operation time (OR, 1.0; 95% CI, 1.0–1.0; P = 0.005), Shamblin type II vs. I (OR, 0.1; 95% CI, 0.0–0.5; P = 0.008), red blood cell count postoperative (OR, 0.2; 95% CI, 0.1–0.8; P = 0.026), hemoglobin (OR, 0.9; 95% CI, 0.9–1.0; P = 0.011), and hematocrit (OR, 0.8; 95% CI, 0.7–1.0; P = 0.025). The smooth curve fitting showed that the trend of complications occurrence rate was reduced with the increase of patients’ postoperative red blood cell count, hemoglobin, and hematocrit. Gender, weight, length of operation, Shamblin type, postoperative red blood cell count, hemoglobin, and hematocrit were included in the risk model with AUC = 0.86.ConclusionThese patients with CBT who received surgical resection with low postoperative red blood cell, hemoglobin, or hematocrit had a high risk of temporary postoperative complications. The risk prediction model established for predicting temporary postoperative complications showed satisfactory prediction effects.

Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned Pairwise Agreement (RBS-LPA), was proposed. The process was formulated as a graph-regularized row-sparse constrained optimization problem, which select a few representative bands to code the all bands based on the learned pairwise band agreement. In RBS-LPA, a spatial-spectral informative graph, constructed by spatial-spectral neighbor relationship, is incorporated to encode both the spatial and spectral geometrical structure. By combining the learning procedure with graph regularizer jointly, the graph regularizer can adaptively change with the sparse representative bands, which ensures that the selected bands can well preserve the local manifold structure. Experimental results on three real urban hyperspectral data demonstrate the efficiency of the proposed RBS-LPA and achieve convincing performance.

With the development of artificial intelligence, multiagent algorithms have been applied to many real-time strategy games. Making plans on the human being is gradually passing away, especially in combat scenarios. Cognitive electronic warfare (CEW) is a complex and challenging work due to the sensitivity of the data sources. There are few studies on CEW. In the past, wargame simulations depended on differential equations and war theory, which resulted in high time and human resource costs. In the future, as other artificial intelligence theories are developed, artificial intelligence will play a more critical role in wargames. The capabilities of multiagent modeling to describe complex systems and predict actions in dynamic environments are superior to those of traditional methods. In this paper, we use a 3D wargame engine from China Aerospace System Simulation Technology Co., Ltd. (Beijing) named All Domain Simulation (ACS), which supports land, sea, and air combat scenarios, to simulate combat. In the simulations, there are several unmanned air vehicles (UAVs) as attackers and several radar stations as defenders, and both have the ability to detect the others. In the game, several UAVs need to learn to detect targets and track targets separately, and we train the UAV's behavior by well-designed reward shaping and multiagent reinforcement learning (MARL) with LSTM. We improved the RDPG algorithm and merged the MADDPG and RDPG algorithms. From the experimental results, we can see that the effectiveness and accuracy of the algorithm have been greatly improved.

At present, low emissivity coating has been widely used in various fields, but damage will greatly reduce the efficiency of low emissivity coating, so the damage detection of low emissivity coating becomes an important work. Based on convolution neural network, a model for automatic identification of coating damage with low emissivity is proposed. Firstly, the optical image data set of low emissivity coating is constructed and extended by means of data enhancement. After that, VGG-19 and ResNet-50 models are built based on tensorflow, and the cross entropy loss function is used in the models. Then, SGD, momentum, RMSprop and Adam are used to optimize the model. In the process of model optimization, the learning rate is adjusted to get the optimal model. The results show that when the learning rate is $5\times 10^{-5}$ and Adam method is used to optimize the model, the recognition accuracy of VGG-19 model is 90.64%, while that of ResNet-50 model is 94.14%. This paper is of great significance for the study of automatic damage identification of low emissivity coatings.

In this article, a multiple diffuse coding metasurface (MDCM) of independent polarization is designed to control the propagation direction of diffuse reflections under different polarizations and to improve the monostatic and bistatic RCS (radar cross section) reduction effect. First, a method for controlling the distribution range and propagation direction of the diffuse field is studied, and the diffuse field distribution of the random phase metasurface is optimized by a genetic algorithm to improve the uniformity of the diffuse scattering distribution. Then, the random phase distribution is superimposed on the periodic gradient phase distributions of the linear and hedge types in the orthogonal direction so that the main propagation direction of the diffuse metasurface deviates from the specular reflection region under different polarizations, showing single and two diffuse beams. Finally, the anisotropic unit cell with a rectangle inside and an improved Jerusalem cross on the outside is employed as the basic coding element of the MDCM due to its independent polarization phase response. The numerical and experimental results show that the MDCM features multiple diffuse scattering, independent polarization and angle insensitivity and can efficiently improve the monostatic and bistatic RCS reduction effect simultaneously. Because the scattered energies are redirected away from the specular reflection direction, the specular scattering reduction effect is better than the isotropic diffuse metasurface. The proposed method increases the difficulty of detection by single or netted radar and has the potential for the applications of stealth techniques.

