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Abstract Background Peucedanum praeruptorum Dunn has typical stacked umbels and medicinal value; however, the lack of an effective tissue culture system for P. praeruptorum has limited the large-scale propagation of its seedlings. Results We systematically established an in vitro regeneration system for P. praeruptorum using young leaves and stems as explants. Tissue culture plantlets were successfully obtained within 123 and 90 d of somatic embryogenesis and organogenesis, respectively. Combined plant growth regulators (PGRs) were optimized to promote efficient plant regeneration at each stage of the culture process. Specifically, embryogenic callus induction was superior in Murashige and Skoog (MS) medium supplemented with 0.5 mg/L 6-benzyladenine (BA) and 2.0 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D). For somatic embryonic development, the highest differentiation rates were achieved using BA, 2,4-D, and 6-furfuryl aminopurine (6-KT). Induction of organogenesis resulted in the highest differentiation rates and proliferation coefficients of buds in MS medium supplemented with BA and α-naphthaleneacetic acid (NAA). Moreover, regeneration of P. praeruptorum seedlings was achieved by adjusting the BA and indole-3-butyric acid (IBA) concentrations in 1/2 MS medium. Conclusion Our results provide a technical system for the rapid propagation of P. praeruptorum, which can facilitate germplasm improvement, resource conservation, and further genetic transformation of Peucedanum species.

Abstract Transformer-based methods effectively capture global dependencies in images, demonstrating outstanding performance in multiple visual tasks. However, existing Transformers cannot effectively denoise large noisy images captured under low-light conditions owing to (1) the global self-attention mechanism causing high computational complexity in the spatial dimension owing to a quadratic increase in computation with the number of tokens; (2) the channel-wise self-attention computation unable to optimise the spatial correlations in images. We propose a local–global interaction Transformer (LGIT) that employs an adaptive strategy to select relevant patches for global interaction, achieving low computational complexity in global self-attention computation. A top-N patch cross-attention model (TPCA) is designed based on superpixel segmentation guidance. TPCA selects top-N patches most similar to the target image patch and applies cross attention to aggregate information from them into the target patch, effectively enhancing the utilisation of the image’s nonlocal self-similarity. A mixed-scale dual-gated feedforward network (MDGFF) is introduced for the effective extraction of multiscale local correlations. TPCA and MDGFF were combined to construct a hierarchical encoder-decoder network, LGIT, to compute self-attention within and across patches at different scales. Extensive experiments using real-world image-denoising datasets demonstrated that LGIT outperformed state-of-the-art (SOTA) convolutional neural network (CNN) and Transformer-based methods in qualitative and quantitative results.

Background: Immunotherapy has emerged as a novel strategy for cancer treatment following surgery, radiotherapy, and chemotherapy. Immune checkpoint blockade and Chimeric antigen receptor (CAR)-T cell therapies have been successful in clinical trials. Cancer cells evade immune surveillance by hijacking inhibitory pathways via overexpression of checkpoint genes. The Cluster of Differentiation 47 (CD47) has emerged as a crucial checkpoint for cancer immunotherapy by working as a “don’t eat me” signal and suppressing innate immune signaling. Furthermore, CD47 is highly expressed in many cancer types to protect cancer cells from phagocytosis via binding to SIRPα on phagocytes. Targeting CD47 by either interrupting the CD47-SIRPα axis or combing with other therapies has been demonstrated as an encouraging therapeutic strategy in cancer immunotherapy. Antibodies and small molecules that target CD47 have been explored in pre- and clinical trials. However, formidable challenges such as the anemia and palate aggregation cannot be avoided because of the wide presentation of CD47 on erythrocytes. Aim of view: This review summarizes the current knowledge on the regulation and function of CD47, and provides a new perspective for immunotherapy targeting CD47. It also highlights the clinical progress of targeting CD47 and discusses challenges and potential strategies. Key scientific concepts of review: This review provides a comprehensive understanding of targeting CD47 in cancer immunotherapy, it also augments the concept of combination immunotherapy strategies by employing both innate and adaptive immune responses.

Abstract Doctors typically use non‐contrast‐enhanced computed tomography (NCECT) in the treatment of kidney cancer to map kidney and tumour structural information to functional imaging single‐photon emission computed tomography, which is then used to assess patient kidney function and predict postoperative recovery. However, the assessment of kidney function and formulation of surgical plans is constrained by the low contrast of tumours in NCECT, which hinders the acquisition of accurate tumour boundaries. Therefore, this study designed a radiomics feature‐enhanced level‐set evolution (RFLSE) to precisely segment small‐sample low‐contrast kidney tumours. Integration of high‐dimensional radiomics features into the level‐set energy function enhances the edge detection capability of low‐contrast kidney tumours. The use of sensitive radiomics features to control the regional term parameters achieves adaptive adjustment of the curve evolution amplitude, improving the level‐set segmentation process. The experimental data used low‐contrast, limited‐sample tumours provided by hospitals, as well as the public datasets BUSI18 and KiTS19. Comparative results with advanced energy functionals and deep learning models demonstrate the precision and robustness of RFLSE segmentation. Additionally, the application value of RFLSE in assisting doctors with accurately marking tumours and generating high‐quality pseudo‐labels for deep learning datasets is demonstrated.

High-order harmonic techniques can be used to recreate neutron flux distributions in reactor cores using the neutron diffusion equation. However, traditional source iteration and source correction iteration techniques have sluggish convergence rates and protracted calculation periods. The correctness of the implicitly restarted Arnoldi method (IRAM) in resolving the eigenvalue problems of the one-dimensional and two-dimensional neutron diffusion equations was confirmed by computing the benchmark problems SLAB_1D_1G and two-dimensional steady-state TWIGL using IRAM. By integrating Galerkin projection with Proper Orthogonal Decomposition (POD) techniques, a POD-Galerkin reduced-order model was developed and the IRAM model was used as the full-order model. For 14 macroscopic cross-section values, the TWIGL benchmark problem was perturbed within a 20% range. We extracted 100 sample points using the Latin hypercube sampling method, and 70% of the samples were used as the testing set to assess the performance of the reduced-order model The remaining 30% were utilized as the training set to develop the reduced-order model, which was employed to rebuild the TWIGL benchmark problem. The reduced-order model demonstrates good flexibility and can efficiently and accurately forecast the effective multiplication factor and neutron flux distribution in the core. The reduced-order model predicts keff and neutron flux distribution with a high degree of agreement compared to the full-order model. Additionally, the reduced-order model's computation time is only 10.18% of that required by the full-order model.The neutron flux distribution of the steady-state TWIGL benchmark was recreated using the reduced-order model. The obtained results indicate that the reduced-order model can accurately predict the keff and neutron flux distribution of the steady-state TWIGL benchmark.Overall, the proposed technique not only has the potential to accurately project neutron flux distributions in transient settings, but is also relevant for reconstructing neutron flux distributions in steady-state conditions; thus, its applicability is bound to increase in the future.

Traditional text-based system engineering, which has been used in the design and application of passive residual heat removal system (PRHRS) for lead-cooled fast reactors, is prone to several problems such as low development efficiency, long iteration cycles, and model ambiguity. This study aims to effectively address the above-mentioned problems by adopting a model-based system engineering (MBSE) method, which has been preliminarily applied to meet the design requirements of a PRHRS. The design process has been implemented based on the preliminary design of the system architecture and consists of three stages: top-level requirement analysis, functional requirements analysis, and design requirements synthesis. The results of the top-level requirements analysis and the corresponding use case diagram can determine the requirements, top-level use cases, and scenario flow of the system. During the functional requirements analysis, the sequence, activity, and state machine diagrams are used to develop the system function model and provide early confirmation. By comparing these sequence diagrams, the requirements for omissions and inconsistencies can be effectively checked. In the design requirements synthesis stage, the Analytic Hierarchy Process is used to conduct preliminary trade-off calculations on the system architecture, after which a white box model is established during the system architecture design. Through these two steps, the analysis and design of the system architecture are ultimately achieved. The resulting system architecture ensures the consistency of the design requirements. Ultimately, a functional hazard analysis was conducted for a specific incident to validate case requirements and further refine the system architecture. Future research can further reduce the design risk, improve the design efficiency, and provide a practical reference for the design and optimization of PRHRS in digital lead-cooled fast reactors.

