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A pair of atropisomers secofumitremorgins C (1a) and D (1b), together with fifteen known alkaloids (2-16), were isolated from a saltern-derived fungus Aspergillus fumigatus GXIMD00544. The structures of atropisomers 1a and 1b were elucidated by the detailed spectroscopic data, chemical reaction and quantum chemical calculations. Compounds 1 and 8 displayed antifungal spore germination effects against plant pathogenic fungus associated with sugarcane Fusarium sp. with inhibitory rates of 53% and 77% at the concentration of 100 µM, repectively. Atropisomers 1 also exhibited antifouling potential against Balanus amphitrite larval settlement with an inhibitory rate of 96% at the concentration of 100 µM. [ABSTRACT FROM AUTHOR]

A finite model incorporating microstructure was developed to investigate the thermal shock resistance of TiB2‒B4C composite ceramic cutting tool materials. Numerical simulations were conducted to analyze changes in the transient temperature field and thermal stress field during the thermal shock process. TiB2‒B4C composite ceramic tool materials were fabricated using discharge plasma sintering technology. With different B4C contents, different sintering temperatures and holding times as study variables, optimization of the sintering process parameters and ceramic material components was achieved through testing the mechanical and microscopic morphology of the ceramics. Thermal shock water quenching experiments were performed, and the strength‐decay method was employed to assess the thermal shock resistance of TiB2‒B4C ceramics. The findings indicate that the optimal sintering conditions are temperature of 1700°C and holding time of 5 min. The TiB2‒B4C ceramic material containing 20 vol% B4C exhibits superior comprehensive mechanical properties and thermal shock resistance, and its relative density, fracture toughness, hardness, and flexural strength were 99.3%, 6.8 MPa m1/2, 22.8 GPa, and 598 MPa, respectively. The critical thermal shock temperature difference falls within the range of 400°C‒500°C. [ABSTRACT FROM AUTHOR]

Here, an approach to produce a hierarchical porous Fe‐N‐C@TABOH catalyst with densely accessible high intrinsic active FeNx sites is proposed. The method involves a single‐step pyrolysis of Zn/Fe‐zeolitic imidazolate framework (Zn/Fe‐ZIF‐H) with tetrabutylammonium hydroxide (TABOH) micelles, which is obtained by utilizing TABOH as a structural template and electronic mediator at room temperature for a brief duration of 16 min. Notably, the yield of Zn/Fe‐ZIF‐H is 3.5 times that of Zn/Fe‐ZIF‐N prepared by conventional method. Results indicate that in addition to expediting synthesis and increasing yield of the Zn/Fe‐ZIF‐H, the TABOH induces a hierarchical porous structure and fosters the formation of more and higher intrinsic active FeNx moieties in Fex‐N‐C@TABOH, showing that TABOH is a multifunctional template. Crucially, the increased mesoporosity/external surface area and optimized microenvironment of Fe‐N‐C@TABOH significantly enhance ORR activity by facilitating the formation of high intrinsic active FeNx sites, increasing accessible FeNx sites, and reducing mass transfer resistance. Through structure tailoring and microenvironment optimization, the resulting Fe‐N‐C@TABOH exhibits superior ORR performance. DFT calculation further validates that the synergistic effect of these two factors leads to low ORR barrier and optimized *OH adsorption energy. This study underscores the importance of structure and electronic engineering in the development of highly active ORR catalysts. [ABSTRACT FROM AUTHOR]

Summary: Wood formation, which occurs mainly through secondary xylem development, is important not only for supplying raw material for the 'ligno‐chemical' industry but also for driving the storage of carbon. However, the complex mechanisms underlying the promotion of xylem formation remain to be elucidated. Here, we found that overexpression of Auxin‐Regulated Gene involved in Organ Size (ARGOS) in hybrid poplar 84 K (Populus alba × Populus tremula var. glandulosa) enlarged organ size. In particular, PagARGOS promoted secondary growth of stems with increased xylem formation. To gain further insight into how PagARGOS regulates xylem development, we further carried out yeast two‐hybrid screening and identified that the auxin transporter WALLS ARE THIN1 (WAT1) interacts with PagARGOS. Overexpression of PagARGOS up‐regulated WAT1, activating a downstream auxin response promoting cambial cell division and xylem differentiation for wood formation. Moreover, overexpressing PagARGOS caused not only higher wood yield but also lower lignin content compared with wild‐type controls. PagARGOS is therefore a potential candidate gene for engineering fast‐growing and low‐lignin trees with improved biomass production. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification is widely applied in several fields. Since existing datasets focus on a single scene, current deep learning-based methods typically divide patches randomly on the same image as training and testing samples. This can result in similar spatial distributions of samples, which may incline the network to learn specific spatial distributions in pursuit of falsely high accuracy. In addition, the large variation between single-scene datasets has led to research in cross-scene hyperspectral classification, focusing on domain adaptation and domain generalization while neglecting the exploration of the generalizability of models to specific variables. This paper proposes two approaches to address these issues. The first approach is to train the model on the original image and then test it on the rotated dataset to simulate cross-scene evaluation. The second approach is constructing a new cross-scene dataset for spatial distribution variations, named GF14-C17&C16, to avoid the problems arising from the existing single-scene datasets. The image conditions in this dataset are basically the same, and only the land cover distribution is different. In response to the spatial distribution variations, this paper proposes a constrained spectral attention mechanism and a constrained spatial attention mechanism to limit the fitting of the model to specific feature distributions. Based on these, this paper also constructs a constrained spectral–spatial attention residual network (CSSARN). Extensive experimental results on two public hyperspectral datasets and the GF14-C17&C16 dataset have demonstrated that CSSARN is more effective than other methods in extracting cross-scene spectral and spatial features. [ABSTRACT FROM AUTHOR]

