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During the cold-start process of a PEMFC, the supply of air and hydrogen in the gas supply system has a great influence on the cold-start performance. The cold-start of a PEMFC is a complex nonlinear coupling process, and the traditional control strategy is not sensitive to the real-time characteristics of the system. Inspired by the strong perception and decision-making abilities of deep reinforcement learning, this paper proposes a cold-start control strategy for a gas supply system based on the MADDPG algorithm, and designs an air supply controller and a hydrogen supply controller based on this algorithm. The proposed strategy can optimize the control parameters of the gas supply system in real time according to the temperature rise rate of the stack during the cold-start process, the fluctuation of the OER, and the voltage output characteristics. After the strategy is trained offline according to the designed reward function, the detailed in-loop simulation experiment results are given and compared with the traditional control strategy for the gas supply system. From the results, it can be seen that the proposed MADDPG control strategy has a more effective coordination control effect. [ABSTRACT FROM AUTHOR]

In the process of stereo super-resolution reconstruction, in addition to the richness of the extracted feature information directly affecting the texture details of the reconstructed image, the texture details of the corresponding pixels between stereo image pairs also have an important impact on the reconstruction accuracy in the process of network learning. Therefore, aiming at the information interaction and stereo consistency of stereo image pairs, a cross-view attention interaction fusion stereo super-resolution algorithm is proposed. Firstly, based on parallax attention mechanism and triple attention mechanism, an attention stereo fusion module is constructed. The attention stereo fusion module is inserted between different levels of two single image super-resolution network branches, and the attention weight is calculated through the cross dimensional interaction of the three branches. It makes full use of the ability of single image super-resolution network to extract single view information and further maintaining the stereo consistency between stereo image pairs. Then, an enhanced cross-view interaction strategy including three fusion methods is proposed. Specifically, the vertical sparse fusion method is used to integrate the interior view information of different levels in the two single image super-resolution sub branches, the horizontal dense fusion method is used to connect the adjacent attention stereo fusion modules and the constraint between stereo image consistency is further strengthened in combination with the feature fusion method. Finally, the experimental results on Flickr 1024, Middlebury and KITTI benchmark datasets show that the proposed algorithm is superior to the existing stereo image super-resolution methods in quantitative measurement and qualitative visual quality while maintaining the stereo consistency of image pairs. [ABSTRACT FROM AUTHOR]

[3 + 2] Cycloaddition reactions of heteroaromatic N-ylides with electron-deficient olefins have been developed. The heteroaromatic N-ylides, in situ generated from N-phenacylbenzothiazolium bromides, can smoothly react with maleimides under very mild conditions, affording fused polycyclic octahydropyrrolo[3,4-c]pyrroles in good-to-excellent isolated yields. This reaction concept could also be extended to 3-trifluoroethylidene oxindoles and benzylidenemalononitriles as electron-deficient olefins for accessing highly functionalized polyheterocyclic compounds. A gram-scale experiment was also carried out to verify the practicability of the methodology. [ABSTRACT FROM AUTHOR]

Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network has limitations such as poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced the Ghost module into the YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance of unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention modules (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) is set to 0.5, and the mAP is set from 0.5 to 0.95 of our model and can reach 99.4% and 91.7%; the parameters and model size were reduced to 3,807,372 and 8.79 M, which can be easily deployed to embedded devices such as UAVs. Moreover, the speed of detection can reach 10.9 ms/image, which can meet the real-time detection requirement. [ABSTRACT FROM AUTHOR]

This research proposes an apple quality grading approach based on multi-dimensional view information processing using YOLOv5s network as the framework to rapidly and accurately perform the apple quality grading task. The Retinex algorithm is employed initially to finish picture improvement. Then, the YOLOv5s model, which is improved by adding ODConv dynamic convolution and GSConv convolution and VoVGSCSP lightweight backbone, is used to simultaneously complete the detection of apple surface defects and the identification and screening of fruit stem information, retaining only the side information of the apple multi-view. After that, the YOLOv5s network model-based approach for assessing apple quality is then developed. The introduction of the Swin Transformer module to the Resnet18 backbone increases the grading accuracy and brings the judgment closer to the global optimal solution. In this study, datasets were made using a total of 1244 apple images, each containing 8 to 10 apples. Training sets and test sets were randomly created and divided into 3:1. The experimental results demonstrated that in the multi-dimensional view information processing, the recognition accuracy of the designed fruit stem and surface defect recognition model reached 96.56% after 150 iteration training, the loss function value decreased to 0.03, the model parameter was only 6.78 M, and the detection rate was 32 frames/s. After 150 iteration training, the average grading accuracy of the quality grading model reached 94.46%, the loss function value decreased to 0.05, and the model parameter was only 3.78 M. The test findings indicate that the proposed strategy has a good application prospect in the apple grading task. [ABSTRACT FROM AUTHOR]