Accurately predicting the thermal hydraulic parameters of a transient reactor core under different working conditions is the first step toward reactor safety. Mass flow rate and temperature are important parameters of core thermal hydraulics, which have often been modeled as time series prediction problems. This study aims to achieve accurate and continuous prediction of core thermal hydraulic parameters under instantaneous conditions, as well as test the feasibility of a newly constructed gated recurrent unit (GRU) model based on the soft attention mechanism for core parameter predictions. Herein, the China Experimental Fast Reactor (CEFR) is used as the research object, and CEFR 1/2 core was taken as subject to carry out continuous predictive analysis of thermal parameters under transient conditions., while the subchannel analysis code named SUBCHANFLOW is used to generate the time series of core thermal-hydraulic parameters. The GRU model is used to predict the mass flow and temperature time series of the core. The results show that compared to the adaptive radial basis function neural network, the GRU network model produces better prediction results. The average relative error for temperature is less than 0.5 % when the step size is 3, and the prediction effect is better within 15 s. The average relative error of mass flow rate is less than 5 % when the step size is 10, and the prediction effect is better in the subsequent 12 s. The GRU model not only shows a higher prediction accuracy, but also captures the trends of the dynamic time series, which is useful for maintaining reactor safety and preventing nuclear power plant accidents. Furthermore, it can provide long-term continuous predictions under transient reactor conditions, which is useful for engineering applications and improving reactor safety.

Photoacoustic spectroscopy (PAS) gas sensors based on optomechanical resonators (OMRs) have garnered significant attention for ultrasensitive trace-gas detection. However, a major challenge lies in balancing small size with high performance when developing ultrasensitive miniaturized optomechanical resonant PAS (OMR-PAS) gas sensors for space-constrained applications. Here, we present a miniature optical fiber PAS gas sensor based on a planar-spiral spring OMR (PSS-OMR) that is in situ 3D micro-printed on the end-face of a fiber-optic ferrule. Experimental results demonstrate that mechanical vibrational resonance can enhance the sensor's acoustic sensitivity by over two orders of magnitude. Together with a 1.4 μL non-resonant photoacoustic cell, it can detect C2H2 gas concentration at the 45-ppb level, and its response is very fast approximating 0.2 seconds. This optical fiber OMR-PAS gas sensor holds great promise for the detection or monitoring of rapidly varying trace gas in many applications ranging from production process control to industrial environmental surveillance.

Semantic segmentation of airborne point clouds is crucial for 3D scene reconstruction and remote sensing in surveying applications. Current deep learning methods for point clouds primarily focus on effectively aggregating local neighborhood information. However, they often overlook the fusion of global context information and elevation features, which are vital for airborne point clouds. In this study, we propose Dense-LGEANet, a novel network with dense connected architecture and multiscale feature supervision based on our designed LGEA module. The key component of our LGEA module is the combination of the graph convolution block and the transformer block with elevation attention. It can effectively fuse local neighborhood information and global context information to improve the accuracy of semantic segmentation of airborne point cloud. Moreover, the designed dense connected network architecture can enhance the feature extraction capability for point cloud objects at different scales by facilitating interactions between multiple up-sampling and down-sampling layers. We have conducted multiple experiments on the public point cloud dataset. Experimental results show that our method can achieve an mIoU of 58.5% and an mF1 of 72.0% on the ISPRS Vaihingen 3D dataset, while an mIoU of 67.2% and an mF1 of 78.3% on the LASDU dataset. It demonstrates the superior performance of our network and the effectiveness of the proposed feature enhancement module and network architecture.

The interaction between cluster of differentiation 47 (CD47) and signal regulatory protein α (SIRPα) protects healthy cells from macrophage attack, which is crucial for maintaining immune homeostasis. Overexpression of CD47 occurs widely across various tumor cell types and transmits the “don't eat me” signal to macrophages to avoid phagocytosis through binding to SIRPα. Blockade of the CD47-SIRPα axis is therefore a promising approach for cancer treatment. Lymphoma is the most common hematological malignancy and is an area of unmet clinical need. This review mainly described the current strategies targeting the CD47-SIRPα axis, including antibodies, SIRPα Fc fusion proteins, small molecule inhibitors, and peptides both in preclinical studies and clinical trials with Hodgkin lymphoma and non-Hodgkin lymphoma.

The network topology discovery in OpenFlow-based software-defined networks is mainly achieved by utilizing the OpenFlow discovery protocol (OFDP).However, it has been observed in existing research that OFDP exhibits low updating efficiency and is susceptible to network topology pollution attacks.To address the efficiency and safety concerns of the network topology discovery protocol, an in-depth investigation was conducted on the mechanism and safety of OFDP network topology discovery.The characteristics of network topology establishment and updating in OFDP were analyzed, and an improved protocol named Im-OFDP (improved OpenFlow discovery protocol) based on the minimum vertex covering problem in graph theory was proposed.In Im-OFDP, the switch port table and network link table were initially established using prior information obtained from OFDP network topology discovery.Subsequently, a graph model of the network topology was constructed, and the minimum vertex covering algorithm in graph theory was employed to select specific switches for the reception and forwarding of topology discovery link layer discovery protocol (LLDP) packets.Multi-level flow tables were designed based on the network topology structure, and these flow entries were installed on the selected switches by the controller to process LLDP packets.To address security issues, dynamic check code verification in LLDP packets was employed to ensure the safety of network links.Additionally, a network equipment information maintenance mechanism was established based on known network topologies to ensure the safety of the network equipment.Experimental results demonstrate a significant reduction in the number of network topology discovery messages, bandwidth overhead, and CPU overhead through the deployment of Im-OFDP.Moreover, the response time for node failures and link recovery time after mode failure is substantially reduced.Im-OFDP also effectively mitigates various network topology pollution attacks, such as link fabrication and switch forgery attacks.Overall, Im-OFDP has the capability to enhance the efficiency and safety of SDN topology discovery.

With the continuous advancement of autonomous driving technology, an increasing number of high-definition (HD) maps have been generated and stored in geospatial databases. These HD maps can provide strong localization support for mobile robots equipped with light detection and ranging (LiDAR) sensors. However, the global localization of heterogeneous robots under complex environments remains challenging. Most of the existing point cloud global localization methods perform poorly due to the different perspective views of heterogeneous robots. Leveraging existing HD maps, this paper proposes a base-map-guided heterogeneous robots localization solution. A novel co-view context descriptor with rotational invariance is developed to represent the characteristics of heterogeneous point clouds in a unified manner. The pre-set base map is divided into virtual scans, each of which generates a candidate co-view context descriptor. These descriptors are assigned to robots before operations. By matching the query co-view context descriptors of a working robot with the assigned candidate descriptors, the coarse localization is achieved. Finally, the refined localization is done through point cloud registration. The proposed solution can be applied to both single-robot and multi-robot global localization scenarios, especially when communication is impaired. The heterogeneous datasets used for the experiments cover both indoor and outdoor scenarios, utilizing various scanning modes. The average rotation and translation errors are within 1° and 0.30 m, indicating the proposed solution can provide reliable localization support despite communication failures, even across heterogeneous robots.