With the rapid development of information technologies, shared manufacturing is proposed to meet the prevailing tendency of servitization and digitalisation in the industry. As the crucial section for performing shared manufacturing, resource monitoring and maintenance aim to detect production exceptions and ensure normal task execution. Existing research mainly uses a resource-centric strategy to acquire production-related data and make decisions for the management of shared resources. The experience data from the users/customers of the shared resources or its similar resources is rarely acquired actively in a cost-effective manner. However, the user/customer's experience data may contain essential knowledge that can be used for effective production performance identification and maintenance. To fill this gap, a hybrid sensing-based approach is proposed to perform the monitoring and maintenance of the shared manufacturing resources. It leverages both the sensor-sensed production data and user/customer-generated data for value creation in a cost-effective manner. Based on the acquired hybrid data, a service model is constructed to achieve the monitoring of the shared manufacturing resource, and a knowledge-based mechanism is designed to perform efficient maintenance. A case study is further presented to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Introduction: Triptolide (TP), a natural product derived from the herbal medicine Tripterygium wilfordii, exhibits potent immunosuppressive activity. However, the mechanisms underlying its effects in rheumatoid arthritis remain incompletely understood. Methods: Collagen‐induced arthritis (CIA) model was induced in Sprague−Dawley rats by immunization with bovine type II collagen, and TP was administrated as treatment. The therapeutic effect of TP was evaluated based on paw swelling, histopathology, and serum levels of inflammatory factors. Exosomes isolated from rat serum were characterized by transmission electron microscopy, dynamic light scattering, and western blot analysis. Proteomic profiling of exosomes was analyzed by direct DIA quantitative proteomics analysis. Gene ontology and the Kyoto Encyclopedia of Genes and Genomes databases were employed for enrichment analysis related to molecular function, biological processes, and signaling pathways. Western blot analysis was used to analyze differentially expressed proteins. Results: TP treatment ameliorated arthritic phenotypes in CIA rats as evidenced by reduced arthritis score, paw swelling, pathological injury severity scores, and serum levels of inflammatory cytokines. The proteomic analysis revealed that TP treatment significantly inhibited complement and coagulation cascades, interleukin‐17 signaling pathway, and cholesterol metabolism, which were reactivated in CIA rats. Importantly, lipocalin 2 (LCN2) and myeloperoxidase (MPO) levels were markedly upregulated in the CIA group but suppressed upon TP administration. Furthermore, in synovial tissues, LCN2 and MPO expression levels were also elevated in the CIA group but decreased following TP treatment. Conclusion: Our findings demonstrate that TP alleviates CIA, possibly through modulation of exosomal LCN2 and MPO proteins. [ABSTRACT FROM AUTHOR]

Although the fusion of multispectral (MS) and hyperspectral (HS) images in remote sensing has become relatively mature, and different types of fusion methods have their own characteristics in terms of fusion effect, data dependency, and computational efficiency, few studies have focused on the impact of radiance extreme areas, which widely exist in real remotely sensed scenes. To this end, this paper proposed a novel method called radiance extreme area compensation fusion (RECF). Based on the architecture of spectral unmixing fusion, our method uses the reconstruction of error map to construct local smoothing constraints during unmixing and utilizes the nearest-neighbor multispectral data to achieve optimal replacement compensation, thereby eliminating the impact of overexposed and underexposed areas in hyperspectral data on the fusion effect. We compared the RECF method with 11 previous published methods on three sets of airborne hyperspectral datasets and HJ2 satellite hyperspectral data and quantitatively evaluated them using 5 metrics, including PSNR and SAM. On the test dataset with extreme radiance interference, the proposed RECF method achieved well in the overall evaluation results; for instance, the PSNR metric reached 47.6076 and SAM reached 0.5964 on the Xiong'an dataset. In addition, the result shows that our method also achieved better visual effects on both simulation and real datasets. [ABSTRACT FROM AUTHOR]

A low-power transmitter driver for chiplet interconnection was designed and experimentally implemented based on the interchip interconnection standard proposed by the UCIe protocol. The driver circuit adopts a source series terminated (SST) diver, whose power consumption is only 1/4 that of the current mode logic (CML) structure. In add-on, based on adjustable feedforward equalization technology, the driver circuit adjusts the equalization strength for different channel attenuations. By de-emphasizing equalization, it enhances the quality of the transmitted signal, ultimately reducing inter-symbol interference. This circuit was designed under CMOS 28 nm process. The front-end simulation results show that the maximum equalization intensity is --3. 7 dB when the 0. 9 V voltage is supplied. When the 32 Gbps NRZ signal passes through the 21 mm channel (the attenuation at the 16 GHz Nyquist frequency is --2. 37 dB), after adjusting the appropriate equalization intensity, the eye height of the output waveform eye diagram is 253 mV (71.8%), the eye width is 27 ps (87%), and the simulation power consumption is only 4. 0 mW. [ABSTRACT FROM AUTHOR]

In the realm of ballistic target analysis, micro-motion attributes, such as warhead precession, nutation, and decoy oscillations, play a pivotal role. This paper addresses these critical aspects by introducing an advanced analytical model for assessing the Doppler power spectra of convex quadric revolution bodies during precession. Our model is instrumental in calculating the Doppler shifts pertinent to both precession and swing cones. Additionally, it extends to delineate the Doppler power spectra for configurations involving cones and sphere–cone combinations. A key aspect of our study is the exploration of the effects exerted by geometric parameters and observation angles on the Doppler spectra, offering a comparative perspective of various micro-motion forms. The simulations distinctly demonstrate how different micro-motion patterns of a cone influence the Doppler power spectra and underscore the significance of geometric parameters and observational angles in shaping these spectra. This research not only contributes to enhancing LIDAR target identification methodologies but also lays a groundwork for future explorations into complex micro-motions like nutation. [ABSTRACT FROM AUTHOR]

A modified optimal velocity with dynamical safety headway is introduced based on the optimal velocity car-following model. The dynamical safety headway relates to vehicle's velocity compared to the constant safety headway. To study the influence of dynamical safety headway on the car-following performance, theoretical analysis and numerical simulation of the optimal velocity car-following model with dynamical safety headway are carried out. With the stability analysis method in control theory, a linear stability condition is obtained for the new model. In addition, the car-following performances of traffic flow evolution rules, traffic capacity properties and vehicle starting features of the new model are exhibited through numerical simulation. The results show that the dynamical safety headway is more efficient than the constant safety headway in improving traffic stability, enhancing traffic capacity and shortening the vehicle starting time in the car-following process. It concludes that the dynamical safety headway is a better strategy compared to constant safety headway in real car-following control. [ABSTRACT FROM AUTHOR]