The change in raindrop spectrum characteristics is an important factor affecting the accuracy of estimations of precipitation. The in-depth study of raindrop spectrum characteristics is of great interest for understanding precipitation process and improving quantitative radar precipitation estimation. In this paper, the raindrop size distributions at Longli (57913), Puding (57808) and Luodian (57916) stations in Guizhou were analyzed from the perspective of precipitation microphysical characteristics. The results showed that the raindrop size distribution was different among different regions. The correlation coefficients of the mass-weighted average diameter for the rain intensities at these three stations were 46.89%, 49.51%, and 47.03%, respectively, which were slightly lower than the normal correlation coefficients of the average volume diameter for the rain intensities: 67.80%, 71.28%, and 71.46%, respectively. Based on the data from the Guiyang weather radar, raindrop spectrometer, and automatic rain gauge, the dynamic Z-I relationship method and the LSTM neural network method were used to estimate precipitation. The correlation coefficients of the dynamic Z-I relationship method and the LSTM neural network method at the three stations studied were 0.8432, 0.7763, and 0.8658 and 0.8745, 0.9125, and 0.8676, respectively. Regarding the process of stratiform cloud precipitation, the correlation coefficients of the dynamic Z-I relationship method and LSTM neural network method at the three stations were 0.6933, 0.0902, and 0.1409 and 0.7114, 0.4984, and 0.4902, respectively. In the estimation of cumulative precipitation for 45 days from 1 July to 16 August 2020, the relative errors of the neural network estimation at the three stations were −4.25%, −11.35%, and −8.68% and the relative errors of the dynamic Z-I relationship estimation were −2.68%, −7.41%, and −21.23%, respectively. The final relative error of the neural network was slightly worse than that of the dynamic Z-I relationship in the cumulative precipitation estimations of Longli station and Puding station, but the overall correlation coefficients of the LSTM neural network method were better than those of the dynamic Z-I relationship method. [ABSTRACT FROM AUTHOR]

In this study, the combustion characteristics of pine needles, pine needle humus, and the co-combustion of these two types of biomass with coal were compared. In addition, the optimization of the combustion performance of coal/humus was assisted through the following studies: (i) the combustion performance of coal/pine needle humus was studied under four different oxygen concentrations (N2, 10%O2/90%N2, 20%O2/80%N2, and 40%O2/60%N2); (ii) the synergistic effect between the humus and coal during combustion was also investigated by adjusting the blending ratio and oxygen content; (iii) the mechanisms of the optimized combustion processes were expounded by kinetics and thermodynamics discussion. The results demonstrated that the combustion characteristics of the coal/humus blends were found to be higher than those of the coal/pine needle blends. The coupling interactions of the oxygen content and blending ratio contributed to the significant synergistic effect between the two fuels, and the synergistic effect showed a nonlinear variation with an increased oxygen concentration. The synergistic effect in a rich oxygen environment (O240%/N260%) is 5.1 times greater than that in the synthetic air (O220%/N280%) and 13.8 times greater than that in the oxygen-poor environment (O210%/N290%). Hence, the blending ratio could be adjusted to maintain the intensity of the synergistic effect in an oxygen-rich atmosphere. [ABSTRACT FROM AUTHOR]

Building runoff presents a good opportunity for water reuse in urban infrastructures; however, it is often polluted by biocides and heavy metals. In order to mitigate the pollution and improve water quality, we analysed the adsorption of heavy metals and biocides onto granular activated carbon (GAC) and investigated the influence of dissolved organic matter (DOM) fractions (>100 kDa, 10–30 kDa, and 3–10 kDa). In addition to our experimental work, we also studied the adsorption process by applying the Langmuir and Freundlich models. The results showed that ≥50% of DOM was adsorbed at low concentrations (5 mgC/L). We also observed that DOM at a small molecular size exhibits improved adsorption. The adsorption capacity estimated by the Langmuir equation for Cu2+ and Zn2+ in the absence of DOM influence was 157 and 85.7 μmol/g, respectively. The presence of DOM at 5 mgC/L improved the adsorption of Cu2+. Zn2+ adsorption was less sensitive to the presence of DOM than Cu2+. Interestingly, without the influence of DOM, diuron-related compounds have a higher affinity toward GAC than terbutryn-related compounds. DOM affected the adsorption of diuron slightly. For terbutryn, the adsorption was enhanced, whereas mecoprop-p exhibited a strong competition with DOM. The presence of Cu2+ and Zn2+ presented a similar effect on the adsorption of biocides like DOM. Overall, GAC is an ideal adsorbent material for use in retaining building runoff pollutants. [ABSTRACT FROM AUTHOR]

Precise recognition of maize growth stages in the field is one of the critical steps in conducting precision irrigation and crop growth evaluation. However, due to the ever-changing environmental factors and maize growth characteristics, traditional recognition methods usually suffer from limitations in recognizing different growth stages. For the purpose of tackling these issues, this study proposed an improved U-net by first using a cascade convolution-based network as the encoder with a strategy for backbone network replacement to optimize feature extraction and reuse. Secondly, three attention mechanism modules have been introduced to upgrade the decoder part of the original U-net, which highlighted critical regions and extracted more discriminative features of maize. Subsequently, a dilation path of the improved U-net was constructed by integrating dilated convolution layers using a multi-scale feature fusion approach to preserve the detailed spatial information of in-field maize. Finally, the improved U-net has been applied to recognize different growth stages of maize in the field. The results clearly demonstrated the superior ability of the improved U-net to precisely segment and recognize maize growth stage from in-field images. Specifically, the semantic segmentation network achieved a mean intersection over union (mIoU) of 94.51% and a mean pixel accuracy (mPA) of 96.93% in recognizing the maize growth stage with only 39.08 MB of parameters. In conclusion, the good trade-offs made in terms of accuracy and parameter number demonstrated that this study could lay a good foundation for implementing accurate maize growth stage recognition and long-term automatic growth monitoring. [ABSTRACT FROM AUTHOR]