Allicin, as a natural antibacterial active substance from plants, has great medical and health care value. However, due to its poor stability, its application in the field of food and medicine is limited. So, in this paper, allicin–zein–sodium caseinate composite nanoparticles (zein–Ali–SC) were prepared by antisolvent precipitation and electrostatic deposition. Through the analysis of the particle size, ζ-potential, encapsulation efficiency (EE), loading rate (LC) and microstructure, the optimum preparation conditions for composite nanoparticles were obtained. The mechanism of its formation was studied by fluorescence spectrum, Fourier infrared spectrum (FTIR), X-ray diffraction (XRD) and thermogravimetric analysis (TGA). The stability study results showed that the particle size of composite nanoparticles was less than 200 nm and its PDI was less than 0.3 under different NaCl concentrations and heating conditions, showing good stability. When stored at 4 °C for 21 days, the retention rate of allicin reached 61.67%, which was 52.9% higher than that of free allicin. After freeze-drying and reheating, the nanoparticles showed good redispersibility; meanwhile, antioxidant experiments showed that, compared with free allicin, the nanoparticles had stronger scavenging ability of free radicals, which provided a new idea for improving the stability technology and bioavailability of bioactive compounds.

Background: Premature ventricular complex (PVC) induced cardiomyopathy (PVC-CMP) and exacerbated left ventricular systolic dysfunction (LVSD) are common in clinical scenarios. However, their precise risk factors are currently unclear. Methods: We performed a systematic review of PubMed, EMBASE, Web of Science, and Chinese-based literature database (CBM) to identify observational studies describing the factors associated with PVC-CMP and post-ablation LVSD reversibility. A total of 25 and 12 studies, involving 4863 and 884 subjects, respectively, were eligible. We calculated pooled multifactorial odds ratios (OR) and 95% confidence intervals (CI) for each parameter using random-effects and fixed-effects models. Results: The results showed that 3 independent risk factors were associated with PVC-CMP: being asymptomatic (OR and 95% CI: 3.04 [2.13, 4.34]), interpolation (OR and 95% CI: 2.47 [1.25, 4.92]), and epicardial origin (epi-origin) (OR and 95% CI: 3.04 [2.13, 4.34]). Additionally, 2 factors were significantly correlated with post-ablation LVSD reversibility: sinus QRS wave duration (QRSd) (OR and 95% CI: 0.95 [0.93, 0.97]) and PVC burden (OR and 95% CI: 1.09 [0.97, 1.23]). Conclusions: the relatively consistent independent risk factors for PVC-CMP and post-ablation LVSD reversibility are asymptomatic status, interpolation, epicardial origin, PVC burden, and sinus QRS duration, respectively.

Inter-Turn Short Circuit (ITSC) faults do not necessarily produce high temperatures but exhibit distinct heat distribution and characteristics. This paper proposes a novel fault diagnosis and identification scheme utilizing an improved You Look Only Once Vision 8 (YOLOv8) algorithm, enhanced with an infrared image slicing-aided hyper-inference (SAHI) technique, to automatically detect ITSC fault trajectories in dry-type transformers. The infrared image acquisition system gathers data on ITSC fault trajectories and captures images with varying contrast to enhance the robustness of the recognition model. Given that the fault trajectory constitutes a small portion of the overall infrared image and is subject to significant background interference, traditional recognition algorithms often misjudge or omit faults. To address this, a YOLOv8-based visual detection method incorporating Dynamic Snake Convolution (DSConv) and the Slicing-Aided Hyper-Inference algorithm is proposed. This method aims to improve recognition precision and accuracy for small targets in complex backgrounds, facilitating accurate detection of ITSC faults in dry-type transformers. Comparative tests with the YOLOv8 model, Fast Region-based Convolutional Neural Networks (Fast-RCNNs), and Residual Neural Networks (Retina-Nets) demonstrate that the enhancements significantly improve model convergence speed and fault trajectory detection accuracy. The approach offers valuable insights for advancing infrared image diagnostic technology in electrical power equipment.

Double-layer heat tubes have been designed to effectively reduce the occurrence of heat pipe rupture accidents. However, inter-tube thermal contact resistance can decrease heat transfer efficiency, thus hampering the heat dissipation in the primary loop system of lead-bismuth reactors. Therefore, optimizing the design of double-layer heat tubes is necessary. This work focuses on the double-layer heat exchanger of a lead-bismuth reactor and utilizes gallium-based graphene nanofluids as a thermal interface material to fill the gap between the heat tubes. Furthermore, the impact of the length, wall thickness, outer diameter, and spacing of heat tubes on the heat transfer performance of the double-layer heat exchanger with and without the nanofluids has been analyzed. The study aims to optimize the JF factor and cost-effectiveness ratio (CER). Genetic algorithms are employed to optimize and evaluate the heat transfer performance of the main heat exchanger based on the four aforementioned parameters. Consequently, a new design scheme is obtained for the double-layer heat exchanger, which increases the optimized overall heat transfer coefficient of the main heat exchanger by 5.79%, pressure drop in the primary loop by 2.32%, JF factor by 5%, and CER by 24.62%. These results demonstrate that the gallium-based graphene nanofluids can effectively enhance the heat transfer performance of the double-layer heat exchanger while reducing the likelihood of steam generator tube rupture accidents.

The semantic segmentation task is an essential issue in various fields, including land cover classification and cultural heritage investigation. The CNN and Transformer have been widely utilized in semantic segmentation tasks due to notable advancements in deep learning technologies. However, these methodologies may not fully account for remote sensing images' distinctive attributes, including the large intra-class variation and the small inter-class variation. Driven by it, we propose a category attention guided network (CAGNet). Initially, a local feature extraction module is devised to cater to striped objects and features at different scales. Then, we propose a novel concept of category attention for remote sensing images as a feature representation of category differences between pixels. Meanwhile, we designed the Transformer-based and CNN-based category attention guided modules to integrate the proposed category attention into the global scoring functions and local category feature weights, respectively. The network is designed to give more attention to the category features by updating these weights during the training process. Finally, a feature fusion module is developed to integrate global, local, and category multi-scale features and contextual information. A series of extensive experiments along with ablation studies on the UAVid, Vaihingen, and Potsdam datasets indicate that our network outperforms existing methods, including those based on CNN and Transformer.

The production of green hydrogen by water splitting from renewable energy is of great significance for achieving sustainable energy development. Alkaline water electrolysis (AWE) is the most economical and feasible route at present due to its earth-abundant catalysts, but it is desirable and crucial to improve the efficiency and purity of hydrogen production. Hydrogen production purity and energy consumption affect the fluctuation range of input power and economy respectively. In this paper, an electrolyzer with independent fluid flow path (IFP) is designed, which improves the problem of shutting current, hydrogen purity and hydrogen production efficiency. It has the characteristics of bipolar plate, zero gap and IFP. A test platform was built to verify its performance. The results showed that the IFP structure significantly improved the purity of hydrogen production, and the energy efficiency value increased by more than 3.29%.

Wireless power transfer (WPT) technology is a novel power transmission technology using electromagnetic field as the transmission medium. It uses the advantages of electromagnetic coupling in the near field to realize contactless transmission of electric energy on the basis of resonance theory. This mode has the advantages of stability, flexibility, efficiency and safety. WPT is mainly used for non-contact charging of electrical equipment without electrical connection. It can safely, flexibly and quickly charge the electrical equipment, which makes up for the shortcomings of the traditional charging mode. Due to the change of the load or the transmission coil position in WPT system, the transmission efficiency will drop sharply. It is of great significance to maintain the maximum efficiency transmission. This paper proposes a novel maximum efficiency transmission method. This method can achieve the maximum efficiency transmission with constant voltage output by controlling the primary-side current without communication feedback between the primary-side and the-secondary side. The Buck-Boost converter with constant output voltage is introduced at the load-side to equivalent the actual load. By adjusting the duty ratio of the Buck converter on the primary side and detecting the minimum input current, the maximum transmission efficiency could be tracked. The theoretical conditions of the method are analyzed and experimental verification is performed. The experimental results of variable relative position load show that the transmission efficiency of the proposed method can still reach 83%.