A new hybrid Finite-Difference Time-Domain (hybrid FDTD) algorithm is proposed in this paper. This hybrid FDTD method combines the superiorities of explicit unconditionally stable FDTD (US-FDTD) and traditional FDTD methods to achieve unconditional stability and high calculation efficiency. US-FDTD is used in fine grids and the adjacent coarse grids subdomain and it breaks the Courant-Friedrich-Levy (CFL) condition. Traditional FDTD is used in the remaining coarse grids subdomain and it is a matrix free method. A compensation scheme is used on the subdomain boundary without compromising accuracy. Hybrid FDTD makes the explicit time marching with a uniform time step determined by the size of coarse grid in whole domain, which reduces the iteration time. In addition, because US-FDTD is only used in one of the subdomains, compared with global US-FDTD method, the matrix dimension of hybrid FDTD is reduced, which saves the time for eigenvalue solution. Numerical results show high efficiency and accuracy of the proposed method.

Ultrasonic non-destructive testing can effectively detect damage in aircraft composite materials, but traditional manual testing is time-consuming and labor-intensive. To realize the intelligent recognition of aircraft composite material damage, this paper proposes a 1D-YOLO network, in which intelligent fusion recognizes both the ultrasonic C-scan image and ultrasonic A-scan signal of composite material damage. Through training and testing the composite material damage data on aircraft skin, the accuracy of the model is 94.5%, the mean average precision is 80.0%, and the kappa value is 97.5%. The use of dilated convolution and a recursive feature pyramid effectively improves the feature extraction ability of the model. The effectively used Cascade R-CNN (Cascade Region-Convolutional Neural Network) improves the recognition effect of the model, and the effectively used one-dimensional convolutional neural network excludes non-damaged objects. Comparing our network with YOLOv3, YOLOv4, cascade R-CNN, and other networks, the results show that our network can identify the damage of composite materials more accurately.

Objective: Type II nuclear hormone receptors, including farnesoid X receptors (FXR), liver X receptors (LXR), and peroxisome proliferator-activated receptors (PPAR), which serve as drug targets for metabolic diseases, are permanently positioned in the nucleus and thought to be bound to DNA regardless of the ligand status. However, recent genome-wide location analysis showed that LXRα and PPARα binding in the liver is largely ligand-dependent. We hypothesized that pioneer factor Foxa2 evicts nucleosomes to enable ligand-dependent binding of type II nuclear receptors and performed genome-wide studies to test this hypothesis. Methods: ATAC-Seq was used to profile chromatin accessibility; ChIP-Seq was performed to assess transcription factors (Foxa2, FXR, LXRα, and PPARα) binding; and RNA-Seq analysis determined differentially expressed genes in wildtype and Foxa2 mutants treated with a ligand (GW4064 for FXR, GW3965, and T09 for LXRα). Results: We reveal that chromatin accessibility, FXR binding, LXRα occupancy, and ligand-responsive activation of gene expression by FXR and LXRα require Foxa2. Unexpectedly, Foxa2 occupancy is drastically increased when either receptor, FXR or LXRα, is bound by an agonist. In addition, co-immunoprecipitation experiments demonstrate that Foxa2 interacts with either receptor in a ligand-dependent manner, suggesting that Foxa2 and the receptor, bind DNA as an interdependent complex during ligand activation. Furthermore, PPARα binding is induced in Foxa2 mutants treated with FXR and LXR ligands, leading to the activation of PPARα targets. Conclusions: Our model requires pioneering activity for ligand activation that challenges the existing ligand-independent binding mechanism. We also demonstrate that Foxa2 is required to achieve activation of the proper receptor – one that binds the added ligand – by repressing the activity of a competing receptor.