UAV transmission tower inspection is the use of UAV technology for regular inspection and troubleshooting of towers on transmission lines, which helps to improve the safety and reliability of transmission lines and ensures the stability of the power supply. From the traditional manual tower boarding to the current way of manually selecting target camera shooting points from 3D point clouds to plan the inspection path of the UAV, operational efficiency has drastically improved. However, indoor planning work is still labor-consuming and expensive. In this paper, a deep learning-based point cloud transmission tower segmentation (PCTTS) model combined with the corresponding target point localization algorithm is proposed for automatic segmentation of transmission tower point cloud data and automatically localizing the key inspection component as the target point for UAV inspection. First, we utilize octree sampling with unit ball normalization to simplify the data and ensure translation invariance before putting the data into the model. In the feature extraction stage, we encode the point set information and combine Euclidean distance and cosine similarity features to ensure rotational invariance. On this basis, we adopt multi-scale feature extraction, construct a local coordinate system, and introduce the offset-attention mechanism to enhance model performance further. Then, after the feature propagation module, gradual up-sampling is used to obtain the features of each point to complete the point cloud segmentation. Finally, combining the segmentation results with the target point localization algorithm completes the automatic extraction of UAV inspection target points. The method has been applied to six kinds of transmission tower point cloud data of part segmentation results and three kinds of transmission tower point cloud data of instance segmentation results. The experimental results show that the model achieves mIOU of 94.1% on the self-built part segmentation dataset and 86.9% on the self-built instance segmentation dataset, and the segmentation accuracy outperforms that of the methods for point cloud segmentation, such as PointNet++, DGCNN, Point Transformer, and PointMLP. Meanwhile, the experimental results of UAV inspection target point localization also verify the method's effectiveness in this paper. [ABSTRACT FROM AUTHOR]

Six benzophenone derivatives, carneusones A-F (1–6), along with seven known compounds (7–13) were isolated from a strain of sponge-derived marine fungus Aspergillus carneus GXIMD00543. Their chemical structures were elucidated by detailed spectroscopic data and quantum chemical calculations. Compounds 5, 6, and 8 exhibited moderate anti-inflammatory activity on NO secretion using lipopolysaccharide (LPS)-induced RAW 264.7 cells with EC50 values of 34.6 ± 0.9, 20.2 ± 1.8, and 26.8 ± 1.7 μM, while 11 showed potent effect with an EC50 value of 2.9 ± 0.1 μM. [ABSTRACT FROM AUTHOR]

Multi-spectral imaging technologies have made great progress in the past few decades. The development of snapshot cameras equipped with a specific multi-spectral filter array (MSFA) allow dynamic scenes to be captured on a miniaturized platform across multiple spectral bands, opening up extensive applications in quantitative and visualized analysis. However, a snapshot camera based on MSFA captures a single band per pixel; thus, the other spectral band components of pixels are all missed. The raw images, which are captured by snapshot multi-spectral imaging systems, require a reconstruction procedure called demosaicing to estimate a fully defined multi-spectral image (MSI). With increasing spectral bands, the challenge of demosaicing becomes more difficult. Furthermore, the existing demosaicing methods will produce adverse artifacts and aliasing because of the adverse effects of spatial interpolation and the inadequacy of the number of layers in the network structure. In this paper, a novel multi-spectral demosaicing method based on a deep convolution neural network (CNN) is proposed for the reconstruction of full-resolution multi-spectral images from raw MSFA-based spectral mosaic images. The CNN is integrated with the channel attention mechanism to protect important channel features. We verify the merits of the proposed method using 5 × 5 raw mosaic images on synthetic as well as real-world data. The experimental results show that the proposed method outperforms the existing demosaicing methods in terms of spatial details and spectral fidelity. [ABSTRACT FROM AUTHOR]

This article, published in Stem Cell Reviews & Reports, explores the role of the TET1 protein in the proliferation of mouse skin-derived stem cells (SDSCs) in vitro. The study found that TET1 is crucial for maintaining the self-renewal and proliferation abilities of SDSCs. Knockdown of TET1 resulted in decreased cell proliferation and altered cell cycle distribution. The study suggests that TET1 regulates SDSC proliferation through the DNA replication pathway. This research provides valuable insights into the mechanisms underlying the maintenance of stem cell characteristics and may have implications for regenerative medicine. [Extracted from the article]

Background: Stanford type B aortic dissection (TB‐AD) is a life‐threatening vascular condition with high rates of morbidity and mortality. Currently, thoracic endovascular aortic repair (TEVAR) is widely performed to treat TB‐AD, and some studies have analyzed the influence of stents on hemodynamics using computational fluid dynamics (CFD) models. However, the accuracy of TB‐AD simulation models are not satisfactory, they are often constructed as a regular ideal model. Furthermore, it is unclear which tear should be closed for the best treatment when there are multi entry tears. Purpose: The aims of this paper were to provide an assessment method for the selection of the surgical closure location for type B aortic dissection. Five 3D models of multiple entry tears in type B aortic dissection were produced using real patient computed tomography (CT) images to perform hemodynamic analyses of flow velocity streamlines, wall pressure, and wall shear stress. Methods: A Boolean operation was adopted to establish 3D models with multiple entry tears in type B aortic dissection based on patient‐specific CT images. The Mimics and Ansys plug‐in The Integrated Computer Engineering and Manufacturing code for Computational Fluid Dynamics (ICEM CFD) software were applied to mesh the 3D models. The flow velocity streamlines, wall pressures, and wall shear stresses were then analyzed in the finite element analysis software Fluent. Five 3D models were produced to compare the hemodynamic characteristics of different entry tear numbers, as well as the changes of different closure positions before and after closure. Results: The false lumen of the model with two entry tears had a higher wall pressure than that of model with multiple entry tears, which may tend to squeeze the true lumen and expand the false lumen. The load distribution of the vessel in the model with multiple entry tears had a more balanced flow velocity, and its wall pressure and shear stress were lower than that of model with two entry tears. For aortic dissection with two entry tears, the closure of the proximal entry tear was recommended, which helped to isolate and thrombose the false lumen, thereby improving the blood supply function of the true lumen. Because the postoperative vascular flow velocity and mechanical load performance of the vascular wall were still higher than those of normal blood vessels, the postoperative blood vessels remained pathological, and TEVAR did not restore the blood vessels to their original healthy state. Conclusions: Type B aortic dissection with two entry tears tend to squeeze the true lumen and expand the false lumen, resulting in a new entry tear and deterioration into multiple entry type B aortic dissection. The model of the vessel with multiple entry tears had a more balanced distribution in flow velocity and a smaller wall pressure and shear stress than that of the vessel with two entry tears. The closure of the proximal entry tear was considered an ideal solution for type B aortic dissection with two entry tears. [ABSTRACT FROM AUTHOR]