RGB-D image mapping is an important tool in applications such as robotics, 3D reconstruction, autonomous navigation, and augmented reality (AR). Efficient and reliable mapping methods can improve the accuracy, real-time performance, and flexibility of sensors in various fields. However, the currently widely used Truncated Signed Distance Field (TSDF) still suffers from the problem of inefficient memory management, making it difficult to directly use it for large-scale 3D reconstruction. In order to address this problem, this paper proposes a highly efficient and accurate TSDF voxel fusion method, RGBTSDF. First, based on the sparse characteristics of the volume, an improved grid octree is used to manage the whole scene, and a hard coding method is proposed for indexing. Second, during the depth map fusion process, the depth map is interpolated to achieve a more accurate voxel fusion effect. Finally, a mesh extraction method with texture constraints is proposed to overcome the effects of noise and holes and improve the smoothness and refinement of the extracted surface. We comprehensively evaluate RGBTSDF and similar methods through experiments on public datasets and the datasets collected by commercial scanning devices. Experimental results show that RGBTSDF requires less memory and can achieve real-time performance experience using only the CPU. It also improves fusion accuracy and achieves finer grid details.

Recent years have witnessed the fast development of the Financial Internet of Things (FIoT), which integrates the Internet of Things (IoT) into financial activities. At the same time, the FIoT is facing an increasing number of stealthy network attacks. Long short-term memory (LSTM) can be used as an anomaly-detecting method to perceive such attacks since it specializes in discovering anomaly behaviors through the time correlation in FIoT traffic. However, current LSTM-based anomaly detection schemes have not considered the specific correlations among the features of the whole traffic. In addition, current schemes are usually trained based on local traffic with rare cooperation among different detecting nodes, leading to the result that current schemes usually suffer from insufficient adaptability and low coordination. In this paper, we propose a feature-attended federated LSTM (FAF-LSTM) for FIoT to address the above issues. FAF-LSTM combines feature-attended LSTM and federated learning to make full use of the deep correlation in data and enhance the accuracy of the trained model via cooperation among different detecting nodes. In FAF-LSTM, the features are grouped so that the model can learn the time–spatial correlation inner the flows of each group as well as their impact on the output. Meanwhile, the parameter aggregation is optimized based on feature correlation analysis. Simulations are conducted to verify the effect of FAF-LSTM. The results show that FAF-LSTM has good performance in anomaly detection. Compared with independently trained LSTM and traditional federated learning-based LSTM, FAF-LSTM can improve the detection accuracy by up to 39.22% and 334.36%, respectively.

After an emergency shutdown of a lead-bismuth fast reactor, thermal stratification occurs in the upper Plenum, which negatively impacts the integrity of the reactor structure and the residual heat removal capacity of natural circulation flow. The research on thermal stratification of reactors has mainly been conducted using an experimental method, a system program, and computational fluid dynamics (CFD). However, the equipment required for the experimental method is expensive, accuracy of the system program is unpredictable, and resources and time required for the CFD approach are extensive. To overcome the defects of thermal stratification analysis, a high-precision full-order thermal stratification model based on CFD technology is prepared in this study. Furthermore, a reduced-order model has been developed by combining proper orthogonal decomposition (POD) with Galerkin projection. A comparative analysis of thermal stratification with the proposed full-order model reveals that the reduced-order thermal stratification model can well simulate the temperature distribution in the upper plenum and rapidly elucidate the thermal stratification interface characteristics during the lead-bismuth fast reactor accident. Overall, this study provides an analytical tool for determining the thermal stratification mechanism and reducing thermal stratification.

Abstract Cancer immunotherapy, mainly including immune checkpoints-targeted therapy and the adoptive transfer of engineered immune cells, has revolutionized the oncology landscape as it utilizes patients’ own immune systems in combating the cancer cells. Cancer cells escape immune surveillance by hijacking the corresponding inhibitory pathways via overexpressing checkpoint genes. Phagocytosis checkpoints, such as CD47, CD24, MHC-I, PD-L1, STC-1 and GD2, have emerged as essential checkpoints for cancer immunotherapy by functioning as “don’t eat me” signals or interacting with “eat me” signals to suppress immune responses. Phagocytosis checkpoints link innate immunity and adaptive immunity in cancer immunotherapy. Genetic ablation of these phagocytosis checkpoints, as well as blockade of their signaling pathways, robustly augments phagocytosis and reduces tumor size. Among all phagocytosis checkpoints, CD47 is the most thoroughly studied and has emerged as a rising star among targets for cancer treatment. CD47-targeting antibodies and inhibitors have been investigated in various preclinical and clinical trials. However, anemia and thrombocytopenia appear to be formidable challenges since CD47 is ubiquitously expressed on erythrocytes. Here, we review the reported phagocytosis checkpoints by discussing their mechanisms and functions in cancer immunotherapy, highlight clinical progress in targeting these checkpoints and discuss challenges and potential solutions to smooth the way for combination immunotherapeutic strategies that involve both innate and adaptive immune responses.

Regional lung ventilation assessment is a critical tool for the early detection of lung diseases and postoperative evaluation. Biosensor-based impedance measurements, known for their non-invasive nature, among other benefits, have garnered significant attention compared to traditional detection methods that utilize pressure sensors. However, solely utilizing overall thoracic impedance fails to accurately capture changes in regional lung air volume. This study introduces an assessment method for lung ventilation that utilizes impedance data from the five lobes, develops a nonlinear model correlating regional impedance with lung air volume, and formulates an approach to identify regional ventilation obstructions based on impedance variations in affected areas. The electrode configuration for the five lung lobes was established through numerical simulations, revealing a power–function nonlinear relationship between regional impedance and air volume changes. An analysis of 389 pulmonary function tests refined the equations for calculating pulmonary function parameters, taking into account individual differences. Validation tests on 30 cases indicated maximum relative errors of 0.82% for FVC and 0.98% for FEV1, all within the 95% confidence intervals. The index for assessing regional ventilation impairment was corroborated by CT scans in 50 critical care cases, with 10 validation trials showing agreement with CT lesion localization results.

Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention.

The rapid and reliable detection of oil contaminants is of crucial importance for environmental protection. This paper focused on the classification and identification of six types of oil using unconventional steady-state fluorescence spectroscopy coupled with XY-fused networks (XY-Fs). The excitation emission matrix fluorescence (EEMF) spectroscopy, synchronous fluorescence spectroscopy (SFS) and total synchronous fluorescence spectroscopy (TSFS) of the oil samples were measured by a FS920 steady-state fluorescence spectrometer. Further, the obtained results were compared with those obtained by using partial least square discriminant analysis (PLS-DA), LDA based on score matrix of multivariate curve resolution-alternating least squares (MCR-ALS-LDA) and parallel factor (PARAFAC-LDA) analysis. The experimental results showed that the best accuracy (100%) for the test samples was obtained by TSFS combined with XY-Fs. These results provide important references for discrimination of oil contaminants.