In this work, the laser cleaning process of the surface coating for the Ti-6Al-4V titanium alloy was investigated using a pulsed laser, including cleaning time, laser power, and scanning speed. Meanwhile, the corresponding mechanism of laser cleaning was analyzed using the surface morphology, element content change, coating fracture cross section, and phase change. Results demonstrated that the quality of the coating removed by laser cleaning increased first and then decreased with the increase in the three key process parameters of laser cleaning time, laser power, and scanning speed, respectively. The superior laser cleaning efficiency was achieved when the laser power was 15 W, the cleaning time was 8, and the scanning speed was 400 mm/s. It is concluded that the removal of the titanium alloy surface coating is the result of the coupling of the cleaning mechanisms such as ablation and thermal expansion.

The aircraft skin is an important component of the aircraft, and its integrity affects the flight performance and safety performance of the aircraft. Damage detection technology with ultrasonic nondestructive testing as the core has played an important role in aircraft skin damage detection. Due to the difficulty in aircraft skin detection, relying solely on the ultrasonic A-scan equipment has very low detection efficiency. The introduction of artificial intelligence can effectively improve the detection efficiency. This paper establishes the one-dimensional convolutional neural network and single shot multibox detector network, which is based on the SSD network and uses dilated convolution to improve the real-time tracking of the ultrasonic probe. At the same time, 1DCNN is introduced to classify the ultrasonic A-scan signal. Finally, the detection results of the two are combined to achieve the reflection of the internal conditions of the aircraft skin. After testing, the algorithm can identify skin simulation specimens, and its recognition accuracy is 96.5%, AP is 90.9%, and kappa is 0.996. Comparing the improved SSD network with networks such as SSD, YOLOv3, and Faster R-CNN, the results show that the improved network used in this paper is more excellent and effective. At the same time, the detection effects of four types of optimization algorithms and five learning rates are studied, and finally, the corresponding signal classification model adopts Adam and the learning rate of 0.0001 has the best effect.

Infrared stealth is an important guarantee for weapon equipment to survive on the battlefield. Emissivity is an important index to measure the infrared stealth characteristics, and the emissivity is closely related to the surface roughness of objects. Therefore, it is an important work to study the relationship between emissivity and roughness. In this paper, the correlation between emissivity and roughness is studied, and the fitting curve and specific relationship are obtained. It is found that the correlation between the emissivity in the 8–14 µm band and roughness is stronger. The cast iron surface roughness dataset is constructed, and a new convolution neural network (CNN) is designed by the feature fusion method, which is the strengthen CNN. The network can effectively extract the detail features in the image, and the model is optimized by the Adam method. Finally, the deep learning model for measuring emissivity based on the optical image is obtained. The effects of different learning rate decay methods, such as piecewise constant decay, exponential decay, cosine annealing, and cosine annealing with warm restart, on the model optimization are studied. The results show that the cosine annealing with warm restart has the best effect, the error of the model is the smallest, and its mean square error is only 0.0014. This paper presents a new idea for the emissivity measurement, which is of great significance to emissivity measurement, infrared stealth, and infrared detection.

As a critical transformational process in the attributes of epithelial cells, epithelial-to-mesenchymal transition (EMT) is involved in tumor invasion, metastasis, and resistance to treatment, which contributes to the ultimate death of some patients with breast cancer. Glycogen synthase kinase-3-beta (GSK3β) is thought to be an EMT suppressor that down-regulates the protein, snail, a zinc finger transcription inhibitor, and regulates E-cadherin expression and the Wnt signaling pathway. Our previous studies have shown that Notch3 also inhibits EMT in breast cancer. In mammary gland cells, GSK3β physically bound and phosphorylated the intracellular domain of two Notch paralogs: N1ICD was positively regulated, but N2ICD was negatively regulated; however, the relationship between Notch3, GSK3β, and EMT in breast cancer is still unclear and crosstalk between Notch3 and GSK3β has not been widely investigated. In this study, we revealed that Notch3 was an essential antagonist of EMT in breast cancer cells by transcriptionally upregulating GSK3β. In breast cancer, MCF-7 and MDA-MB-231 cell lines, the silencing of Notch3 reduced GSK3β expression, which is sufficient to induce EMT. Conversely, ectopic Notch3 expression re-activated GSK3β and E-cadherin. Mechanistically, Notch3 can bind to the GSK3β promoter directly and activate GSK3β transcription. In human breast cancer samples, Notch3 expression is positively associated with GSK3β (r = 0.416, p = 0.001); moreover, high expressions of Notch3 and GSK3β mRNA are correlated to better relapse-free survival in all breast cancer patients via analysis in “the Kaplan–Meier plotter” database. In summary, our preliminary results suggested that Notch3 might inhibit EMT by trans-activating GSK3β in breast cancer cells. The suppression of Notch3 expression may contribute to EMT by transcriptionally downregulating GSK3β in breast cancer.

