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Abstract Ru single-atom catalysts have great potential to replace toxic mercuric chloride in acetylene hydrochlorination. However, long-term catalytic stability remains a grand challenge due to the aggregation of Ru atoms caused by over-chlorination. Herein, we synthesize an asymmetric Ru-In atomic pair with vinyl chloride monomer yield (>99.5%) and stability (>600 h) at a gas hourly space velocity of 180 h−1, far surpassing those of the Ru single-atom counterparts. A combination of experimental and theoretical techniques reveals that there is a strong d-p orbital interaction between Ru and In atoms, which not only enables the selective adsorption of acetylene and hydrogen chloride at different atomic sites but also optimizes the electron configuration of Ru. As a result, the intrinsic energy barrier for vinyl chloride generation is lowered, and the thermodynamics of the chlorination process at the Ru site is switched from exothermal to endothermal due to the change of orbital couplings. This work provides a strategy to prevent the deactivation and depletion of active Ru centers during acetylene hydrochlorination.

ObjectivesThe purpose of this study was to develop and validate a new feature fusion algorithm to improve the classification performance of benign and malignant ground-glass nodules (GGNs) based on deep learning.MethodsWe retrospectively collected 385 cases of GGNs confirmed by surgical pathology from three hospitals. We utilized 239 GGNs from Hospital 1 as the training and internal validation set, and 115 and 31 GGNs from Hospital 2 and Hospital 3, respectively, as external test sets 1 and 2. Among these GGNs, 172 were benign and 203 were malignant. First, we evaluated clinical and morphological features of GGNs at baseline chest CT and simultaneously extracted whole-lung radiomics features. Then, deep convolutional neural networks (CNNs) and backpropagation neural networks (BPNNs) were applied to extract deep features from whole-lung CT images, clinical, morphological features, and whole-lung radiomics features separately. Finally, we integrated these four types of deep features using an attention mechanism. Multiple metrics were employed to evaluate the predictive performance of the model.ResultsThe deep learning model integrating clinical, morphological, radiomics and whole lung CT image features with attention mechanism (CMRI-AM) achieved the best performance, with area under the curve (AUC) values of 0.941 (95% CI: 0.898-0.972), 0.861 (95% CI: 0.823-0.882), and 0.906 (95% CI: 0.878-0.932) on the internal validation set, external test set 1, and external test set 2, respectively. The AUC differences between the CMRI-AM model and other feature combination models were statistically significant in all three groups (all p

Timothy syndrome, an extremely rare disease, is closely associated with a mutation in CACNA1C gene, which encodes the cardiac L-type voltage-gated calcium channel (Cav1.2). In this study, we generated a human induced pluripotent stem cell (iPSC) line from a Timothy syndrome infant carrying heterozygous CACNA1C mutation (transcript variant NM_000719.7c.1216G>A: p.G406R). The generated iPSC line showed typical stem cell morphology, positively expressed pluripotency and proliferation markers, normal karyotype, and trilineage differentiation potential. Therefore, this patient-specific iPSC can be of great significance in investigating the mechanisms underlying Timothy syndrome, and hence establishing effective intervention strategies.

Abstract Capturing gaseous mercury (Hg0) from sulfur dioxide (SO2)-containing flue gases remains a common yet persistently challenge. Here we introduce a low-temperature sulfur chemical vapor deposition (S-CVD) technique that effectively converts SO2, with intermittently introduced H2S, into deposited sulfur (Sd 0) on metal sulfides (MS), facilitating self-sustained adsorption of Hg0. ZnS, as a representative MS model, undergoes a decrease in the coordination number of Zn–S from 3.9 to 3.5 after Sd 0 deposition, accompanied by the generation of unsaturated-coordinated polysulfide species (Sn 2–, named Sd *) with significantly enhanced Hg0 adsorption performance. Surprisingly, the adsorption product, HgS (ZnS@HgS), can serve as a fresh interface for the activation of Sd 0 to Sd * through the S-CVD method, thereby achieving a self-sustained Hg0 adsorption capacity exceeding 300 mg g−1 without saturation limitations. Theoretical calculations substantiate the self-sustained adsorption mechanism that S8 ring on both ZnS and ZnS@HgS can be activated to chemical bond S4 chain, exhibiting a stronger Hg0 adsorption energy than pristine ones. Importantly, this S-CVD strategy is applicable to the in-situ activation of synthetic or natural MS containing chalcophile metal elements for Hg0 removal and also holds potential applications for various purposes requiring MS adsorbents.

Abstract The existing conventional treatments for breast cancer, including immune checkpoint blockade, exhibit limited effects in some cancers, particularly triple-negative breast cancer. Epigenetic alterations, specifically DNMT and HDAC alterations, are implicated in breast cancer pathogenesis. We demonstrated that DNMTs and HDACs are overexpressed and positively correlated in breast cancer. The combination of DNMT and HDAC inhibitors has shown synergistic antitumour effects, and our previously designed dual DNMT and HDAC inhibitor (termed DNMT/HDACi) 15a potently inhibits breast cancer cell proliferation, migration, and invasion and induces apoptosis in vitro and in vivo. Mechanistically, 15a induces a viral mimicry response by promoting the expression of endogenous retroviral elements in breast cancer cells, thus increasing the intracellular level of double-stranded RNA to activate the RIG-I–MAVS pathway. This in turn promotes the production of interferons and chemokines and augments the expression of interferon-stimulated genes and PD-L1. The combination of 15a and an anti-PD-L1 antibody had an additive effect in vivo. These findings indicate that this DNMT/HDACi has immunomodulatory functions and enhances the effectiveness of immune checkpoint blockade therapy. A novel dual DNMT and HDAC inhibitor induces viral mimicry, which induces the accumulation of dsRNA to activate tumoral IFN signalling and cytokine production to enhance the immune response in breast cancer.

Deep-learning based approaches have been proven effective for Digital Elevation Model (DEM) super-resolution (SR) tasks. Previous networks typically treat DEM elevation values as single-channel image for input. However, DEM images alone cannot fully capture spatial and terrain features. Shaded relief images (SRIs), derived from DEMs, serve as crucial visual cues that intuitively convey terrain characteristics, addressing the limitations of DEM images and providing synergistic benefits for training DL models. The primary challenge in utilizing SRIs for guiding DEM SR lies in accurately selecting a consistent structure to extract and effectively integrate features from SRIs and DEMs. In this study, we propose an Attention-based Hierarchical Terrain Fusion (AHTF) framework for guided DEM SR. Specifically, an Attention-based Feature Fusion Module (AFFM) is designed to efficiently fuse relevant information from LR DEM and SRI, which includes a feature enhancement block to select valuable features and a feature recalibration block to fuse diverse terrain features. Additionally, we optimize the loss function from the perspectives of terrain analysis and visual effects. We validate AHTF on our newly constructed real-world Shade-DEM SR dataset and two open-source DEM SR datasets. Compared to the current state-of-the-art methods, our AHTF achieves the best results in terms of root mean square error (RMSE) for elevation, slope, and aspect. Furthermore, the extracted stream networks are closer to real-world conditions. This study offers new insights and methods for further research and application in the field of DEM super-resolution. Our dataset can be obtained at https://doi.org/10.6084/m9.figshare.25590945.