Ethylene is one crucial phytohormone modulating plants’ organ development and ripening process, especially in fruits, but its action modes and discrepancies in non-climacteric grape and climacteric peach in these processes remain elusive. This work is focused on the action mode divergences of ethylene during the modulation of the organ development and ripening process in climacteric/non-climacteric plants. We characterized the key enzyme genes in the ethylene synthesis pathway, VvACO1 and PpACO1, and uncovered that their sequence structures are highly conserved, although their promoters exhibit important divergences in the numbers and types of the cis-elements responsive to hormones, implying various responses to hormone signals. Subsequently, we found the two have similar expression modes in vegetative organ development but inverse patterns in reproductive ones, especially in fruits. Then, VvACO1 and PpACO1 were further validated in promoting fruit ripening functions through their transient over-expression/RNAi-expression in tomatoes, of which the former possesses a weaker role than the latter in the fruit ripening process. Our findings illuminated the divergence in the action patterns and function traits of the key VvACO1/PpACO1 genes in the tissue development of climacteric/non-climacteric plants, and they have implications for further gaining insight into the interaction mechanism of ethylene signaling during the modulation of the organ development and ripening process in climacteric/non-climacteric plants.

Abstract Background Mesenchymal stromal cells (MSCs) activated with IFN-γ elicit stronger physical effects. Exosomes (Exos) secreted from MSCs show protective effects against myocardial injury. This study aimed to determine whether Exos derived from IFN-γ-treated MSCs exhibit more potent cardioprotective function and the underlying mechanisms. Methods H9c2 cells or human umbilical vein endothelial cells (HUVECs) were treated with Exos isolated from MSCs (Ctrl-Exo) or IFN-γ-primed MSCs (IFN-γ-Exo) under oxygen and glucose deprivation (OGD) conditions in vitro and in vivo in an infarcted rat heart. RNA sequencing was used to identify differentially expressed functional transcription factors (TFs). Quantitative reverse transcription-PCR (qPCR) was used to confirm the upregulated TFs and miRNA in IFN-γ-primed MSCs. Dual-luciferase reporter gene assay was used to analyze the transcriptional regulation of miRNAs by STAT1. The target of miR-21-5p (miR-21) was determined by luciferase reporter assays and qPCR. The function of BTG2 was verified in vitro under OGD conditions. Result IFN-γ-Exo accelerated migration and tube-like structure formation and prevented OGD-induced apoptosis in H9c2. Similarly, IFN-γ-Exo treatment caused a decrease in fibrosis, reduced cardiomyocyte apoptosis, and improved cardiac function compared to Ctrl-Exo treatment. MiR-21 was significantly upregulated in IFN-γ-primed MSCs and IFN-γ-Exo. STAT1 transcriptionally induced miR-21 expression. Up-regulated miR-21 could inhibit BTG anti-proliferation factor 2 (BTG2) expressions. BTG2 promoted H9c2 cell apoptosis and reversed the protective effects of miR-21 under OGD conditions. Conclusion IFN-γ-Exo showed enhanced therapeutic efficacy against acute MI, possibly by promoting angiogenesis and reducing apoptosis by upregulating miR-21, which directly targeted BTG2.

Abstract Catechin (Cat) is one of the primary functional components of tea, and its accurate detection is significant for understanding the health benefits of tea. In this study, cyclodextrin nanosponges (CDNS) were prepared and employed to combine with graphene quantum dots (GQDs) and carboxylated multi‐walled carbon nanotubes (MWCNTs‐COOH) to form a CDNS/GQDs/MWCNTs‐COOH composite. Scanning electron microscope and X‐ray diffraction were used to characterize the composite which was used to build a new electrochemical Cat sensor. The abundance of cavities, intermolecular gaps, and surface active groups of CD molecules endowed CDNS with remarkable Cat enrichment capacity. Due to the outstanding biocompatibility of GQDs, the good electrocatalytic activity of MWCNTs‐COOH, and the enrichment capacity of CDNS, the composite modified glass carbon electrode (GCE) exhibited excellent electrochemical sensing performance. The CDNS/GQDs/MWCNTs‐COOH/GCE showed a sensitive peak current response to catechins in the linear range between 2 × 10−8 and 1.2 × 10−6 M, with a minimum limit of detection (S/N = 3) of 4.45 nM. The sensor has also been successfully applied for catechin detection in tea beverages.

The purpose of the study was to investigate the stability and oral delivery of DHA-encapsulated Pickering emulsions stabilized by soy protein isolate–chitosan (SPI-CS) nanoparticles (SPI-CS Pickering emulsions) under various conditions and in the simulated gastrointestinal (GIT) model. The stability of DHA was characterized by the retention rate under storage, ionic strength, and thermal conditions. The oral delivery efficiency was characterized by the retention and release rate of DHA in the GIT model and cell viability and uptake in the Caco-2 model. The results showed that the content of DHA was above 90% in various conditions. The retention rate of DHA in Pickering emulsions containing various nanoparticle concentrations (1.5 and 3.5%) decreased to 80%, while passing through the mouth to the stomach, and DHA was released 26% in 1.5% Pickering emulsions, which was faster than that of 3.5% in the small intestine. After digestion, DHA Pickering emulsions proved to be nontoxic and effectively absorbed by cells. These findings helped to develop a novel delivery system for DHA.

Allicin is a kind of natural antimicrobial active substance, but its water solubility is poor, and it is easy to degrade. In order to improve the stability and bioavailability of allicin, allicin–zein composite nanoparticles (Al-Ze) were prepared by the combination method of antisolvent precipitation and electrostatic deposition, and their characteristic parameters, such as average particle size, polydispersity index (PDI), and ζ-potential, were analyzed. Then, Al-Ze was used as the delivery carrier for the active substance (allicin), and gelatin with good film-forming properties was selected as the film-forming matrix to prepare Al-Ze gelatin films. The optical properties, mechanical properties, and characterization parameters were used to analyze the prepared composite materials; the results confirmed that Al-Ze gelatin film has good mechanical properties and barrier properties. The prepared film was applied to the storage of cold, fresh beef, and the quality change of beef was monitored at 4 °C. The results showed that Al-Ze gelatin film could effectively delay the quality deterioration of beef. This paper provides a new idea and data support for the application of Al-Ze gelatin film in meat storage and fresh-keeping, and offers new insight for the promotion and application of allicin in the food industry.

Abstract Background Chinese herbal medicine is made up of hundreds of natural drug molecules and has played a major role in traditional Chinese medicine (TCM) for several thousand years. Therefore, it is of great significance to study the target of natural drug molecules for exploring the mechanism of treating diseases with TCM. However, it is very difficult to determine the targets of a fresh natural drug molecule due to the complexity of the interaction between drug molecules and targets. Compared with traditional biological experiments, the computational method has the advantages of less time and low cost for targets screening, but it remains many great challenges, especially for the molecules without social ties. Methods This study proposed a novel method based on the Cosine-correlation and Similarity-comparison of Local Network (CSLN) to perform the preliminary screening of targets for the fresh natural drug molecules and assign weights to them through a trained parameter. Results The performance of CSLN is superior to the popular drug-target-interaction (DTI) prediction model GRGMF on the gold standard data in the condition that is drug molecules are the objects for training and testing. Moreover, CSLN showed excellent ability in checking the targets screening performance for a fresh-natural-drug-molecule (scenario simulation) on the TCMSP (13 positive samples in top20), meanwhile, Western-Blot also further verified the accuracy of CSLN. Conclusions In summary, the results suggest that CSLN can be used as an alternative strategy for screening targets of fresh natural drug molecules.