The basis of plasma stealth technology is the attenuation of electromagnetic waves by plasma. In this study, the calculation principle of the finite-difference time-domain (FDTD) method is introduced and a FDTD time–space coupling model of electromagnetic wave propagation in plasma is established. The time-domain variation characteristics of electromagnetic waves entering the plasma are analyzed. The plasma parameter distribution under different conditions obtained using the COMSOL fluid mechanics model is introduced into the FDTD model. The plasma reflectivity measurement experiment was carried out in a microwave anechoic chamber, and the influence of different experimental conditions on the plasma reflectivity was analyzed. The variation of reflectivity under different plasma parameter distributions is obtained. The results show that increasing electron density and plasma thickness and enhanced plasma distribution uniformity are beneficial for improving the attenuation effect of plasma on electromagnetic waves. These results provide a reference for the inductively coupled plasma parameter distribution in a closed quartz cavity, which provides a basis for the plasma to attenuate the electromagnetic waves.

The use of low-emissivity coatings as an infrared (IR) stealth method for weaponry is an important guarantee for the survival of weaponry in the battlefield. The damage on low-emissivity coatings leads to a significant drop in the IR stealth performance of weaponry. Therefore, this paper carries out a research on the intelligent damage identification of low-emissivity coatings based on convolutional neural networks (CNNs). A low-emissivity coating damage dataset with the characteristics of large changes in brightness, distortion of images, and large changes in the target scale is constructed. A composite network (CompoNet) based on feature fusion is designed to extract the detailed features of damage and identify damage types. Compared with VGG16, the damage identification accuracy of CompoNet is improved by 5.49% points. The idea of specific feature extraction layers (SFELs) is proposed. Three SFELs are designed to extract the color, texture, and contour features, which are important basis for judging damage types. The SFEL accelerates the convergence of the model and increases the identification accuracy of the model by 2.02% points. The generative adversarial network is used to generate low-emissivity coating damage images, which solves the problem that the number of damage images collected is small due to practical difficulties. The final damage identification accuracy of the CompoNet fused with SFELs (CompoNet with SFELs) constructed in this paper reaches 99.17%, which is much higher than other generic CNNs.

Abstract Background The aim of this study was to investigate the relationship between Talin-1 and stability of carotid atherosclerosis plaque and also find out the role of miRNA, as an upstream regulator, in regulating the expression level of Talin-1. Methods Human carotid plaques were obtained from 20 symptomatic carotid stenosis patients who underwent carotid endarterectomy (CEA) in our hospital between October 2014 and August 2017. Western blot analysis and immunohistochemistry was carried out to detect the distribution and expression level of Talin-1 in each plaque sample. The content of miRNAs in carotid plaque was decected by quantitative reverse transcription polymerase chain reaction (RT-qPCR), and the relative expression levels were calculated by 2-△△Ct method after the (cycle threshold) Ct value (power amplification knee point) was obtained. Dual-luciferase reporter assays were applied to verify the successful transfections. Finally, we compared all the groups with independent-samples t-test and one-way analysis of variance (ANOVA). Results Talin-1 was significantly downregulated in human unstable carotid plaque samples compared with stable carotid plaques (P

Abstract Background The mutational processes underlying non-coding cancer mutations and their biological significance in tumor evolution are poorly understood. To get better insights into the biological mechanisms of mutational processes in breast cancer, we integrate whole-genome level somatic mutations from breast cancer patients with chromatin states and transcription factor binding events. Results We discover that a large fraction of non-coding somatic mutations in estrogen receptor (ER)-positive breast cancers are confined to ER binding sites. Notably, the highly mutated estrogen receptor binding sites are associated with more frequent chromatin loop contacts and the associated distal genes are expressed at higher level. To elucidate the functional significance of these non-coding mutations, we focus on two of the recurrently mutated estrogen receptor binding sites. Our bioinformatics and biochemical analysis suggest loss of DNA-protein interactions due to the recurrent mutations. Through CRISPR interference, we find that the recurrently mutated regulatory element at the LRRC3C-GSDMA locus impacts the expression of multiple distal genes. Using a CRISPR base editor, we show that the recurrent C→T conversion at the ZNF143 locus results in decreased TF binding, increased chromatin loop formation, and increased expression of multiple distal genes. This single point mutation mediates reduced response to estradiol-induced cell proliferation but increased resistance to tamoxifen-induced growth inhibition. Conclusions Our data suggest that ER binding is associated with localized accumulation of somatic mutations, some of which affect chromatin architecture, distal gene expression, and cellular phenotypes in ER-positive breast cancer.