Colorectal cancer is the second most prevalent and deadly cancer worldwide. The emergence of immune checkpoint therapy has provided a revolutionary strategy for the treatment of solid tumors. However, less than 5 % of colorectal cancer patients respond to immune checkpoint therapy. Thus, it is of great scientific significance to develop “potentiators” for immune checkpoint therapy. In this study, we found that knocking down different DNMT and HDAC isoforms could increase the expression of IFNs in colorectal cancer cells, which can enhance the effectiveness of immune checkpoint therapy. Therefore, the combined inhibition of DNMT and HDAC cloud synergistically enhance the effect of immunotherapy. We found that dual DNMT and HDAC inhibitors C02S could inhibit tumor growth in immunocompetent mice but not in immunocompromised nude mice, which indicates that C02S exerts its antitumor effects through the immune system. Mechanistically, C02S could increase the expression of ERVs, which generated the intracellular levels of dsRNA in tumor cells, and then promotes the expression of IFNs through the RIG-I/MDA5-MAVS signaling pathway. Moreover, C02S increased the immune infiltration of DCs and T cells in microenvironment, and enhanced the efficacy of anti-PD-L1 therapy in MC38 and CT26 mice model. These results confirmed that C02S can activate IFNs through the RIG-I/MDA5-MAVS signaling pathway, remodel the tumor immune microenvironment and enhance the efficacy of immune checkpoint therapy, which provides new evidence and solutions for the development of “potentiator” for colorectal cancer immunotherapy.

Currently, video question answering (VideoQA) algorithms relying on video-text pretraining models employ intricate unimodal encoders and multimodal fusion Transformers, which often lead to decreased efficiency in tasks such as visual reasoning. Conversely, video question answering algorithms based on graph neural networks often exhibit suboptimal performance in video description and reasoning, attributed to their simplistic graph construction and cross-modal interaction designs, necessitating additional pretraining data to mitigate these performance disparities. In this work, we introduce the Spatio-temporal Graph Convolution Transformer (STCT) model for VideoQA. By leveraging Spatio-temporal Graph Convolution (STGC) and dynamic graph Transformers, our model explicitly captures the spatio-temporal relationships among visual objects, thereby facilitating dynamic interactions and enhancing visual reasoning capabilities. Moreover, our model introduces a novel cross-modal interaction approach utilizing dynamic graph attention mechanisms to adjust the attention weights of visual objects based on the posed question, thereby augmenting multimodal cooperative perception. By addressing the limitations of graph-based algorithms dependent on pretraining for performance enhancement through meticulously designed graph structures and cross-modal interaction mechanisms, our model achieves superior performance in visual description and reasoning tasks with simpler unimodal encoders and multimodal fusion modules. Comprehensive analyses and comparisons of the model’s performance across multiple datasets, including NExT-QA, MSVD-QA, and MSRVTT-QA datasets, have confirmed its robust capabilities in video reasoning and description.

Abstract PARP inhibitors and HDAC inhibitors have been approved for the clinical treatment of malignancies, but acquired resistance of or limited effects on solid tumors with a single agent remain as challenges. Bioinformatics analyses and a combination of experiments had demonstrated the synergistic effects of PARP and HDAC inhibitors in triple-negative breast cancer. A series of novel dual PARP and HDAC inhibitors were rationally designed and synthesized, and these molecules exhibited high enzyme inhibition activity with excellent antitumor effects in vitro and in vivo. Mechanistically, dual PARP and HDAC inhibitors induced BRCAness to restore synthetic lethality and promoted cytosolic DNA accumulation, which further activates the cGAS–STING pathway and produces proinflammatory chemokines through type I IFN-mediated JAK–STAT pathway. Moreover, these inhibitors promoted neoantigen generation, upregulated antigen presentation genes and PD-L1, and enhanced antitumor immunity when combined with immune checkpoint blockade therapy. These results indicated that novel dual PARP and HDAC inhibitors have antitumor immunomodulatory functions in triple-negative breast cancer. Novel dual PARP and HDAC inhibitors induce BRCAness to restore synthetic lethality, activating tumoral IFN signaling via the cGAS–STING pathway and inducing cytokine production, promoting neoantigen generation and presentation to enhance the immune response.

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

Fruit storage is optimized at the lowest safe temperature to maximize storage life whilst avoiding chilling or freezing injury. The few published studies of freezing in kiwifruit (Actinidia spp.) have been conducted with A. chinensis var. deliciosa ‘Hayward’ fruit, with freezing temperatures reported in the range of −1.5 °C to −2.5 °C. In China, a large number of kiwifruit cultivars have been commercialized recently, with at least ten being commonly planted. In this research, freshly harvested fruits from 45 kiwifruit germplasm accessions were measured for freezing point, soluble solids content (SSC) and water content (WC). The difference in freezing point of different tissue zones within a fruit and after different periods of cold storage were determined for the fruit of three representative main cultivars: the green-fleshed ‘Hayward’, the red-fleshed A. chinensis var. chinensis ‘Donghong’, and the yellow-fleshed interspecific hybrid A. eriantha × A. chinensis ‘Jinyan’. The the freezing point of kiwifruit was found to be highly correlated with the SSC. This relationship was found irrespective of whether the measurement was made at harvest, at different places in the fruit, or after ripening during storage. These findings agree with previous reports for kiwifruit and other fruits, although it appears that the relationship may differ among kiwifruit cultivars, something not previously reported and which requires further confirmation.

Abstract During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013–2022), the first ten-year stage of the lifespan CCNP (2013–2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0–17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the “Chinese Data-sharing Warehouse for In-vivo Imaging Brain” in the Chinese Color Nest Project (CCNP) – Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.

As a response to the damage caused by the spread of COVID-19, the Chinese government has implemented severe quarantine measures that have greatly affected the operational patterns of small and medium-sized enterprises (SMEs). This paper explores the critical role of dynamic capabilities (DCs) in helping Chinese SMEs manage crises, adjust their business strategies, and mitigate the uncertainty caused by the epidemic. Although the importance of DCs in promoting organizational resilience is well recognized, academic research on their specific contributions to business model innovation (BMI) and SME performance improvement during crises remains scarce. Our study fills this gap by pioneering the development and empirical testing of a microintegrated mediation model linking DCs, BMI and organizational performance. By surveying 257 Chinese SMEs severely affected by a pandemic, we verify our hypotheses using partial least squares structural equation modeling (PLS-SEM). Our results strongly show a positive relationship between DCs and BMI and SME performance. In addition, we found that BMI plays a partial mediating role in the interrelationship between DCs and SME performance. Our findings clarify the critical role of BMI as a channel through which SMEs' DCs can be transformed into higher performance in the face of sudden crises. Thus, our results not only contribute to the broader discussion of strategic management and organizational theory but also provide theoretical and practical insights into the mechanisms by which SMEs can increase their flexibility and resilience in a crisis. Thus, our results not only contribute to the broader discussion of strategic management and organizational theory but also provide theoretical and practical insights into the mechanisms by which SMEs can increase their flexibility and resilience in a crisis. Importantly, this study suggests policy and market strategies that can support SMEs in leveraging DCs and BMI for sustained performance, thereby contributing valuable insights for policymakers and business leaders aiming to fortify economic stability and growth in the face of global health emergencies.