ObjectivesInfluenza is an infectious respiratory disease that can cause severe inflammatory reactions and threaten human life. Chaishi Tuire Granules (CSTRG), a Chinese patent medicine widely used clinically in the treatment of respiratory diseases in China, has a definite anti-inflammatory effect. However, the mechanism of CSTRG in the treatment of influenza is still unclear. This study aimed to demonstrate the anti-inflammatory effect of CSTRG on influenza A treatment and potential mechanisms.MethodsInfluenza-associated mice pneumonia model was used to explore the antiviral and anti-inflammatory effects of CSTRG in vivo. Bioinformatics analysis methods such as network pharmacology and molecular docking were carried out to predict the main active components and potential anti-inflammatory targets of CSTRG. The anti-inflammatory activity of CSTRG was determined using the lipopolysaccharide (LPS)-induced macrophages RAW264.7 cells in vitro.ResultsIn vivo results showed that CSTRG can reduce the viral load in the lung tissue of infected mice, reduce the expression of TNF-α and IL-6 in lung tissue and serum, and regulate the host inflammatory response. Additionally, CSTRG treatment markedly improves the sick signs, weight loss, lung index, and lung pathological changes. Bioinformatics analysis predicted that six active compounds of CSTRG including quercetin, kaempferol, luteolin, beta-sitosterol, sitosterol, and stigmasterol could contribute to the anti-influenza activity through regulating the TRAF6/MAPK14 axis. The following research confirmed that CSTRG significantly inhibited pro-inflammatory cytokines (TNF-α and IL-6) by suppressing the expression of TRAF6 and MAPK14 in LPS-stimulated macrophages RAW264.7 cells.ConclusionCSTRG might inhibit the inflammatory response by mediating the TRAF6/MAPK14 axis. In the future, in-depth research is still needed to verify the mechanism of CSTRG in the treatment of influenza.

Alterations in thermal hydraulic parameters directly affect the safety of reactors. Accurately predicting the trends of key thermal hydraulic parameters under various working conditions can greatly improve reactor safety, thereby effectively preventing the occurrence of nuclear power plant accidents. The thermal hydraulic characteristic parameters in the reactor are affected by many factors, in order to preliminarily study whose forecasting methods and determine the feasibility of neural network forecasting, the China Experimental Fast Reactor (CEFR) is selected as the research target in this study, and the maximum surface temperature of fuel rod sheath and mass flow rate are used as predictive variables. After data samples are generated through the reactor sub channel analysis code (named SUBCHANFLOW), two widely used adaptive neural networks are used to perform the thermal hydraulic parameter forecast analysis of CEFR fuel assembly under steady-state conditions. The 1/2 core model of CEFR is used to perform a single-step predictive analysis of thermal hydraulic parameters under transient conditions. The results show that the adaptive radial basis function (RBF) neural network exhibits a better fitting ability and higher forecasting accuracy than that of the adaptive back propagation neural network, and the maximum error under steady-state conditions is 0.5%. Under transient conditions, poor forecasting accuracy is observed for some local points; however, the adaptive RBF neural network is generally excellent at predicting temperature and mass flow. The mean relative error of temperature does not exceed 1%, and the mean relative error of flow does not exceed 6.5%. The proposed RBF neural network model can provide real-time forecasting in a short time under unstable flow conditions, and its forecasting results have a certain reference value.

Background The HAK family is the largest potassium (K+) transporter family, vital in K+ uptake, plant growth, and both plant biotic and abiotic stress responses. Although HAK family members have been characterized and functionally investigated in many species, these genes are still not studied in detail in Medicago truncatula, a good model system for studying legume genetics. Methods In this study, we screened the M. truncatula HAK family members (MtHAKs). Furthermore, we also conducted the identification, phylogenetic analysis, and prediction of conserved motifs of MtHAKs. Moreover, we studied the expression levels of MtHAKs under K+ deficiency, drought, and salt stresses using quantitative real-time PCR (qRT-PCR). Results We identified 20 MtHAK family members and classified them into three clusters based on phylogenetic relationships. Conserved motif analyses showed that all MtHAK proteins besides MtHAK10 contained the highly conserved K+ transport domain (GVVYGDLGTSPLY). qRT-PCR analysis showed that several MtHAK genes in roots were induced by abiotic stress. In particular, MtHAK15, MtHAK17, and MtHAK18 were strongly up-regulated in the M. truncatula roots under K+ deficiency, drought, and salt stress conditions, thereby implying that these genes are good candidates for high-affinity K+ uptake and therefore have essential roles in drought and salt tolerance. Discussions Our results not only provided the first genetic description and evolutionary relationships of the K+ transporter family in M. truncatula, but also the potential information responding to K+ deficiency and abiotic stresses, thereby laying the foundation for molecular breeding of stress-resistant legume crops in the future.

BackgroundThe prognostic nutrition index (PNI), which has been evaluated in various kinds of cancers, offered a simple yet effective approach to predict the prognosis. The aim of this meta-analysis is to reveal the correlation between preoperative PNI and the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) who underwent curative resection.MethodsWe searched the PubMed, Embase, Web of Science and Cochrane Library databases, and extracted the hazard ratio (HR) with 95% confidential interval (CI) from eligible studies. The pooled HR with 95% CI was applied to evaluate the association between PNI and overall survival (OS), recurrence-free survival (RFS).ResultsA total of fourteen studies with 3,385 patients were included for meta-analysis. The results (the pooled HR: 1.664, 95% CI: 1.424–1.994, I² = 42.6%, p value = 0.046) indicated that low preoperative PNI was closely related to poor OS. In addition, the results suggested that PNI was negatively correlated with RFS (the pooled HR: 1.369, 95%CI: 1.080–1.734). The robustness of these pooled results was verified by our subgroup analysis and sensitivity analysis. Moreover, different cutoff values among studies are responsible for the heterogeneity of pooled HR of OS through meta-regression analysis (p value = 0.042). Funnel plots, Begg's test (p value = 0.228) and Egger’s test (p value = 0.702) indicated no significant publication bias in OS.ConclusionPreoperative PNI might be a promising marker to predict the prognosis of PDAC patients who underwent curative resection.

BackgroundThree-dimensional activation mapping during sinus rhythm can demonstrate the earliest atrial activation (EAA) site, which could be the sinoatrial node (SAN). We aimed to compare the electroanatomical characteristics of superior vena cava (SVC), myocardial sleeve, and SAN between patients with atrial fibrillation (AF) and non-AF.Materials and methodsIn this study, 136 patients with AF were assigned to the study group, and 20 patients with premature ventricular contractions (PVCs) who had no history of AF were assigned to the control group. The right atrium (RA) and SVC anatomical activation models were constructed, and the EAA of SAN was delineated using the CARTO3 mapping system. The length of the SVC myocardial sleeve (LSVC) was measured.ResultsOf the 136 patients, 93 patients had paroxysmal AF (PAF), and 43 patients had persistent AF (PsAF). The LSVC was not significantly different among AF and non-AF, PAF, and PsAF. The LSVC in men was longer than in women (42.1 ± 9.4 mm vs. 35.4 ± 8.1 mm, p < 0.001). The LSVC was longer in patients with EAA of SAN above the RA-SVC junction than in those with below the RA-SVC junction (p < 0.001). The EAA of SAN was below the RA-SVC junction in 64/136 (47.1%) and was above the junction in 72/136 (52.9%) patients with AF. The spatial distribution of the EAA of SAN between PAF and PsAF was not different. There was a trend of statistical difference in the distribution of the EAA of SAN between PsAF and non-AF.ConclusionThe EAA of SAN was located in the SVC in most of the patients, especially in patients with PsAF.