The length of buried pipelines usually extends thousands of meters or more in engineering, and it is difficult to carry out full-scale tests in the laboratory. Therefore, considering the seriousness of pipeline damage and the difficulty of operating tests and other test limitations, it is necessary to develop a reasonable method to simplify the length of the model for a practical lab test. In this research, an equivalent spring model was established to simulate the small deformation section of the pipeline far away from the fault and the effect of fault displacements, pipeline diameters, wall thicknesses, buried depths, soil materials, and spring constraints on the mechanical properties of pipelines was analyzed. Based on the finite element model using ABAQUS software, the results of the shell model with fixed boundary at both ends were compared; in addition, the dynamic effect of pipelines was investigated. The results show that the two-end spring device can better control the size of the test model and enhance the reliability of the test results. The vibration response of the pipeline mainly depends on the inconsistent movement of soil at both ends of the fault. The analysis results show that choosing a larger pipeline diameter, smaller buried depth, noncohesive backfill soil, and spring with a smaller elastic coefficient is beneficial to reduce pipeline strain and resist pipeline deformation. A simplified formula of the axial compressive strain of buried pipelines across oblique-slip fault is obtained.

The complex pore structure of low-permeability sandstone reservoir makes it difficult to characterize the heterogeneity of pore throat. Taking the reservoir of Sanjianfang formation in QL oilfield as an example, the fractal dimension of different storage spaces is calculated by using fractal theory based on casting thin section, scanning electron microscope, and high-pressure mercury injection, and the correlation between porosity, permeability, and contribution of different storage space permeabilities is analyzed. The results show that the reservoir of Sanjianfang formation in QL oilfield mainly develops small pores, fine pores, and micropores, and the fractal dimension of micropore structure is between 2.6044 and 2.9982, with an average value of 2.8316. The more complex the pore structure is, the stronger the microheterogeneity is. The higher the fractal dimension, the more complex the pore structure and the smaller the porosity and permeability. The fractal dimensions of small pores, fine pores, and micropores increase successively with the decrease in pore radius, and the microstructure heterogeneity of large pores is weaker than that of small pores. It provides a theoretical basis for the exploration and development of low-permeability sandstone reservoirs.

The baseline-free damage detection method of Lamb waves has the potential to obtain damage information efficiently in plate structures through damage scattering signals. However, the missing detection of damage occurs occasionally due to the angular scattering characteristic of Lamb waves. To solve this problem, a novel baseline-free damage detection approach based on path scanning at the detection region edges using mobile piezoelectric transducers is proposed herein. Several sensing points carrying separated damage scattering signals were picked out from the scanning paths. By removing the direct and boundary reflected signals, the damage signals were extracted and exported to a delay-and-sum imaging method to locate the damage. Two experiments with and without mobile transducers were conducted to validate the proposed method on an aluminum plate with artificially fabricated crack-like damage. The results show that the proposed baseline-free approach can locate the crack-like damage with high accuracy and efficiency and avoid potential loss of damage information. The proposed baseline-free method provides a novel and practical damage detection approach when considering the angular-dependent scattering characteristic of Lamb waves and can enhance the credibility of results in damage detection.

To study the effects of an axial magnetic field on the discharge characteristics of Ar inductively coupled plasma, a set of discharge plasma generators was designed. The plasma parameters such as electron temperature and electron density were diagnosed with a Langmuir probe. The research showed that as the air pressure was 10 Pa, with the increase in axial magnetic field intensity, the electron temperature and electron density reduced continuously in the central discharge region, while the threshold power of discharge mode transition increased constantly. The analysis suggested that due to the circumnutation of charged particles acted upon by Lorentz force, the introduction of the axial magnetic field had a constraint effect on the particle movement and energy transfer and decreased the collision between the high-energy electron in the discharge sheath and the electron in the central region, thereby reducing the electron density and inductive coupling efficiency. From further analysis of the electron energy probability function, it could be found that in the E mode, the constraint effect of the axial magnetic field on electron motion was more obvious. The proportion of the high-energy electron (>27 eV) was apparently higher than that in the H mode, and the electron energy distribution was more even. This was caused by less electron collision.