Summary: Background: Tafolecimab, a fully human proprotein convertase subtilisin/kexin type 9 (PCSK9) monoclonal antibody developed for the treatment of hypercholesterolemia, demonstrated robust lipid-lowering efficacy and favorable safety in previous short-term studies. We aimed to assess the long-term efficacy and safety of tafolecimab in Chinese non-familial hypercholesterolemia (non-FH) patients. Methods: Non-FH patients at high or very-high cardiovascular risk with screening low-density lipoprotein cholesterol (LDL-C) level ≥1.8 mmol/L or non-FH patients with screening LDL-C level ≥3.4 mmol/L and on stable lipid-lowering therapy for at least 4 weeks, were randomized in a 2:2:1:1 ratio to receive subcutaneous tafolecimab 450 mg Q4W, tafolecimab 600 mg Q6W, placebo 450 mg Q4W, or placebo 600 mg Q6W, respectively, in the 48-week double-blind treatment period. The primary endpoint was the percent change from baseline to week 48 in LDL-C levels. Findings: A total of 618 patients were randomized and 614 patients received at least one dose of tafolecimab (n = 411) or placebo (n = 203). At week 48, tafolecimab induced significant reductions in LDL-C levels (treatment differences versus placebo [on-treatment estimand]: −65.0% [97.5% CI: −70.2%, −59.9%] for 450 mg Q4W; −57.3% [97.5% CI: −64.0%, −50.7%] for 600 mg Q6W; both P

Highlights A facile chelated coupled pyrolysis strategy was reported to prepare Mn single-cluster Hg0 adsorbent. The nitrogen-containing group in biomass determined the size of atomically dispersed manganese. The presence of oxygen and small size Mn clusters in biochar promoted Hg0 adsorption.

In the field of aerospace and advanced equipment manufacturing, accurate response analysis has been paid more attention, requiring a more comprehensive study of the variation of mechanical parameters with the service environment. The damping variation characteristics of 304 aluminum alloy, Sa564 high-strength alloy, GW63K magnesium alloy, and Q235 steel were investigated in this paper, which plays a significant role in the dynamic responses of structures. Variable damping ratios were revealed by the damping tests based on a dynamic mechanical analysis (DMA). The numerical method of temperature/frequency-dependent damping parameters in stochastic dynamics was focused on. With a large variation in the damping ratio, a numerical constitutive relation for temperature-dependent damping was proposed, and an efficient stochastic dynamics method was derived to analyze the responses of structures based on the pseudo excitation method (PEM) and variable damping theory. The computational accuracy and validity of the proposed method are confirmed during the vibration tests and numerical analysis. Based on the comparison results of the two damping models and the experiments on GW63K alloy, we proved that the proposed method is more accurate to the real response of the actual engineering structure. The differences in dynamic responses between the constant damping and experiments are significant, and more attention should be paid to the numerical method of stochastic dynamic response of variable damping materials in the aviation and aerospace fields and high-temperature environments.

The basal eudicot Epimedium (Barrenwort) exhibits innovative floral morphology in the form of petal spurs filled with nectar and petaloid sepals. The B-class MADS-box genes APETALAS3 (AP3) and PISTILLA (PI) determine sepal and petal identity while the C-class gene AGAMOUS (AG) determines carpel and stamen identity in Arabidopsis. Complex histories of gene duplication resulted in subsequent subfunctionalization or neofunctionalization of paralogs. Here, a total of four B- and two C-class genes were successfully isolated from E. sagittatum. Phylogenetic analysis showed that EsAP3-1, EsAP3-2, and EsAP3-3 are part of the AP3 clade; EsPI is part of the PI group; and EsAG and EsAG11 clustered into the AG and AG11 groups, respectively. Quantitative real-time PCR was utilized to detect the expression patterns of these genes, and all B-class genes except for EsAP3-3 were found to be universally expressed. The transcribed EsAG and EsAG11 genes were confined to reproductive organs. In addition, yeast three and two hybrid assays were used to explore the status of protein complexes. EsAP3-1 was found to have broadly interactive partners, and EsPI can form a heterodimer with EsAP3-1 and EsAP3-2. Transgenic EsAG overexpression in wild-type Arabidopsis confirmed the conserved function determining carpel development.

Multimodal matching remains a difficult and pressing problem in the imaging processing community. Accurate and robust multimodal matching is important for the performance of applications, such as registration and fusion. Traditional image matching algorithms cannot effectively handle multimodal images with severe nonlinear radiometric distortion (NRD). In this article, a novel multiscale template matching algorithm for multimodal image matching is proposed to address this problem. We propose a novel frequency-domain convolutional map based on the wavelet transform and phase congruency to construct a feature description map that significantly reduces the NRD between multimodal images. The development of omnidirectional aggregated feature vectors with rotational invariance also helped to achieve robustness on rotated images. Finally, a multiscale template matching strategy improved the matching performance on multimodal images with displacement and scale variations. To improve the time efficiency of the algorithm, most of the complex computations in this article are performed in the frequency domain. According to the experimental findings on six multimodal image datasets, the method can obtain accurate and robust matching results between multimodal images. Through qualitative and quantitative evaluations, the method outperforms several mainstream multimodal image matching algorithms in terms of matching accuracy, success rate, and time consumption.

Amino acid (AA) was used as a green grafting agent to functionalize polyepoxysuccinic acid (PESA), and three AA-modified PESA (AA-PESA) CaCO3 scale inhibitors were obtained to change the structural singleness of PESA and further improve its comprehensive properties. The structures of AA-PESA were characterized by Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance hydrogen (1HNMR) spectroscopy. The molecular weights of AA-PESA were analyzed by gel permeation chromatography (GPC). The synthesis technology of AA-PESA was optimized by single-factor and orthogonal experiments. The CaCO3 scale inhibition performance of AA-PESA was studied by the static scale-inhibition method, and the scale inhibition mechanism was analyzed. Results showed that AA-PESAs had the same synthesis process: n(PESA):n(AA) = 1:0.625, reaction temperature of 95 °C, and reaction time of 2 h. In the water system with pH 7.0–8.5, agent concentration of 8–10 mg/L, action period of 10–14 h, ambient temperature

ObjectiveTo develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics.MethodsThis retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise.ResultsThe model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%.ConclusionThe baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.