To promote the practical application of TiO2 in photocatalytic toluene oxidation, the honeycomb aluminum plates were selected as the metal substrate for the loading of TiO2 powder. Surface-etching treatment was performed and titanium tetrachloride was selected as the binder to strengthen the loading stability. The loading stability and photocatalytic activity of the monolithic catalyst were further investigated, and the optimal surface treatment scheme (acid etching with 15.0 wt.% HNO3 solution for 15 min impregnation) was proposed. Therein, the optimal monolithic catalyst could achieve the loading efficiency of 42.4% and toluene degradation efficiencies of 76.2%. The mechanism for the stable loading of TiO2 was revealed by experiment and DFT calculation. The high surface roughness of metal substrate and the strong chemisorption between TiO2 and TiCl4 accounted for the high loading efficiency and photocatalytic activity. This work provides the pioneering exploration for the practical application of TiO2 catalysts loaded on the surface of metal substrate for VOCs removal, which is of significance for the large-scaled application of photocatalytic technology.

To improve the inherent safety and cost-effectiveness of lead-bismuth cooled fast reactors, the SPALLER-100 reactor designed by the University of South China has been selected as the research object to determine the maximum power it can produce. This is a multi-objective, complex, multi-dimensional, nonlinear, and constrained optimization problem. To maintain the transportability, material durability, and long-term operation stability of the reactor core and ensure safety under accident conditions, three steady-state limitations and three accident limitations are proposed. The platform used to calculate the maximum neutronic power produced by the reactor at different core heights has been built using Latin hypercube sampling and the Kriging proxy model. Meanwhile, the cooling power of the reactor at different core heights is calculated by considering its natural circulation capacity. Finally, a design scheme is obtained that meets the requirements of neutronic and thermal-hydraulic assessments, while producing maximum power. Consequently, during the entire life-cycle of SPALLER-100, a safety analysis of three typical accident scenarios (unprotected loss of heat sink, unprotected transient over power, and unprotected coolant inlet temperature undercooling) is performed using a Quasi-Static Reactivity Balance (QSRB) approach. The results show that the platform used to calculate the maximum neutronic power exhibits high accuracy, and that the design scheme with maximum power is safe and economical. Overall, this study can provide reference ideas for designing natural circulation reactors that can maximize power output.

BackgroundPancreatic cancer mortality is growing every year, and radical resection is the most essential therapy strategy. It is critical to evaluate the long-term prognosis of individuals receiving radical surgery. CA19-9 is a biomarker for patient recurrence and survival, however obstructive jaundice has a significant impact on this index. Researchers have attempted to modify the index using various modification methods, but the results have been unsatisfactory. In this study, we adjusted CA19-9 values based on clinical stage and bilirubin and found that it provided better prediction than CA19-9 alone in assessing patients.MethodsWe analyzed over 5 years follow-up records of patients who underwent radical pancreatic cancer surgery between August 2009 and May 2017 in a single center. We investigated the association of risk factors with overall survival (OS) as well as disease-free survival (DFS) after surgery. Threshold values for high-risk features associated with poor prognosis in resectable pancreatic cancer were determined. The hazard ratios of the indicators were eventually examined under the stratification of patients’ clinical stages.ResultsA total of 202 patients were involved in the study. The optimum cut-off values for CA19-9 and CA19-9/TB for predicting overall survival were 219.4 (p = 0.0075) and 18.8 (p = 0.0353), respectively. CA19-9>219.4 increased the risk of patient mortality by 1.70 times (95% CI 1.217-2.377, p = 0.002), and tumor poor differentiation raised the risk by 1.66 times (95% CI 1.083-2.553, P = 0.02). Based on clinical stage stratification, we found discrepancies in the predictive efficacy of CA19-9 and CA19-9/TB. CA19-9 was a better predictor in clinical stage 1 (HR = 2.056[CI 95%1.169-3.616], P = 0.012), whereas CA19-9/TB indications were better in stages 2 (HR = 1.650[CI 95%1.023-2.662], P = 0.040) and 3 (HR = 3.989[CI95%1.145-13.896], P = 0.030).ConclusionsCA19-9, CEA, and tumor differentiation are predictors for patients with resectable PDAC. CA19-9 values can be adjusted based on clinical stage and bilirubin levels to better predict overall survival in patients with resectable PDAC. CA19-9>219.4 predicted poor survival in individuals in clinical stage 1, whereas CA19-9/TB>18.8 predicted poor survival for individuals in stages 2 and 3.

Abstract Background Oral squamous cell carcinoma (OSCC) is the dominant histologic type of oral cancer. Locally advanced OSCC remains a major therapeutic challenge. Our study aimed to develop and validate nomograms predicting survival prognosis in patients with locally advanced oral squamous cell carcinoma (OSCC) after curative resection. Methods A total of 269 consecutive patients with primary OSCC who received curative resection between September 2007 and March 2020 were retrospectively enrolled in our study. Patients were randomly assigned to the training cohort (n = 201) or the validation cohort (n = 68). Multivariate Cox regression analyses were conducted to determine independent prognostic factors for overall survival (OS) and cancer specific survival (CSS) in the training set, which were used to develop nomogram models estimating 3-, and 5-year OS and CSS. We also evaluated the nomograms using concordance indices (c-index), calibration curves, and decision curve analyses (DCA), and compared those with the AJCC 8th staging system. The results were externally validated in the validation cohort. Results Age, Kaplan-Feinstein (KFI) index, pT, the number of positive nodes and systemic inflammatory index (SII) were significant prognostic predictors for OS and CSS. The OS nomogram had c-index values of 0.712 in the training set and 0.697 in the validation set, while the CSS nomogram exhibited c-index values of 0.709 in the training set and 0.675 in the validation set. These data were superior to those of AJCC 8th staging system, suggesting high discriminative ability of the nomograms. Calibration curves exhibited good agreement between observed and predicted survival. DCA curves indicated the nomograms were with potential clinical usefulness. These results were validated in the validation set. Conclusions The novel nomograms incorporating clinically available characteristics for OS and CSS prediction were developed in the locally advanced OSCC patients after curative surgery. Validation revealed good discrimination and calibration, indicating the clinical utility of the nomograms in the individualized prognosis prediction of locally advanced OSCC after curative surgery.

Abstract Ischemic cardiomyopathy seriously endangers human health leading to a poor prognosis. Acute myocardial infarction (AMI) is the primary etiology, and the pathophysiological process concludes with the death of cardiomyocytes caused by acute and persistent ischemia and hypoxia in the coronary arteries. We identified a circRNA (circSNRK) which was downregulated in rats with myocardial infarction (MI), however, the role it plays in the MI environment is still unclear. This study contained experiments to investigate the role of circSNRK in the regulation of cardiac survival and explore the mechanisms underlying circSNRK functions. Quantitative real-time PCR (qRT-PCR) was performed to determine the circSNRK expression patterns in hearts. Gain-of-function assays were also conducted in vitro and in vivo to determine the role of circSNRK in cardiac repair. qRT-PCR, western blot, and luciferase reporter assays were used to study circRNA interactions with micro RNAs (miRNAs). Overexpression of circSNRK in cardiomyocytes reduced apoptosis and increased proliferation. Adeno associated virus 9 (AAV9) mediated myocardium overexpression of circSNRK in post MI hearts reduced cardiomyocyte apoptosis, promoted cardiomyocyte proliferation, enhanced angiogenesis, and improved cardiac functions. Overall, upregulation of circSNRK promotes cardiac survival and functional recovery after MI. Mechanistically, circSNRK regulates cardiomyocyte apoptosis and proliferation by acting as a miR-103-3p sponge and inducing increased expression of SNRK which can bind GSK3β to regulate its phosphorylated activity. And thus circSNRK may be a promising therapeutic target for improving clinical prognosis after MI.