Plant-endophytic microbes affect plant growth, development, nutrition, and resistance to pathogens. However, how endophytic microbial communities change in different strawberry plant compartments after Fusarium pathogen infection has remained elusive. In this study, 16S and internal transcribed spacer rRNA amplicon sequencing were used to systematically investigate changes in the bacterial and fungal diversity and composition in the endophytic compartments (roots, stems, and leaves) of healthy strawberries and strawberries with Fusarium wilt, respectively. The analysis of the diversity, structure, and composition of the bacterial and fungal communities revealed a strong effect of pathogen invasion on the endophytic communities. The bacterial and fungal community diversity was lower in the Fusarium-infected endophytic compartments than in the healthy samples. The relative abundance of certain bacterial and fungal genera also changed after Fusarium wilt infection. The relative abundance of the beneficial bacterial genera Bacillus, Bradyrhizobium, Methylophilus, Sphingobium, Lactobacillus, and Streptomyces, as well as fungal genera Acremonium, Penicillium, Talaromyces, and Trichoderma, were higher in the healthy samples than in the Fusarium wilt samples. The relative abundance of Fusarium in the infected samples was significantly higher than that in the healthy samples, consistent with the field observations and culture isolation results for strawberry wilt. Our findings provide a theoretical basis for the isolation, identification, and control of strawberry wilt disease. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to develop predictive models for postoperative estimated glomerular filtration rate (eGFR) based on the split glomerular filtration rate measured by radionuclide (rGFR), as choosing radical nephrectomy (RN) or partial nephrectomy (PN) for complex renal masses requires accurate prediction of postoperative eGFR. Methods: Patients who underwent RN or PN for a single renal mass at Xijing Hospital between 2008 and 2022 were retrospectively included. Preoperative split rGFR was evaluated using technetium-99 m-diethylenetriaminepentaacetic acid (Tc-99 m DTPA) renal dynamic imaging, and the postoperative short-term (< 7 days) and long-term (3 months to 5 years) eGFRs were assessed. Linear mixed-effect models were used to predict eGFRs, with marginal R2 reflecting predictive ability. Results: After excluding patients with missing follow-up eGFRs, the data of 2251 (RN: 1286, PN: 965) and 2447 (RN: 1417, PN: 1030) patients were respectively included in the long-term and short-term models. Two models were established to predict long-term eGFRs after RN (marginal R2 = 0.554) and PN (marginal R2 = 0.630), respectively. Two other models were established to predict short-term eGFRs after RN (marginal R2 = 0.692) and PN (marginal R2 = 0.656), respectively. In terms of long-term eGFRs, laparoscopic and robotic surgery were superior to open surgery in both PN and RN. Conclusions: We developed novel tools for predicting short-term and long-term eGFRs after RN and PN based on split rGFR that can help in preoperative decision-making. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs) have been extensively studied, and their crucial roles in adipogenesis, lipid metabolism, and gene expression have been revealed. However, the exact regulatory or other mechanisms by which lncRNAs influence the functioning of mesenteric adipose tissue (MAT) remain largely unknown. In this paper, we report the identification of a new lncRNA, named G8110, from the MAT of Bama pigs. The coordinated expression levels of lncRNA G8110 and NFE2L1 were significantly decreased in the MAT of obese Bama pigs compared with those in the MAT of lean pigs. Using a bone mesenchymal stem cell adipogenic differentiation model, we found that lncRNA G8110 played a role in adipocyte differentiation by positively regulating NFE2L1. We also found that lncRNA G8110 inhibited the formation of intracellular lipid synthesis, promoted lipid metabolism, and inhibited the expression of inflammatory cytokines. Our findings regarding lipid synthesis may further promote the role of lncRNAs in driving adipose tissue remodeling and maintaining metabolic health. [ABSTRACT FROM AUTHOR]

Recently, triple-network convergence systems (TNCS) have emerged from the deep integration of the power grid, transportation networks, and information networks. Fault recovery research in the TNCS is important since this system's complexity and interactivity can expand the fault's scale and increase the fault's impact. Currently, fault recovery focuses primarily on single power grids and cyber–physical systems, but there are certain shortcomings, such as ignoring uncertainties, including generator start-up failures and the occurrence of new faults during recovery, energy supply–demand imbalances leading to system security issues, and communication delays caused by network attacks. In this study, we propose a recovery method based on the improved twin-delayed deep deterministic algorithm (TD3), factoring in the shortcomings of the existing research. Specifically, we establish a TNCS model to analyze interaction mechanisms and design a state matrix to represent the uncertainty changes in the TNCS, a negative reward to reflect the impact of unit start-up failures, a special reward to reflect the impact of communication delay, and an improved actor network update mechanism. Experimental results show that our method obtains the optimal recovery decisions, maximizes restoration benefits in power grid failure scenarios, and demonstrates a strong resilience against communication delays caused by DoS attacks. [ABSTRACT FROM AUTHOR]

In the intensive care unit, sepsis is a prevalent clinical syndrome (i.e. the final pathway to death from most infections). Peripheral blood gene expression profiling is becoming more and more accepted as a potential diagnostic or prognostic tool. This work aimed to recognize genes related to sepsis, providing potential translational therapeutic targets. RNA sequencing was performed on peripheral blood mononuclear cells from 20 healthy control subjects and 51 sepsis patients. Weighted gene coexpression network analysis was employed to pick out sepsis-related and immunocyte-related gene modules. Genes in the yellow module are primarily involved in excessive inflammation and immune suppression. STRING and Cytoscape were combined to identify ACTG1 and IQGAP1 as hub genes with highest connective degree, and prognostic predication value of ACTG1 was confirmed. Both univariate and multivariate logistic regression analyses were carried out. ACTG1 messenger RNA expression was increased in animal and in cell-related sepsis models. Small interfering RNA revealed decreasing ACTG1 can reduce the in vitro sepsis model apoptosis. We have authenticated ACTG1 as a reliable signature of a poor outcome of sepsis and promising therapeutic targets for sepsis. [ABSTRACT FROM AUTHOR]

In search, exploration, and reconnaissance operations of autonomous ground vehicles, an image recognition capability is needed for specifically classifying targeted objects (relevant classes) and at the same time identifying as unknown (irrelevant and unseen) objects that do not belong to any known classes, as opposed to falsely classifying them in one of the relevant classes. This paper integrates an unsupervised learning feature extraction framework based on the Instance Discrimination method with an Open-Set Low-Shot (IDLS) classifier for creating the desired new capability. Unlabeled images from the vehicle's operating environment are used for training the feature extractor while a modest number (less than 40) images for each relevant class and unlabeled irrelevant images are used for training the Open-Set Low-Shot (OSLS) classifier in a manner that enables recognition of images unseen during training as irrelevant. The value and the accuracy of the new IDLS approach are demonstrated through a thorough comparison with alternative unsupervised and fully supervised methods. [ABSTRACT FROM AUTHOR]

Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis. Latterly, significant attention has been given to HAD methods based on Autoencoders (AE). Nevertheless, due to a lack of prior information, transferring of modeling capacity, and the "curse of dimensionality", AE-based detectors still have limited performance. To address the drawbacks, we propose a Multi-Prior Graph Autoencoder (MPGAE) with ranking-based band selection for HAD. There are three main components: the ranking-based band selection component, the adaptive salient weight component, and the graph autoencoder. First, the ranking-based band selection component removes redundant spectral channels by ranking the bands by employing piecewise-smooth first. Then, the adaptive salient weight component adjusts the reconstruction ability of the AE based on the salient prior, by calculating spectral-spatial features of the local context and the multivariate normal distribution of backgrounds. Finally, to preserve the geometric structure in the latent space, the graph autoencoder detects anomalies by obtaining reconstruction errors with a superpixel segmentation-based graph regularization. In particular, the loss function utilizes ℓ 2 , 1 - norm and adaptive salient weight to enhance the capacity of modeling anomaly patterns. Experimental results demonstrate that the proposed MPGAE effectively outperforms other state-of-the-art HAD detectors. [ABSTRACT FROM AUTHOR]

With the continuous development of surface observation methods and technologies, we can acquire multiple sources of data more effectively in the same geographic area. The quality and availability of these data have also significantly improved. Consequently, how to better utilize multi-source data to represent ground information has become an important research question in the field of geoscience. In this paper, a novel model called multi-modal transformer cascaded fusion net (MMTCFN) is proposed for fusion and classification of multi-modal remote sensing data, Hyperspectral Imagery (HSI) and LiDAR data. Feature fusion and feature extraction are the two stages of the model. First, in the feature extraction stage, a three-branch cascaded Convolutional Neural Network (CNN) framework is employed to fully leverage the advantages of convolutional operators in extracting shallow-level local features. Based on this, we generated multi-modal long-range integrated deep features utilizing the transformer-based vectorized pixel group transformer (VPGT) module during the feature fusion stage. In the VPGT block, we designed a vectorized pixel group embedding that preserves the global features extracted from the three branches in a non-overlapping multi-space manner. Moreover, we introduce the DropKey mechanism into the multi-head self-attention (MHSA) to alleviate overfitting caused by insufficient training samples. Finally, we employ a probabilistic decision fusion strategy to integrate multiple class estimations, assigning a specific category to each pixel. This model was experimented on three HSI-LiDAR datasets with balanced and unbalanced training samples. The proposed model outperforms the other seven SOTA approaches in terms of OA performance, proving the superiority of MMTCFN for the HSI-LiDAR classification task. [ABSTRACT FROM AUTHOR]

This paper analyzes the performance requirements that need to be met by a clock generator applied to a low-temperature quantum computer and analyzes the negative effects on the clock generator circuit under low-temperature conditions. In order to meet the performance requirements proposed in this paper and suppress the negative effects brought about by the low temperature, a clock generator for ultra-low-temperature quantum computing is designed. This clock generator is designed by using F-CLASS Voltage Controlled Oscillator (VCO), power filter, tail resistor, differential charge pump, and other techniques. And the noise characteristics of the clock generator are analyzed by Impulse Sensitive Function (ISF) and simulation results. After simulation tests, the average power consumption of the clock generator designed in this paper is 7 mW, the phase noise is −121 dBc/Hz@1 MHz, and the jitter is 62 fs. The performance of the clock generator meets the performance requirements proposed in this paper, and the reduction in the corner frequency proves that the circuit will have better performance at low temperatures. [ABSTRACT FROM AUTHOR]

Purpose: To screen out potential prognostic biomarkers for HBV-related acute-on-chronic liver failure (HBV-ACLF).Patients and Methods: Peripheral blood samples of HBV-ACLF patients (n=56) and normal controls (n=15) from the Affiliated Hospital of Southwest Medical University from January 2021 to April 2022 were collected, 5 normal patients and 10 patients with ACLF were randomly selected for Data independent acquisition (DIA) mass spectrometry analysis, and the potential core proteins were screened out via bioinformatics. All samples were validated by Enzyme linked immunosorbent assays (ELISA) technology, and the survival curve was constructed based on the patient's 90-day survival time.Results: A total of 247 differentially expressed proteins (DEPs) were screened, of which 148 were upregulated and 99 were down-regulated. The DEPs were mainly enriched in high-density lipoprotein particle remodeling, coagulation, and hemostasis and participated in signaling pathways such as cholesterol metabolism, coagulation cascades, and PPAR signaling pathway. Finally, bikunin was selected for further study and validated via the ELISA, compared with the normal group, bikunin was poorly expressed in the HBV-ACLF group, the difference was statistically significant (P < 0.0001), the area under the curve (AUC) for Receiver operating characteristic (ROC) analysis was 0.917. Furthermore, compared with the non-survival group, bikunin was highly expressed in the HBV-ACLF survival group, the difference was statistically significant (P=0.0015), and the survival curve showed a positive correlation with patient survival (P=0.0063).Conclusion: The level of plasma bikunin in HBV-ACLF is down-regulated, which is positively correlated with the survival of the patients with HBV-ACLF, and is expected to become a new prognostic biomarker. [ABSTRACT FROM AUTHOR]

The North Konawe region in Indonesia, known for its lateritic nickel (Ni) deposits, holds significant potential for obtaining Ni resources. However, the complex topographic conditions of this area pose challenges. Exploring the application of remote sensing technology to reveal the spectral response mechanism of Ni grade from high-precision multispectral data and inversion of Ni grade represents a novel direction in future Ni resource exploration. Traditional remote sensing inversion methods solely consider the spectral characteristics of sample data and ignore vital geospatial location information. As a result, efficiently obtaining regional details of target substance content over large areas has become challenging. The introduction of the geographically weighted regression (GWR) method offers an opportunity for fine-grained Ni grade inversion based on remote sensing. This study focused on the E and K blocks within the lateritic Ni mining area in North Konawe. Through utilizing the WordView-3 multispectral data which exhibits immense potential in quantitative remote sensing inversion studies, GWR was employed to integrate spectral features and spatial information. The goal was to reveal the correlation between multispectral remote sensing data and Ni grade. The obtained results were then compared and analyzed with multiple linear regression (MLR) and back propagation neural network (BPNN) models. The findings revealed that GWR achieved the highest coefficient of determination R2 of 0.96, surpassing MLR and BPNN values of 0.05 and 0.17, respectively. Additionally, GWR exhibited the lowest root mean square error of 0.04, which was lower than those of MLR and BPNN with the values of 0.25 and 0.23, respectively. These results confirmed the enhanced stability and accuracy of the GWR method compared to MLR and BPNN. Furthermore, GWR effectively mapped the spatial distribution trends of Ni grades in the study area, providing evidence of the method's effectiveness in Ni grade inversion. The study also delved into the inversion effect of the GWR method in areas with varying weathering crust thickness and vegetation cover. The research revealed that higher values of weathering crust thickness negatively impacted the inversion effect. However, the influence mechanism of vegetation cover on Ni grade inversion necessitated further investigation. These results served as a significant demonstration of the remote sensing inversion of mineral resource grades in similar areas. They provided valuable insights for future exploration and decision-making processes. [ABSTRACT FROM AUTHOR]