IntroductionAircraft noise is one of the most significant sources of environmental pollution in large cities. During the COVID-19 pandemic, strict lockdown in community might increase residents’ discomfort with the noise, which could disrupt public activities and reduce subjective well-being. Most of the existing studies considered aircraft noise as a single sound source, which have ignored the influence of other sounds in the community. This paper applied field survey to identify the soundscape and non-acoustic factors related to aircraft noise evaluation.MethodsPaper questionnaires were delivered to select residents of three sample residential areas near Shenzhen Bao’an International Airport to investigate residents’ general health, evaluation of aircraft noise, community activities, and attitudinal factors. The relationship between respondent’s noise evaluations and subjective factors were investigated through statistical analyses controlling for measured aircraft noise levels and the existence of soundscape facilities.ResultsThe results indicated that the negative effects of aircraft noise were enhanced during the lock down, especially for frequent space users and those residents in poor health status. Under conditions of similar levels of aircraft noise exposure, communities with more birdsong and fountain sounds had lower proportion of highly annoyed respondents and higher level of soundscape ratings. This paper further indicated that personal factors including fear of air travel, noise sensitivity, and the frequency of outdoor activity had increased the level of annoyance to aircraft noise, while higher degree of annoyance to aircraft noise was associated with poor health status.DiscussionThe findings implied the moderating effects of subjective factors and the restorative effects of natural sounds, which could inform aircraft noise control and community consultation strategies by protecting vulnerable populations and creating community soundscape facilities. Future research might conduct a pre- and post-experiment to estimate the potential causal impact of the soundscape intervention.

Business model innovation (BMI) is an emerging field that has attracted much attention from scholars and practitioners. However, the literature on BMI is fragmented and inconsistent, lacking a comprehensive and systematic framework. This study aims to fill this gap by conducting a literature review of 272 peer-reviewed articles on BMI published between 2010 and 2022. We analyze the articles based on six dimensions: antecedents, processes, types, barriers, outcomes, and moderators/mediators of BMI. We synthesize the findings and propose an integrated theoretical model that captures the complex relationships among these dimensions. We also identify several research gaps and directions for future studies. This study contributes to the BMI literature by providing a clearer understanding of this phenomenon and offering practical guidance for various types of businesses.

A military helicopter is easily attacked by bullets in a battlefield environment. The composite blade is the main lifting surface and control surface of the helicopter. Its ballistic performance directly determines the vulnerability and survivability of the helicopter in the battlefield environment. To study the ballistic performance of the composite helicopter blade, the damage characteristics of the impacted composite rotor blade are obtained by experiments. A numerical simulation model is established by applying Abaqus software to predict the blade ballistic damage. The three-dimensional progressive damage failure model is used to analyze the ballistic damage under the experimental conditions. The effectiveness and accuracy of the numerical simulation model are verified through a comparison with the experimental results. The ballistic damage of composite blades under three experimental conditions was investigated. The results show that the ballistic damage type of composite blade mainly includes delamination, fiber breakage, and foam collapse. The damage to the composite material at the position of bullet incidence is mainly local shear fracture, while the damage to the composite material at the exit position is mainly fiber tensile fracture. The ballistic damage size of the composite blade is closely related to the ballistic position, incident angle, and structure characteristics along the ballistic path. The larger the incident angle, the smaller the ballistic damage size of the blade. The greater the structural stiffness of the structure near the exit, the greater the damage size of the exit. The numerical simulation model presented in this paper can provide a reference for research on the ballistic performance of composite helicopter blades.

Deep learning, which is a dominating technique in artificial intelligence, has completely changed image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications and future trends. Our review focuses on research published from 2016 to the present, with a specific focus on deep-learning-based approaches in the last five years. We divided all related algorithms into three categories, including the conventional image classification approach, the machine learning-based approach and deep-learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in four aspects including climate research, navigation, geographic information systems (GIS) production and others. This paper also provides insightful observations and inspiring future research directions.

As a crucial component of rotor systems, tension and torsion (TT) straps, recognized for their compact structure, have been adopted in advanced helicopters such as the SB > 1, EC145, and Mi-26. This paper presents an analytical investigation and experimental study on the key performance of TT straps. A method for rapidly evaluating the performance of the torsional deformation segment was developed. The size parameters and material properties of the torsional deformation segment that greatly influence the torsional stiffness and the stress distributions of TT straps were comprehensively identified and clearly investigated. Moreover, an experimental study of four cases of TT straps was carried out to verify the influence of the torsional deformation segment length, material, and connector segment structure on TT strap performance. The experimental results confirm that the rapid evaluation method provides high accuracy in assessing the stiffness and stress performance of TT straps, with deviations in stiffness less than 10%. The correlation between the calculated stress and shear forces and the experimental failure modes of the connector segments validates the effectiveness of the method in capturing key parameters. This research provides a theoretical and practical basis for designing critical key parameters of TT straps, facilitating a dramatic reduction in performance assessment time from days to hours and enabling the immediate identification of enhancement strategies. This accelerates the design process, thereby contributing to enhanced design efficiency and reduced costs.

Abstract Mobile edge computing (MEC) is considered as a key enabler for the industrial internet of things (IIoT) to cope with the ever-increasing communication and computing demands of nodes. In consideration of the limited resource of the IIoT nodes, it is necessary to design cost-effective multi-hop data transmission schemes for mobile edge IIoTs. However, most of the traditional schemes have to spend enormous cost to meet the reliability requirement, which cannot support timely information processing of MEC-based IIoT. In this article, the probabilistic cooperative coded forwarding (PCCF) scheme for multi-hop data transmission in mobile edge IIoTs is proposed to address the above problem. First, the data packets are encoded at a source IIoT node using the systematic sparse network coding (SSNC) mechanism, then the source broadcasts the coded packets to its one-hop neighbors. To minimize the required number of redundant coded packets, the sparsity of coded packets is optimized. Second, the nodes which received the packets will become volunteer relay nodes and forward the coded packets using the cooperative coded forwarding (CCF) mechanism. The volunteer relays first forward the received coded packets with a forwarding probability, and then re-encode a pair of received coded packets and broadcast the re-encoded packet with a re-encoding probability. To guarantee the broadcast performance while minimizing the transmission number at relay nodes, the feasible forwarding and re-encoding probability are provided. Third, the receiver nodes will try to decode the received coded and re-encoded packets and recover data packets without sending acknowledgments. Finally, through a series of experiments, we verify the accuracy of analytical approximations and also find out the optimal sparsity of coded packets and the existence of minimum transmission numbers. These provide insights for further optimization of multi-hop data transmission in mobile edge IIoTs.