Tunnel magnetoresistance (TMR) can measure weak magnetic fields and has significant advantages for use in alternating current/direct current (AC/DC) leakage current sensors for power equipment; however, TMR current sensors are easily perturbed by external magnetic fields, and their measurement accuracy and measurement stability are limited in complex engineering application environments. To enhance the TMR sensor measurement performance, this paper proposes a new multi-stage TMR weak AC/DC sensor structure with high measurement sensitivity and anti-magnetic interference capability. The front-end magnetic measurement characteristics and interference immunity of the multi-stage TMR sensor are found to be closely related to the multi-stage ring size design via finite element simulation. The optimal size of the multipole magnetic ring is determined using an improved non-dominated ranking genetic algorithm (ACGWO-BP-NSGA-II) to derive the optimal sensor structure. Experimental results demonstrate that the newly designed multi-stage TMR current sensor has a measurement range of 60 mA, a fitting nonlinearity error of less than 1%, a measurement bandwidth of 0–80 kHz, a minimum AC measurement value of 85 μA and a minimum DC measurement value of 50 μA, as well as a strong external electromagnetic interference. The TMR sensor can effectively enhance measurement precision and stability in the presence of intense external electromagnetic interference.

The wall-associated kinases (WAKs) can perceive and transmit extracellular signals as one kind of unique receptor-like kinases (RLKs) involved in the regulation of cell expansion, pathogen resistance and abiotic stress tolerance. To understand their potential roles and screen some key candidates in Medicago truncatula (M. truncatula), genome-wide identification and characterization of MtWAKs were conducted in this study. A total of 54 MtWAK genes were identified and classified into four groups based on their protein domains. They were distributed on all chromosomes, while most of them were clustered on chromosome 1 and 3. The synteny analysis showed that 11 orthologous pairs were identified between M. truncatula and Arabidopsis thaliana (A. thaliana) and 31 pairs between M. truncatula and Glycine max (G. max). The phylogenetic analysis showed that WAK-RLKs were classified into five clades, and they exhibited a species-specific expansion. Most MtWAK-RLKs had similar exon–intron organization and motif distribution. Multiple cis-acting elements responsive to phytohormones, stresses, growth and development were observed in the promoter regions of MtWAK-RLKs. In addition, the expression patterns of MtWAK-RLKs varied with different plant tissues, developmental stages and biotic and abiotic stresses. Interestingly, plasm membrane localized MtWAK24 significantly inhibited Phytophthora infection in tobacco. The study provides valuable information for characterizing the molecular functions of MtWAKs in regulation of plant growth, development and stress tolerance in legume plants.

Using photovoltaic (PV) energy to produce hydrogen through water electrolysis is an environmentally friendly approach that results in no contamination, making hydrogen a completely clean energy source. Alkaline water electrolysis (AWE) is an excellent method of hydrogen production due to its long service life, low cost, and high reliability. However, the fast fluctuations of photovoltaic power cannot integrate well with alkaline water electrolyzers. As a solution to the issues caused by the fluctuating power, a hydrogen production system comprising a photovoltaic array, a battery, and an alkaline electrolyzer, along with an electrical control strategy and energy management strategy is proposed. The energy management strategy takes into account the predicted PV power for the upcoming hour and determines the power flow accordingly. By analyzing the characteristics of PV panels and alkaline water electrolyzers and imposing the proposed strategy, this system offers an effective means of producing hydrogen while minimizing energy consumption and reducing damage to the electrolyzer. The proposed strategy has been validated under various scenarios through simulations. In addition, the system’s robustness was demonstrated by its ability to perform well despite inaccuracies in the predicted PV power.

The status of zinc oxide (ZnO) arresters is directly related to the safety of power grids. However, as the service life of ZnO arresters increases, their insulation performance may decrease due to factors such as operating voltage and humidity, which can be identified through the measurement of leakage current. Tunnel magnetoresistance (TMR) sensors with high sensitivity, good temperature stability, and small size are excellent for measuring leakage current. This paper constructs a simulation model of the arrester and investigates the deployment of the TMR current sensor and the size of the magnetic concentrating ring. The arrester’s leakage current magnetic field distribution under different operating conditions is simulated. The simulation model can aid in optimizing the detection of leakage current in arresters using TMR current sensors, and the findings serve as a basis for monitoring the condition of arresters and improving the installation of current sensors. The TMR current sensor design offers potential advantages such as high accuracy, miniaturization, and ease of distributed application measurement, making it suitable for large-scale use. Finally, the validity of the simulations and conclusions is verified through experiments.

Abstract Background Exosome transplantation is a promising cell-free therapeutic approach for the treatment of ischemic heart disease. The purpose of this study was to explore whether exosomes derived from Macrophage migration inhibitory factor (MIF) engineered umbilical cord MSCs (ucMSCs) exhibit superior cardioprotective effects in a rat model of AMI and reveal the mechanisms underlying it. Results Exosomes isolated from ucMSCs (MSC-Exo), MIF engineered ucMSCs (MIF-Exo) and MIF downregulated ucMSCs (siMIF-Exo) were used to investigate cellular protective function in human umbilical vein endothelial cells (HUVECs) and H9C2 cardiomyocytes under hypoxia and serum deprivation (H/SD) and infarcted hearts in rats. Compared with MSC-Exo and siMIF-Exo, MIF-Exo significantly enhanced proliferation, migration, and angiogenesis of HUVECs and inhibited H9C2 cardiomyocyte apoptosis under H/SD in vitro. MIF-Exo also significantly inhibited cardiomyocyte apoptosis, reduced fibrotic area, and improved cardiac function as measured by echocardiography in infarcted rats in vivo. Exosomal miRNAs sequencing and qRT-PCR confirmed miRNA-133a-3p significantly increased in MIF-Exo. The biological effects of HUVECs and H9C2 cardiomyocytes were attenuated with incubation of MIF-Exo and miR-133a-3p inhibitors. These effects were accentuated with incubation of siMIF-Exo and miR-133a-3p mimics that increased the phosphorylation of AKT protein in these cells. Conclusion MIF-Exo can provide cardioprotective effects by promoting angiogenesis, inhibiting apoptosis, reducing fibrosis, and preserving heart function in vitro and in vivo. The mechanism in the biological activities of MIF-Exo involves miR-133a-3p and the downstream AKT signaling pathway.

Small Modular Reactors (SMRs) are attracting wide attention due to their outstanding performance, extensive studies have been carried out for lead-based fast reactors (LFRs) that cooled with Lead or Lead-bismuth (LBE), and small modular natural circulation LFR is one of the promising candidates for SMRs and LFRs development. One of the challenges for the design small modular natural circulation LFR is to master the natural circulation thermal-hydraulic performance in the reactor primary circuit, while the natural circulation characteristics is a coupled thermal-hydraulic problem of the core thermal power, the primary loop layout and the operating state of secondary cooling system etc. Thus, accurate predicting the natural circulation LFRs thermal-hydraulic features are highly required for conducting reactor operating condition evaluate and Thermal hydraulic design optimization. In this study, a thermal-hydraulic analysis code is developed for small modular natural circulation LFRs, which is based on several mathematical models for natural circulation originally. A small modular natural circulation LBE cooled fast reactor named URANUS developed by Korea is chosen to assess the code's capability. Comparisons are performed to demonstrate the accuracy of the code by the calculation results of MARS, and the key thermal-hydraulic parameters agree fairly well with the MARS ones. As a typical application case, steady-state analyses were conducted to have an assessment of thermal-hydraulic behavior under nominal condition, and several parameters affecting natural circulation were evaluated. What's more, two characteristics parameters that used to analyze natural circulation LFRs natural circulation capacity were established. The analyses show that the core thermal power, thermal center difference and flow resistance is the main factors affecting the reactor natural circulation. Improving the core thermal power, increasing the thermal center difference and decreasing the flow resistance can significantly increase the reactor mass flow rate. Characteristics parameters can be used to quickly evaluate the natural circulation capacity of natural circulation LFR under normal operating conditions.