Background/Aims: RNA elements such as catalytic RNA, riboswitch, microRNA, and long non coding RNA (lncRNA) play central roles in many cellular processes. Studying diverse RNA functions require large quantities of RNA for precise structure analysis. Current RNA structure and function studies can benefit from improved RNA quantity and quality, simpler separation procedure and enhanced accuracy of structural analysis. Methods: Here we present an optimized protocol for analyzing the structure of any RNA, including in vitro transcription, size-exclusion chromatography (SEC) based denaturing purification and improved secondary structure analysis by chemical probing. Results: We observed that higher Mg2+, nucleoside triphosphate (NTP) concentrations and longer reaction duration can improve the RNA yield from in vitro transcription, specifically for longer and more complicated constructs. Our improved SEC-based denaturing RNA purification effectively halved the experiment duration and labor without introducing any contaminant. Finally, this study increased the accuracy and signal-to-noise ratio (SNR) of selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) chemical probing for analyzing RNA structure. Conclusion: Part or all of our modified method can improve almost any RNA-related study from protein-RNA interaction analysis to crystallography.

Abstract Background This study aimed to assessed whether Talin-1 is involved in the pathogenesis of aortic dissection via regulating vascular smooth muscle cell (VSMC) biological function. Methods Human aortic samples were obtained from organ donors who died from nonvascular diseases as normal controls and from patients undergoing surgical repair of thoracic aortic dissection. The expression level and distribution of Talin-1 were detected using westernblot analysis and immunohistochemistry in each sample. We inhibited the expression of Talin-1 via RNA interference in VSMCs. VSMC proliferation was detected by Cell-counting Kit-8 analyses. Scratch test and flow cytometry were used to identify the migration and apoptosis ability. Antibody microarray analysis and qRT-PCR were used to detect some protein and mRNA changes which were induced by Talin-1 downregulation. Results Talin-1 was significantly downregulated in the media of aortic dissection samples compared with controls (P

Setting the thickness and pressure for an absorbing plasma is difficult in plasma stealth engineering. In this study we established a numerical model of electromagnetic wave propagation in radiofrequency plasma using a z-transformed, finite-difference, time-domain method. We studied the effects of thickness and pressure on the reflectance, transmittance and attenuation of the plasma under three typical electron density distributions: uniform, axially symmetric and monotonic. The results show that the electron density distribution has a significant influence on the electromagnetic wave transmission characteristics. The attenuation effect reaches a maximum when the electron density is increased monotonically along the wave propagation direction. An increase in thickness can significantly increase the attenuation rate of the incident wave and reduce the transmittance, but has little effect on the reflectance. An increase in air pressure reduces the reflectance of the incident wave and increases the transmittance and the attenuation rate. However, once the air pressure exceeds a certain threshold, any further increase in air pressure will no longer enhance the attenuation rate.

Aiming at the carbon fiber reinforced plastics (CFRP) of poor electrical conductivity which will lead to thermal stress damage due to intense Joule effect under lightning strikes, electro-thermal-structure coupling models of CFRP lightning strikes were established. By analyzing the thermal strain, thermal stress and status of the modified and unmodified CFRP, their thermal expansion characteristics were compared. Compared with the unmodified CFRP, the thermal strain peak value of the modified CFRP is respectively increased by 36.33%, 53.92%, 66.62%, and the thermal stress peak value of them is respectively reduced by 22.63%, 26.53%, and 9.45% in the absence of dielectric breakdown. But in the breakdown state, both specimens appear complete delamination damage, the protective effect of matrix modification becomes invalid. The thermal stress damage is found having little effect on the damage morphology of the specimens after lightning strikes by comparing their CT images.

Coupled electrical–thermal finite element analysis (FEA) models are widely adopted to analyze the thermal ablation damage of carbon fiber reinforced polymer (CFRP) caused by lightning, but it is still difficult to analyze the ablation due to its complex space geometry. According to the principle of computerized tomography (CT), tomographic images of FEA models’ temperature fields with different thicknesses were obtained to calculate the mass loss and compare the damage morphology. The four areas including Area 0, Area I, Area II, and Area III; were separated from the temperature fields in terms of different vaporization and pyrolysis temperature ranges of carbon fiber (CF) and resin matrix. Ablation mass losses were calculated by pixel statistics and tomographic intervals, which were consistent with the experimental results. The maximum ablation area of unprotected CFRP was found on the tomography images of 50 μm rather than the surface by comparing tomographic images with different thickness due to the influence of the thermal radiation, but this effect was not found in CFRP protected by copper mesh. Some other phenomena, including continuous evolutions of ablation areas and the influence of the intersection angle on the direction of the ablation extension, were also discovered.