Introduction: Focused ultrasound ablation surgery (FUAS) has been emerging to treat a wide range of conditions non-invasively and effectively with promising therapeutic outcomes. The focusing capability of an ultrasound transducer (i.e., focus shift, beam distortion, and acoustic pressure at the focus) determines the ablation effects. However, the focus shift and focal beam distortion after ultrasound propagating through multi-layered heterogeneous viscoelastic biological tissues become significant and are found to deteriorate the performance of FUAS in clinics.Methods: To achieve an accurate and reliable focal field among patients with large variations in the anatomical structures and properties, a spherical cavity transducer with open ends and sub-wavelength focal size (Li et al., APL, 2013,102:204102) was applied here. Both experimental measurements and numerical simulations were performed to characterize the acoustic fields of the spherical cavity transducer in water, the multi-layered concentric cylindrical phantom, and the heterogeneous tissue model (an adult male pelvis enclosed by porcine skin, fat, and muscle) and then compared with those of a conventional concave transducer at the same electrical power output.Results: It is found that standing-wave focusing using the spherical cavity transducer results in much less focus shift (0.25λ vs. 1.67λ) along the transducer axis and focal beam distortion (−6 dB beam area of 0.71 mm2vs. 4.72 mm2 in water and 2.55 mm2vs. 17.30 mm2 in tissue) in the focal plane but higher pressure focusing gain (40.05 dB vs. 33.61 dB in tissue).Discussion: Such a highly accurate and reliable focal field is due to the excitation at an appropriate eigen-frequency of the spherical cavity with the varied media inside rather than the reverberation from the concave surface. Together with its sub-wavelength focal size, the spherical cavity transducer is technically advantageous in comparison to the concave one. The improved focusing capability would benefit ultrasound exposure for not only safer and more effective FUAS in clinics, but also broad acoustic applications.

IntroductionFlower color is an ideal trait for studying the molecular basis for phenotypic variations in natural populations of species. Epimedium (Berberidaceae) species exhibit a wide range of flower colors resulting from the varied accumulation of anthocyanins and other pigments in their spur-like petals and petaloid sepals.MethodsIn this work, the anthocyanidins of eight different Epimedium species with different floral pigmentation phenotypes were analyzed using HPLC. Twelve genes involved in anthocyanin biosynthesis were cloned and sequenced, and their expression was quantified.ResultsThe expression levels of the catalytic enzyme genes DFR and ANS were significantly decreased in four species showing loss of floral pigmentation. Complementation of EsF3’H and EsDFR in corresponding Arabidopsis mutants together with overexpression of EsF3’5’H in wild type Arabidopsis analysis revealed that these genes were functional at the protein level, based on the accumulation of anthocyanin pigments.DiscussionThese results strongly suggest that transcriptional regulatory changes determine the loss of anthocyanins to be convergent in the floral tissue of Epimedium species.

Push-the-bit rotary steering system can greatly improve the rate of penetration (ROP), hole conditions and hole extension length and represents the development direction of modern steering drilling technology. In order to accurately predict and evaluate the build-up rate of push-the-bit rotary steering system (RSS), this paper establishes the drilling trajectory prediction model and the calculation methods of ‘limit build-up rate’ and ‘corrected build-up rate’ for push-the-bit rotary steering system by comprehensively considering the interaction between thruster and sidewall and between bit and formation, the structure of RSS and other influential factors based on the mechanical model of the conventional RSS. Then, the influence mechanisms on the build-up rate are revealed from the aspects of ‘leverage effect’, ‘pendulum effect’ and ‘thrust effect’. Finally, the influence laws of thrust force, weight on bit (WOB), borehole inclination angle, bit and formation anisotropy on the build-up rate are analyzed. And the following research results are obtained. First, the build-up rate of RSS is the comprehensive result of various effects, among which ‘thrust effect’ plays a dominant role and ‘leverage effect’ and ‘pendulum effect’ play secondary roles. Second, as for the push-the-bit rotary steering system, the proportion of ‘leverage effect’ increases, the proportion of ‘thrust effect’ decreases and the build-up rate declines with the increase of WOB. Third, the build-up rate is in a close relationship with structural parameters, thrust force, bit properties and drilling parameters of RSS. In conclusion, the research results can provide an important theoretical basis for the prediction of build-up rate of push-the-bit rotary steering system and the optimization of RSS structure.

Pompe disease (PD) is a rare, autosomal recessive, inherited, and progressive metabolic disorder caused by α-glucosidase defect in lysosomes, resulting in abnormal glycogen accumulation. Patients with PD characteristically have multisystem pathological disorders, particularly hypertrophic cardiomyopathy, muscle weakness, and hepatomegaly. Although the pathogenesis and clinical outcomes of PD are well-established, disease-modeling ability, mechanism elucidation, and drug development targeting PD have been substantially limited by the unavailable PD-relevant cell models. This obstacle has been overcome with the help of induced pluripotent stem cell (iPSC) reprogramming technology, thus providing a powerful tool for cell replacement therapy, disease modeling, drug screening, and drug toxicity assessment. This review focused on the exciting achievement of PD disease modeling and mechanism exploration using iPSC.

As the most frequently diagnosed arrhythmia, atrial fibrillation (AF) has been recently reported to be closely related to ryanodine receptor (RyR2) dysfunction and calcium leak. Here, using non-integrating sendai viral method, we generated one iPSC line from peripheral blood mononuclear cells (PBMC) isolated a 10-year-old boy with simple atrial fibrillation which carries the heterozygous mutation in RYR2 gene (c.14638G > A, p.V4880I). The generated iPSC line was identified by the typical cell morphology, highly expressed pluripotent markers, normal karyotype, and in-vitro trilineage differentiation potential. It will provide a useful model for studying the pathophysiological consequences of RYR2 mutation on the AF pathogenesis.

Abstract Purpose To assess the value of the multimodel magnetic resonance imaging (MRI), including unenhanced images, dynamic contrast-enhanced MRI (DCE-MRI), MR-cholangiopancreatography (MRCP), and diffusion-weighted imaging (DWI), in differentiation of mass-forming autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). Methods Twelve patients with mass-forming AIP and 30 with PDAC were included. All patients underwent unenhanced MRI, DCE-MRI, DWI, and MRCP. Relevant values including sensitivity and specificity of the imaging features and their diagnostic performance for predicting mass-forming AIP were analyzed. Results Several statistically significant MR findings and quantitative indexes differentiating mass-forming AIP from PDAC, including multiplicity, irregularity or conformation, capsule-like rim enhancement, absence of internal cystic or necrotic portion, homogeneous enhancement during pancreatic, venous, and delayed phases, skipped stricture or stricture of MPD, absence of side branch dilation, maximum upstream MPD diameter 0.739, ContrastAP > 0.710, ContrastPP > 0.879, and ContrastVP or ContrastDP > 0.949, indicated mass-forming AIP (P

IntroductionToxoplasma gondii, a globally zoonotic protozoan parasite, infects most warm-blooded animals including the giant panda, and poses a serious threat to the giant panda conservation. However, the seroprevalence and the risk factors for toxoplasmosis in giant pandas are unknown. Here we aimed to determine the seroprevalence of T. gondii in the captive population of giant pandas and analyze the factors associated with the increased risk of infection.MethodsA total of 203 serum samples were collected from 157 (95 females and 62 males) captive giant pandas from 2007 to 2022, antibodies against T. gondii were screened using commercial ELISA and MAT kits.ResultsThe results showed 56 (35.67%) giant pandas were seropositive, age and transfer history between institutions were identifified as risk factors for T. gondii infection. It is suggested that age-related seroprevalence was the main factor, and housing multiple species in the same environment may increase the chance of cross-infection of T. gondii.DiscussionThis study can provide research data for developing policies for the prevention and control of T. gondii and protecting the health of captive giant pandas and other wildlife.