As the PCIe 6.0 specification places higher requirements on signal integrity and transmission latency, it becomes especially important to improve signal transmission performance at the physical layer of the transceiver interface. Retimer circuits are a key component of high-speed serial interfaces, and their delay and jitter size directly affect the overall performance of PCIe. For the typical retimer circuit with large-latency and low-jitter performance, this paper proposes a low-latency and low-jitter Retimer circuit based on CDR + PLL architecture for PCIe 6.0, using a jitter-canceling filter circuit to eliminate the frequency difference between the retiming clock and data, reduce the retiming clock jitter, and improve the quality of Retimer output data. The data are sampled using the retiming clock and then output, avoiding the problem of large penetration latency of typical retimer circuits. The circuit is designed using the CMOS 28 nm process. Simulation results show that when 112 Gbps PAM4 data are input to the retimer circuit, the Retimer penetration latency is 27.3 ps, which is 83.5% lower than the typical Retimer structure; the output jitter data are 741 fs, a 31.4% reduction compared to the typical retimer structure. [ABSTRACT FROM AUTHOR]

Single metal atom isolated in nitrogen‐doped carbon materials (MNC) are effective electrocatalysts for oxygen reduction reaction (ORR), which produces H2O2 or H2O via 2‐electron or 4‐electron process. However, most of MNC catalysts can only present high selectivity for one product, and the selectivity is usually regulated by complicated structure design. Herein, a carbon black‐supported CoNC catalyst (CB@CoNC) is synthesized. Tunable 2‐electron/4‐electron behavior is realized on CB@Co‐N‐C by utilizing its H2O2 yield dependence on electrolyte pH and catalyst loading. In acidic media with low catalyst loading, CB@CoNC presents excellent mass activity and high selectivity for H2O2 production. In flow cell with gas diffusion electrode, a H2O2 production rate of 5.04 mol h−1 g−1 is achieved by CB@CoNC on electrolyte circulation mode, and a long‐term H2O2 production of 200 h is demonstrated on electrolyte non‐circulation mode. Meanwhile, CB@CoNC exhibits a dominant 4‐electron ORR pathway with high activity and durability in pH neutral media with high catalyst loading. The microbial fuel cell using CB@CoNC as the cathode catalyst shows a peak power density close to that of benchmark Pt/C catalyst. [ABSTRACT FROM AUTHOR]

Micro-LED display technology is considered to be the next generation of display technology, which has the characteristics of high miniaturization, thin film and integration, as well as the advantages of high brightness, high contrast, fast response speed and long service life. However, in the development of Micro-LED display technology, there are still some technical and cost problems to be solved. This paper focuses on the key technologies involved in Micro-LED display technology, such as chip technology, mass transfer, full-color display, bonding and driving technology, the research history and frontier progress of these technologies are reviewed in detail. For chip epitaxy technology, the wavelength uniformity, current density and defect control are emphasized; for chip process, the two main chip structures and the challenges brought by miniaturization are discussed emphatically; for chip integration, full-color display, backplane bonding and driving, several mainstream technical schemes are summarized in turn. Finally, the chip detection and repair technologies and commercial application are introduced. [ABSTRACT FROM AUTHOR]

Accurate soil organic carbon (SOC) maps are helpful for guiding forestry production and management. Different ecological landscape areas within a large region may have different soil–landscape relationships, so models specifically for these areas may capture these relationships more accurately than the global model for the entire study area. The aim of this study was to investigate the role of zonal modelling in predicting forest SOC and to produce highly accurate forest SOC distribution maps. The prediction objects were SOC at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). First, the forest type map and soil texture class map were used to divide the relative homogeneous regions in Shaoguan City, Guangdong Province, China. Second, seven terrain variables derived from a 12.5-m digital elevation model (DEM) and five vegetation variables generated from 10-m Sentinel-2 remote sensing images were used as predictors to develop regional artificial neural network (ANN) models for each homogeneous region, as well as a global ANN model for the entire study area (1000 sample points). Finally, 10-fold cross-validation was used to assess the ANN prediction model performance, and independent validation was used to evaluate the produced forest SOC prediction maps (194 additional samples). The cross-validation results showed that the accuracies of the regional models were better than that of the global model. Independent validation results also showed that the precision (R2) of 0- to 100-cm forest SOC maps generated by forest type modelling had an improvement of 0.05–0.15, and that by soil texture class modelling had an improvement of 0.07–0.13 compared to the map generated by the global model. In conclusion, delineating relatively homogeneous regions via simple methods can improve prediction accuracy when undertaking soil predictions over large areas, especially with complex forest landscapes. In addition, SOC in the study area is generally more abundant in broadleaf forest and clay areas, with overall levels decreasing with soil depth. Accurate SOC distribution information can provide references for fertilization and planting. Plants with particularly high soil fertility requirements may perhaps be planted in broadleaf forests or clay areas, and plants with particularly developed roots may require furrow application of a small amount of SOC. [ABSTRACT FROM AUTHOR]

Background: Cattle (Bos taurus) are a major large livestock, however, compared with other species, the transcriptional specificity of bovine oocyte development has not been emphasised. Results: To reveal the unique transcriptional signatures of bovine oocyte development, we used integrated multispecies comparative analysis and weighted gene co-expression network analysis (WGCNA) to perform bioinformatic analysis of the germinal follicle (GV) and second meiosis (MII) gene expression profile from cattle, sheep, pigs and mice. We found that the expression levels of most genes were down-regulated from GV to MII in all species. Next, the multispecies comparative analysis showed more genes involved in the regulation of cAMP signalling during bovine oocyte development. Moreover, the green module identified by WGCNA was closely related to bovine oocyte development. Finally, integrated multispecies comparative analysis and WGCNA picked up 61 bovine-specific signature genes that participate in metabolic regulation and steroid hormone biosynthesis. Conclusion: In a short, this study provides new insights into the regulation of cattle oocyte development from a cross-species comparison. [ABSTRACT FROM AUTHOR]