ObjectiveWe evaluated the effect of a novel modified OLIF technique (anteroinferior psoas approach, AIPA) for anterior decompression reconstruction in lumbar burst fractures, and compared the clinical, radiological outcomes and approach-related complications with the mini-open, lateral transpsoas approach (LTPA).MethodsFrom March 2016 to November 2019, 68 patients with lumbar burst fractures underwent one-stage monosegmental posterior/anterior surgery from L1–L4 segments. 35 patients included in AIPA and 33 patients in LTPA group underwent anterior decompression reconstruction. The clinical, radiological and functional evaluation outcomes were recorded during the 16–60 months follow-up period.ResultsAt the latest follow up, neurological state of one or more ASIA grades were achieved in AIPA (90.9%) and LTPA group (94.9%). No significant differences were noted between the two groups regarding preoperative and postoperative Cobbs angle. The surgery time (192.29 vs. 230.47 min, P = 0.02) in AIPA group was better compared with LTPA. The AIPA showed better improvement on Oswestry Disability Index (43.4% vs. 60.8%, P

Pompe disease results from GAA mutations that leads to lysosomal glycogen accumulation and cardiac and skeletal muscle pathology. We have previously generated an infantile-onset Pompe disease patient-derived human-induced pluripotent stem cells (iPSCs) line carrying compound GAA mutations (R608X and E888X). Using his parents’ peripheral blood mononuclear cells (PBMCs), we here generated two iPSCs lines which carry mutations of R608X E888X respectively. Both lines show typical cell morphology, high expressed pluripotent and self-renewal markers, normal karyotype, and trilineage differentiation potential. These two lines are valuable re-sources for studying the pathological mechanisms of GAA mutation-caused Pompe disease.

Accurate measurements of the bubble size distribution (BSD) are crucial for investigating gas–liquid mass transfer mechanisms and describing the characteristics of chemical production. However, measuring the BSD in high-density bubbly flows remains challenging due to limited image algorithms and high data densities. Therefore, an end-to-end BSD detection method in dense bubbly flows based on deep learning is proposed in this paper. The bubble detector locates the positions of dense bubbles utilizing objection detection networks and simultaneously performs ellipse parameter fitting to measure the size of the bubbles. Different You Only Look Once (YOLO) architectures are compared, and YOLOv7 is selected as the backbone network. The complete intersection over union calculation method is modified by the circumferential horizontal rectangle of bubbles, and the loss function is optimized by adding L2 constraints of ellipse size parameters. The experimental results show that the proposed technique surpasses existing methods in terms of precision, recall, and mean square error, achieving values of 0.9871, 0.8725, and 3.8299, respectively. The proposed technique demonstrates high efficiency and accuracy when measuring BSDs in high-density bubbly flows and has the potential for practical applications.

Three-dimensional (3D) scene reconstruction plays an important role in digital cities, virtual reality, and simultaneous localization and mapping (SLAM). In contrast to perspective images, a single panoramic image can contain the complete scene information because of the wide field of view. The extraction and matching of image feature points is a critical and difficult part of 3D scene reconstruction using panoramic images. We attempted to solve this problem using convolutional neural networks (CNNs). Compared with traditional feature extraction and matching algorithms, the SuperPoint (SP) and SuperGlue (SG) algorithms have advantages for handling images with distortions. However, the rich content of panoramic images leads to a significant disadvantage of these algorithms with regard to time loss. To address this problem, we introduce the Improved Cube Projection Model: First, the panoramic image is projected into split-frame perspective images with significant overlap in six directions. Second, the SP and SG algorithms are used to process the six split-frame images in parallel for feature extraction and matching. Finally, matching points are mapped back to the panoramic image through coordinate inverse mapping. Experimental results in multiple environments indicated that the algorithm can not only guarantee the number of feature points extracted and the accuracy of feature point extraction but can also significantly reduce the computation time compared to other commonly used algorithms.

Piezoresistive pressure sensors exhibit inherent nonlinearity and sensitivity to ambient temperature, requiring multidimensional compensation to achieve accurate measurements. However, recent studies on software compensation mainly focused on developing advanced and intricate algorithms while neglecting the importance of calibration data and the limitation of computing resources. This paper aims to present a novel compensation method which generates more data by learning the calibration process of pressure sensors and uses a larger dataset instead of more complex models to improve the compensation effect. This method is performed by the proposed aquila optimizer optimized mixed polynomial kernel extreme learning machine (AO-MPKELM) algorithm. We conducted a detailed calibration experiment to assess the quality of the generated data and evaluate the performance of the proposed method through ablation analysis. The results demonstrate a high level of consistency between the generated and real data, with a maximum voltage deviation of only 0.71 millivolts. When using a bilinear interpolation algorithm for compensation, extra generated data can help reduce measurement errors by 78.95%, ultimately achieving 0.03% full-scale (FS) accuracy. These findings prove the proposed method is valid for high-accuracy measurements and has superior engineering applicability.

Abstract Study design This was a cross-sectional frequency-matched case–control study. Background and aim The serum lipid profile of lipoprotein(a) [Lp(a)] level and apolipoprotein B/apolipoprotein A1 ratio (Apo B/Apo A1) ratio were found to be more representative for serum lipid level and were recognized as the independent risk factors for various diseases. Although the blood levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were found to be associated with symptomatic intervertebral disk herniation (IDH), no studies to date have evaluated the association of Apo AI, Apo B, Lp(a), and Apo B/Apo AI levels with symptomatic IDH. This study aimed to assess the link between blood lipid levels and symptomatic IDH. Method The study included 1839 Chinese patients. Of these, 918 patients were diagnosed with IDH and enrolled in the experimental group. A control group of 921 patients underwent a physical examination during the same period. The serum lipid levels of TC, TG, LDL-C, HDL-C, Lp(a), Apo B, and Apo B/Apo AI were examined and analyzed. The control group comprised randomly selected patients who met the baseline levels of the aforementioned lipid molecules. Results Patients with IDH exhibited significantly higher TC, TG, LDL, Apo B, and Lp(a) levels than controls. The percentage of high TC, high TG, high LDL, high Apo B, and high Lp(a) were obviously higher in the IDH group than in the control group. However, hyperlipidemia had no relationship with the degenerated segment of the IDH (P = 0.201). The odds ratio (OR) for the incidence of IDH with elevated levels of LDL-C, TC, TG, Lp(a), Apo B, and Apo B/Apo AI was 1.583, 1.74, 1.62, 1.58, 1.49, and 1.39, respectively. The correlation analysis revealed the correlation between elevated LDL-C, TC, TG, Apo B, Lp(a), and incidence of IDH was significant (R 2 LDL = 0.017; R 2 TC = 0.004; R 2 TG = 0.015; R 2 Apo B = 0.004; R 2 Lp(a) = 0.021) (P

Until today, the coronavirus disease 2019 (COVID-19) pandemic has caused 6,043,094 deaths worldwide, and most of the mortality cases have been related to patients with long-term diseases, especially cancer. Autophagy is a cellular process for material degradation. Recently, studies demonstrated the association of autophagy with cancer development and immune disorder, suggesting autophagy as a possible target for cancer and immune therapy. Laminarin is a polysaccharide commonly found in brown algae and has been reported to have pharmaceutic roles in treating human diseases, including cancers. In the present report, we applied network pharmacology with systematic bioinformatic analysis, including gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, reactome pathway analysis, and molecular docking to determine the pharmaceutic targets of laminarin against COVID-19 and cervical cancer via the autophagic process. Our results showed that the laminarin would target ten genes: CASP8, CFTR, DNMT1, HPSE, KCNH2, PIK3CA, PIK3R1, SERPINE1, TLR4, and VEGFA. The enrichment analysis suggested their involvement in cell death, immune responses, apoptosis, and viral infection. In addition, molecular docking further demonstrated the direct binding of laminarin to its target proteins, VEGFA, TLR4, CASP8, and PIK3R1. The present findings provide evidence that laminarin could be used as a combined therapy for treating patients with COVID-19 and cervical cancer.

Obesity is a growing global health problem; it has been forecasted that over half of the global population will be obese by 2030. Obesity is complicated with many diseases, such as diabetes and cardiovascular diseases, leading to an economic impact on society. Other than diet, exposure to environmental pollutants is considered a risk factor for obesity. Exposure to perfluorooctanoic acid (PFOA) was found to impair hepatic lipid metabolism, resulting in obesity. In this study, we applied network pharmacology and systematic bioinformatics analysis, such as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, together with molecular docking, to investigate the targets of fucoidan for treating PFOA-associated obesity through the regulation of endoplasmic reticulum stress (ERS). Our results identified ten targets of fucoidan, such as glucosylceramidase beta (GBA), glutathione-disulfide reductase (GSR), melanocortin 4 receptor (MC4R), matrix metallopeptidase (MMP)2, MMP9, nuclear factor kappa B subunit 1 (NFKB1), RELA Proto-Oncogene, NF-KB Subunit (RELA), nuclear receptor subfamily 1 group I member 2 (NR1I2), proliferation-activated receptor delta (PPARD), and cellular retinoic acid binding protein 2 (CRABP2). GO and KEGG enrichment analyses highlighted their involvement in the pathogenesis of obesity, such as lipid and fat metabolisms. More importantly, the gene cluster is responsible for obesity-associated diseases and disorders, such as insulin resistance (IR), non-alcoholic fatty liver disease, and diabetic cardiomyopathy, via the control of signaling pathways. The findings of this report provide evidence that fucoidan is a potential nutraceutical product against PFOA-associated obesity through the regulation of ERS.

Point symbols on a scanned topographic map (STM) provide crucial geographic information. However, point symbol recognition entails high complexity and uncertainty owing to the stickiness of map elements and singularity of symbol structures. Therefore, extracting point symbols from STMs is challenging. Currently, point symbol recognition is performed primarily through pattern recognition methods that have low accuracy and efficiency. To address this problem, we investigated the potential of a deep learning-based method for point symbol recognition and proposed a deep convolutional neural network (DCNN)-based model for this task. We created point symbol datasets from different sources for training and prediction models. Within this framework, atrous spatial pyramid pooling (ASPP) was adopted to handle the recognition difficulty owing to the differences between point symbols and natural objects. To increase the positioning accuracy, the k-means++ clustering method was used to generate anchor boxes that were more suitable for our point symbol datasets. Additionally, to improve the generalization ability of the model, we designed two data augmentation methods to adapt to symbol recognition. Experiments demonstrated that the deep learning method considerably improved the recognition accuracy and efficiency compared with classical algorithms. The introduction of ASPP in the object detection algorithm resulted in higher mean average precision and intersection over union values, indicating a higher recognition accuracy. It is also demonstrated that data augmentation methods can alleviate the cross-domain problem and improve the rotation robustness. This study contributes to the development of algorithms and the evaluation of geographic elements extracted from STMs.

Aiming at the risk assessment requirements of typical industrial control systems with integrated architecture of security and safety, we propose an objective and quantitative integrated security and safety assessment scheme based on Fuzzy Analytic Hierarchy Process (FAHP). First, we establish a safety and security integrated (SSI) architecture for typical industrial control systems with security measures integrated into safety failure modes. On this basis, we establish a hierarchical model of risk assessment with SSI failure mode as an element of the evaluation layer, and then standardize characteristic values of various safety-related heterogeneous index parameters. We design an entropy weight method that uses Grey Relation Analysis (GRA) method to modify the correlation of multiple indicators as a parameter strategy for determining the relative importance of element layer and evaluation layer and then use the membership function method of fuzzy statistical method to obtain the membership degree of hierarchical elements, and finally obtain the failure risk level value of equipment and system by fuzzy comprehensive evaluation. Based on a typical distributed control system, we build an experimental platform to test and verify the risk assessment plan, and compared with expert experience parameter method. The result shows that the scheme takes into account the correlation between indicators which measure the SSI risk level of industrial control system, and the entropy weight method is used to evaluate the risk of industrial control system which can overcome the subjectivity and uncertainty of individual judgment. Furthermore, the quantitative evaluation of system risk is completed by using fuzzy statistical method in the case of industrial control system without prior knowledge, and the idea of this scheme has a wide range of engineering value.

BackgroundUlcerative colitis (UC) is an inflammatory bowel disease that causes inflammation and ulcers in the digestive tract. Approximately 3 million US adults suffer from this disease. Mesalazine, an anti-inflammatory agent, is commonly used for the treatment of UC. However, some studies have demonstrated side effects of mesalazine, such as acute pancreatitis and hypereosinophilia. Therefore, a better understanding of the anti-inflammatory mechanism of mesalazine in UC could help improve the effectiveness of the drug and reduce its side effects. In this study, we used a dextran sodium sulfate-induced UC mouse model, and applied network pharmacology and omics bioinformatics approaches to uncover the potential pharmaceutical targets and the anti-inflammatory mechanism of mesalazine.ResultsNetwork pharmacology analysis identified the core targets of mesalazine, biological processes, and cell signaling related to immunity and inflammatory responses mediated by mesalazine. Molecular docking analysis then indicated possible binding motifs on the core targets (including TNF-α, PTGS2, IL-1β, and EGFR). Metabolomics and 16S metagenomic analyses highlighted the correlation between gut microbiota and metabolite changes caused by mesalazine in the UC model.ConclusionsCollectively, the omics and bioinformatics approaches and the experimental data unveiled the detailed molecular mechanisms of mesalazine in UC treatment, functional regulation of the gut immune system, and reduction of intestinal inflammation. More importantly, the identified core targets could be targeted for the treatment of UC.