Hypoxic-ischemic white matter injury (WMI) pathogenesis in preterm infants is not well established, and iron-related proteins in the brain may play an important role in imbalanced iron metabolism. We aimed to investigate the iron-related protein changes in neonatal rats after hypoxia-ischemia (HI), clarify the role of iron-related proteins in hypoxic-ischemic WMI, and potentially provide a new target for the clinical treatment of hypoxic-ischemic WMI in preterm infants. We adopted a WMI animal model of bilateral common carotid artery electrocoagulation combined with hypoxia in neonatal 3-day-old Sprague-Dawley rats. We observed basic myelin protein (MBP) and iron-related protein expression in the brain (ferritin, transferrin receptor [TfR], and membrane iron transporter 1 [FPN1]) via Western blot and double immunofluorescence staining. The expression of MBP in the WMI group was significantly downregulated on postoperative days (PODs) 14, 28, and 56. Ferritin levels were significantly increased on PODs 3, 7, 14, and 28 and were most significant on POD 28, returning to the sham group level on POD 56. FPN1 levels were significantly increased on PODs 7, 28, and 56 and were still higher than those in the sham group on POD 56. TfR expression was significantly upregulated on PODs 1, 7, and 28 and returned to the sham group level on POD 56. Immunofluorescence staining showed that ferritin, TfR, and FPN1 were expressed in neurons, blood vessels, and oligodendrocytes in the cortex and corpus callosum on POD 28. Compared with the sham group, the immune-positive markers of three proteins in the WMI group were significantly increased. The expression of iron-related proteins in the brain (ferritin, FPN1, and TfR) showed spatiotemporal dynamic changes and may play an important role in hypoxic-ischemic WMI. [ABSTRACT FROM AUTHOR]

In the field of remote sens., change detection is an important monitoring technology. However, effectively extracting the change feature is still a challenge, especially with an unsupervised method. To solve this problem, we proposed an unsupervised transformer boundary autoencoder network (UTBANet) in this paper. UTBANet consists of a transformer structure and spectral attention in the encoder part. In addition to reconstructing hyperspectral images, UTBANet also adds a decoder branch for reconstructing edge information. The designed encoder module is used to extract features. First, the transformer structure is used for extracting the global features. Then, spectral attention can find important feature maps and reduce feature redundancy. Furthermore, UTBANet reconstructs the hyperspectral image and boundary information simultaneously through two decoders, which can improve the ability of the encoder to extract edge features. Our experiments demonstrate that the proposed structure significantly improves the performance of change detection. Moreover, comparative experiments show that our method is superior to most existing unsupervised methods. [ABSTRACT FROM AUTHOR]

Horizontal wells with a slim hole (HW‐SH), characterized by high productivity, high environmental protection, and low cost are gradually being introduced into oil and gas extraction processes in high‐temperature and high‐pressure formations. However, the HW‐SH annulus is narrow, and the distributions of temperature and pressure in the wellbore are very different from those of conventional horizontal wells. Furthermore, the mud properties change under the influence of high temperature and pressure. In this study, a numerical model of the transient temperature‐pressure coupling field in an HW‐SH wellbore was developed based on high‐temperature and high‐pressure performance tests of mud, combined with the characteristics of a narrow annulus in HW‐SH. Subsequently, the effects of the ground temperature gradient, displacement, drill pipe speed, annulus size, and joint size on the temperature‐pressure coupling field were analyzed. Measured data from the HW‐SH drilled in the Mahu field in China were used to validate the developed model. The results showed that the ground temperature had a significant effect on the mud density and rheological properties. As the temperature increased, the mud density decreased, and its rheological properties improved. Although pressure also has a large effect on density, it has a negligible effect on the rheological properties of the mud. The coupled temperature‐pressure field model for HW‐SH considering the effects of the temperature and narrow annulus can predict the annulus temperature and pressure with an accuracy of up to 97%. The ground temperature gradient has the greatest influence on the HW‐SH annular temperature, larger than that of the displacement. In addition, the annular size, rotation speed of the drill pipe, and joint size have almost no influence on the annular temperature. Compared to those of conventional horizontal wells, the rotational speed, annulus size, and joint size of the HW‐SH have a greater impact on the annulus pressure. [ABSTRACT FROM AUTHOR]

Due to optical noise, electrical noise, and compression error, data hyperspectral remote sensing equipment is inevitably contaminated by various noises, which seriously affect the applications of hyperspectral data. Therefore, it is of great significance to enhance hyperspectral imaging data quality. To guarantee the spectral accuracy during data processing, band-wise algorithms are not suitable for hyperspectral data. This paper proposes a quality enhancement algorithm based on texture search and histogram redistribution combined with denoising and contrast enhancement. Firstly, a texture-based search algorithm is proposed to improve the accuracy of denoising by improving the sparsity of 4D block matching clustering. Then, histogram redistribution and Poisson fusion are used to enhance spatial contrast while preserving spectral information. Synthesized noising data from public hyperspectral datasets are used to quantitatively evaluate the proposed algorithm, and multiple criteria are used to analyze the experimental results. At the same time, classification tasks were used to verify the quality of the enhanced data. The results show that the proposed algorithm is satisfactory for hyperspectral data quality improvement. [ABSTRACT FROM AUTHOR]

Image style transfer has become a key technique in modern photo-editing applications. Although significant progress has been made to blend content from one image with style from another image, the synthesized image may have a hallucinatory effect when the texture from the style image is rich when processing high-resolution image style transfer tasks. In this paper, we propose a novel attention mechanism, named compositional attention, to design a compositional transformer-based autoencoder (CTA) to solve this above-mentioned issue. With the support from this module, our model is capable of generating high-quality images when transferring from texture-riched style images to content images with semantics. Additionally, we embed region-based consistency terms in our loss function for ensuring internal structure semantic preservation in our synthesized image. Moreover, information theory-based CTA is discussed and Kullback–Leibler divergence loss is introduced to preserve more brightness information for photo-realistic style transfer. Extensive experimental results based on three benchmark datasets, namely Churches, Flickr Landscapes, and Flickr Faces HQ, confirmed excellent performance when compared to several state-of-the-art methods. Based on a user study assessment, the majority number of users, ranging from 61% to 66%, gave high scores on the transfer effects of our method compared to 9% users who supported the second best method. Further, for the questions of realism and style transfer quality, we achieved the best score, i.e., an average of 4.5 out of 5 compared to other style transfer methods. [ABSTRACT FROM AUTHOR]

Background A major goal of coronavirus disease 2019 (COVID-19) vaccination is to prevent severe outcomes (hospitalizations and deaths). We estimated the effectiveness of messenger RNA (mRNA) and ChAdOx1 COVID-19 vaccines against severe outcomes in 4 Canadian provinces between December 2020 and September 2021. Methods We conducted this multiprovincial, retrospective, test-negative study among community-dwelling adults aged ≥18 years in Ontario, Quebec, British Columbia, and Manitoba using linked provincial databases and a common study protocol. Multivariable logistic regression was used to estimate province-specific vaccine effectiveness against COVID-19 hospitalization and/or death. Estimates were pooled using random-effects models. Results We included 2 508 296 tested participants, with 31 776 COVID-19 hospitalizations and 5842 deaths. Vaccine effectiveness was 83% after a first dose and 98% after a second dose against both hospitalization and death (separately). Against severe outcomes, effectiveness was 87% (95% confidence interval [CI], 71%–94%) ≥84 days after a first dose of mRNA vaccine, increasing to 98% (95% CI, 96%–99%) ≥112 days after a second dose. Vaccine effectiveness against severe outcomes for ChAdOx1 was 88% (95% CI, 75%–94%) ≥56 days after a first dose, increasing to 97% (95% CI, 91%–99%) ≥56 days after a second dose. Lower 1-dose effectiveness was observed for adults aged ≥80 years and those with comorbidities, but effectiveness became comparable after a second dose. Two doses of vaccines provided very high protection for both homologous and heterologous schedules and against Alpha, Gamma, and Delta variants. Conclusions Two doses of mRNA or ChAdOx1 vaccine provide excellent protection against severe outcomes. [ABSTRACT FROM AUTHOR]

Compared with conventional drilling methods, the method of drilling horizontal wells with a slim hole (HW‐SH) has advantages, such as high productivity, environment‐friendliness, and low costs. However, during the drilling process, the narrow annulus may become blocked, which may lead to an increase in the annulus pressure if the wellbore is not clean. This often leads to serious drilling accidents, such as sticking and leaking formations. Many models have been used to study the cutting transport law in an annulus, but the influence of drill‐pipe rotation on cutting transport is often ignored and weakened in these models. None of the existing models can effectively simulate the cutting transport law of an entire well section. Therefore, using these models to predict the cutting transport law in the annulus of the HW‐SH will cause significant errors. In this study, the authors first establish a pipeline model of the same size as the on‐site annulus using computational fluid dynamics (CFD). Subsequently, a one‐dimensional (1D) two‐layer model that uses three correction factors to express the influence of drill‐pipe rotation on cutting transport and annulus pressure loss is developed. Finally, the parameters in the 1D model are calibrated using the CFD simulation results to accurately predict the cutting transport behavior and annular pressure loss. From the calculation results of the annulus cutting quality, annulus section‐cutting concentration, and annulus pressure drop, it was observed that the two algorithms proposed in this study have a high degree of coincidence, thereby confirming the reliability of the model. The 1D model provides close to three‐dimensional (3D) accuracy at a much shorter central processing unit time than the 3D CFD models. In addition, the accuracy of the developed model was verified using the results of an indoor pipeline two‐phase flow experiment conducted at the University of Tulsa, without considering the rotation of the drill pipe. [ABSTRACT FROM AUTHOR]

Under the condition of vehicle-to-vehicle communication, different information transmission mechanisms may have different effects on the cooperative performance of road traffic. In this paper, a new car-following model with consideration of the iterative traffic information transmission mechanism of vehicle-to-vehicle communication is proposed. The influence of the iterative traffic information transmission mechanism on the cooperative car-following behavior has been revealed. It is proved that based on the iterative traffic information transmission of vehicle-to-vehicle communication, the information of the two nearest preceding vehicles ahead of the ego vehicle has the most obvious role in stabilizing traffic flow. Also, the information of the third or fourth vehicles in front of the ego vehicle has a certain stabilizing effect, but the traffic information of the fifth or further preceding vehicles in front of the ego vehicle has little effect in enhancing traffic stability. This result provides a reference vehicle number for the cooperative car-following performance under the iterative traffic information transmission mechanism of vehicle-to-vehicle communication. [ABSTRACT FROM AUTHOR]

As a means of applying cold atmospheric plasma (CAP), plasma-activated solutions have received extensive attention for their excellent anticancer effects. However, previous studies have focused mainly on the local injection of plasma-activated solutions for cancer treatment while ignoring the anticancer effects of intraluminal plasma-activated solution perfusion. In this paper, the anticancer effects of flowing plasma-activated saline (PAS) on human bladder cancer cell lines (J82) were studied in vitro in a microfluidic system. It is noteworthy that flowing PAS can kill cancer cells more efficiently than PAS immersion. Further experimental results show that the anticancer effects of PAS on J82 cells in the microcavity are affected by plasma irradiation time, perfusion time and liquid flow rate. Our research suggests that PAS perfusion therapy can become a controllable alternative therapy pathway in the future clinical treatment of intracavitary tumors. [ABSTRACT FROM AUTHOR]

As a new generation of information technology, blockchain plays an important role in business and industrial innovation. The employment of blockchain technologies in industry has increased transparency, security and traceability, improved efficiency, and reduced costs of production activities. Many studies on blockchain technology-enabled system construction and performance optimization in Industry 4.0 have been carried out. However, blockchain technology and smart manufacturing have been individually researched in academia and industry, according to the literature. This survey aims to summarize the existing research to provide theoretical foundations for applying blockchain technology to smart manufacturing, thus creating a more reliable and authentic smart manufacturing system. In this regard, the literature related to four types of critical issues in smart manufacturing is introduced: data security, data sharing, trust mechanisms and system coordination issues. The corresponding blockchain solutions were reviewed and analyzed. Based on the insights obtained from the above analysis, a reference framework for blockchain technology-enabled smart manufacturing systems is put forward. The challenges and future research directions are also discussed to provide potential guides for achieving better utilization of this technology in smart manufacturing. [ABSTRACT FROM AUTHOR]

Herein, a small series of 3‐pyrrolin‐2‐ones was efficiently synthesized through a three‐step Ugi cascade sequence. This method features readily available substrates, simple aqueous workup procedures and good yields, dramatically improving generality of reaction. Importantly, the newly product N‐benzyl‐2‐(3‐(4‐chlorophenyl)‐4‐methyl‐2‐oxo‐2,5‐dihydro‐1H‐pyrrol‐1‐yl)‐2‐phen‐ylacetamide exhibited potent anti‐proliferation in prostate cancer cell line through G1/S cell cycle arrest and targeted in PI3K/AKT/TSC2 signal pathway. [ABSTRACT FROM AUTHOR]