Although the potential of lake facies shale oil and gas resources is enormous, the lithology is complex, heterogeneous, and obviously controlled by a sedimentary environment. By integrating cores, thin sections, X-ray diffraction analysis, trace elements, organic geochemical indexes, and paleontological data, this work studies sedimentary environment elements and sedimentary model of shale in the E1f2 member, Gaoyou Depression, Subei Basin. The shale in the E1f2 member is completely shore lacustrine deposits forming into the hot dry climate of tropical central and south Asia. The lake basin extended continuously, with brackish water, strong reduction, and medium water stratification. The transgression resulted in the rapid rise of water, salinity, reducibility, and water stratification. As the climate becomes wetter, the E1f2 water level, salinity, reduction, and stratification decrease. The shale in E1f2 members is characterized by sedimentary zones, which are divided into muddy water desalination deposit in the flooded zone, clear water salinization deposit in the transition zone, and still water salinization deposited in the clear water zone. Under palaeotopography settings of E1f2 members, combining characteristics of vertical stratification and the plane zone of the lake, an integrated sedimentary model of lacustrine shale in a flat platform has been established under climate and transgression control. The plane area of the lake could be divided into the slow slope zone, sub-depression zone, processes zone, deep slope zone, and deep depression zone, corresponding roughly to the sedimentary zones of the flooded zone, transition zone, and clear water zone. Moreover, the oil shale enriched in low-lying parts of the paleotopography is mainly deposited at the end of muddy water areas and clear water areas.

Abstract We construct a two-current model. It includes two gauge fields, which introduce the doping effect, and a neutral scalar field. And then we numerically construct an AdS black brane geometry with scalar hair. Over this background, we study the fermionic system with the pseudoscalar Yukawa coupling. Some universal properties from the pseudoscalar Yukawa coupling are revealed. In particular, as the coupling increases, there is a transfer of the spectral weight from the low energy band to the high energy band. The transfer is over low energy scales but not over all energy scales. The peculiar properties are also explored. The study shows that with the increase of the doping, the gap opens more difficult. It indicates that there is a competition between the pseudoscalar Yukawa coupling and the doping.

This study investigated the relationship between preoperative cerebral blood flow (CBF) in patients with idiopathic normal pressure hydrocephalus (INPH) and preoperative clinical symptoms and changes of clinical symptoms after shunt surgery. A total of 32 patients with diagnosed INPH and 18 age-matched healthy controls (HCs) were involved in this study. All subjects underwent magnetic resonance imaging (MRI), including 3D pulsed arterial-spin labeling (PASL) for non-invasive perfusion imaging, and clinical symptom evaluation at baseline, and all patients with INPH were reexamined with clinical tests 1 month postoperatively. Patients with INPH had significantly lower whole-brain CBF than HCs, with the most significant differences in the high convexity, temporal lobe, precuneus, and thalamus. At baseline, there was a significant correlation between the CBF in the middle frontal gyrus, calcarine, inferior and middle temporal gyrus, thalamus, and posterior cingulate gyrus and poor gait manifestation. After shunting, improvements were negatively correlated with preoperative perfusion in the inferior parietal gyrus, inferior occipital gyrus, and middle temporal gyrus. Preoperative CBF in the middle frontal gyrus was positively correlated with the severity of preoperative cognitive impairment and negatively correlated with the change of postoperative MMSE score. There was a moderate positive correlation between anterior cingulate hypoperfusion and improved postoperative urination. Our study revealed that widely distributed and intercorrelated cortical and subcortical pathways are involved in the development of INPH symptoms, and preoperative CBF may be correlative to short-term shunt outcomes.

Background and PurposeBreast ductal carcinoma in situ (DCIS) has no metastatic potential, and has better clinical outcomes compared with invasive breast cancer (IBC). Convolutional neural networks (CNNs) can adaptively extract features and may achieve higher efficiency in apparent diffusion coefficient (ADC)-based tumor invasion assessment. This study aimed to determine the feasibility of constructing an ADC-based CNN model to discriminate DCIS from IBC.MethodsThe study retrospectively enrolled 700 patients with primary breast cancer between March 2006 and June 2019 from our hospital, and randomly selected 560 patients as the training and validation sets (ratio of 3 to 1), and 140 patients as the internal test set. An independent external test set of 102 patients during July 2019 and May 2021 from a different scanner of our hospital was selected as the primary cohort using the same criteria. In each set, the status of tumor invasion was confirmed by pathologic examination. The CNN model was constructed to discriminate DCIS from IBC using the training and validation sets. The CNN model was evaluated using the internal and external tests, and compared with the discriminating performance using the mean ADC. The area under the curve (AUC), sensitivity, specificity, and accuracy were calculated to evaluate the performance of the previous model.ResultsThe AUCs of the ADC-based CNN model using the internal and external test sets were larger than those of the mean ADC (AUC: 0.977 vs. 0.866, P = 0.001; and 0.926 vs. 0.845, P = 0.096, respectively). Regarding the internal test set and external test set, the ADC-based CNN model yielded sensitivities of 0.893 and 0.873, specificities of 0.929 and 0.894, and accuracies of 0.907 and 0.902, respectively. Regarding the two test sets, the mean ADC showed sensitivities of 0.845 and 0.818, specificities of 0.821 and 0.829, and accuracies of 0.836 and 0.824, respectively. Using the ADC-based CNN model, the prediction only takes approximately one second for a single lesion.ConclusionThe ADC-based CNN model can improve the differentiation of IBC from DCIS with higher accuracy and less time.

The optoelectronic effects of sidewall passivation on micro-light-emitting diodes (Micro-LEDs) were investigated using sol-gel chemical synthesis. Blue InGaN/GaN multi-quantum well (MQW) Micro-LEDs, ranging in size from 20 × 20 μm to 100 × 100 μm and with high EQE, were fabricated and distinguished by the passivation method used, including no passivation, sol-gel SiO2, and plasma-enhanced chemical vapor deposition (PECVD) SiO2. Impressively, the sol-gel method is advantageous in improving the optoelectronic performance of Micro-LEDs. The fabricated 20 × 20 μm Micro-LEDs showed an EQE of 27.7% with sol-gel passivation, which was a 14% improvement compared to devices without sidewall passivation. Sol-gel sidewall passivation allows Micro-LEDs to effectively achieve sharper edge emission, superior surface luminous uniformity, and intensity, providing the possibility for the fabrication of low-cost and high-efficiency Micro-LEDs.

The combination of advanced radar sensor technology and smart grid has broad prospects. It is meaningful to monitor the respiration and heartbeat of grid employees under resting state through radar sensors to ensure that they are in a healthy working state. Ultra-wideband (UWB) radar sensor is suitable for this application because of its strong penetration ability, high range resolution and low average power consumption. However, due to weak heartbeat amplitude and measurement noise, the accurate measurement of the target heart rate is a challenge. In this paper, singular spectrum analysis (SSA) is proposed to reconstruct the eigenvalues of noisy vital signs to eliminate noise peaks around the heartbeat rate; combined with the variational modal decomposition (VMD), the target vital signs can be extracted with high accuracy. The experiment confirmed that the target vital sign information can be extracted with high accuracy from ten subjects at different distances, which can play an important role in short distance human detection and vital sign monitoring.