Natural colorants, mostly of plant and fungal origins, offer advantages over chemically synthetic colorants in terms of alleviating environmental pollution and promoting human health. The market value of natural colorants has been increasing significantly across the globe. Due to the ease of artificially culturing most fungi in the laboratory and in industrial settings, fungi have emerged as the organisms of choice for producing many natural colorants. Indeed, there is a wide variety of colorful fungi and a diversity in the structure and bioactivity of fungal colorants. Such broad diversities have spurred significant research efforts in fungi to search for natural alternatives to synthetic colorants. Here, we review recent research on the genetic and environmental factors influencing the production of three major types of natural fungal colorants: carotenoids, melanins, and polyketide-derived colorants. We highlight how molecular genetic studies and environmental condition manipulations are helping to overcome some of the challenges associated with value-added and large-scale productions of these colorants. We finish by discussing potential future trends, including synthetic biology approaches, in the commercial production of fungal colorants. [ABSTRACT FROM AUTHOR]

Triclosan (TCS), a commonly used antibacterial preservative, has been demonstrated to have high toxicological potential and adversely affects the water bodies. Since algae are one of the most significant primary producers on the planet, understanding the toxicological processes of TCS is critical for determining its risk in aquatic ecosystems and managing the water environment. The physiological and transcriptome changes in Euglena gracilis were studied in this study after 7 days of TCS treatment. A distinct inhibition ratio for the photosynthetic pigment content in E. gracilis was observed from 2.64% to 37.42% at 0.3–1.2 mg/L, with TCS inhibiting photosynthesis and growth of the algae by up to 38.62%. Superoxide dismutase and glutathione reductase significantly changed after exposure to TCS, compared to the control, indicating that the cellular antioxidant defense responses were induced. Based on transcriptomics, the differentially expressed genes were mainly enriched in biological processes involved in metabolism pathways and microbial metabolism in diverse environments. Integrating transcriptomics and biochemical indicators found that changed reactive oxygen species and antioxidant enzyme activities stimulating algal cell damage and the inhibition of metabolic pathways controlled by the down-regulation of differentially expressed genes were the main toxic mechanisms of TCS exposure to E. gracilis. These findings establish the groundwork for future research into the molecular toxicity to microalgae induced by aquatic pollutants, as well as provide fundamental data and recommendations for TCS ecological risk assessment. [ABSTRACT FROM AUTHOR]

Air pollution is a pressing issue that poses significant threats to human health and the ecological environment. The accurate prediction of air quality is crucial to enable management authorities and vulnerable populations to take measures to minimize their exposure to hazardous pollutants. Although many methods have been developed to predict air quality data, the spatio-temporal correlation of air quality data is complex and nonstationary, which makes air quality prediction still challenging. To address this, we propose a novel spatio-temporal neural network, GCNInformer, that combines the graph convolution network with Informer to predict air quality data. GCNInformer incorporates information about the spatial correlations among different monitoring sites through GCN layers and acquires both short-term and long-term temporal information in air quality data through Informer layers. Moreover, GCNInformer uses MLP layers to learn low-dimensional representations from meteorological and air quality data. These designs give GCNInformer the ability to capture the complex and nonstationary relationships between air pollutants and their surrounding environment, allowing for more accurate predictions. The experimental results demonstrate that GCNInformer outperforms other methods in predicting both short-term and long-term air quality data. Thus, the use of GCNInformer can provide useful information for air pollutant prevention and management, which can greatly improve public health by alerting individuals and communities to potential air quality hazards. [ABSTRACT FROM AUTHOR]

The study of massive neutrinos and their interactions is a critical aspect of contemporary cosmology. Recent advances in parallel computation and high-performance computing provide new opportunities for accurately constraining Large-Scale Structures (LSS). In this paper, we introduce the TianNu cosmological N-body simulation during the co-evolution of massive neutrino and cold dark matter components via the CUBEP 3 M code running on the supercomputer Tianhe-2 and TianNu's connected works. We start by analyzing 2.537 × 10 7 dark halos from the scientific data of TianNu simulation, and compare their angular momentum with the matched halos from neutrino-free TianZero, revealing a dependence of angular momentum modulus on neutrino injection at scales below 50 Mpc and around 10 Mpc. [ABSTRACT FROM AUTHOR]

To give full play to the advantages of polyurethane as a binder, such as mixing at room temperature, short curing time, and high curing strength, polyurethane was used as the binder of a waste asphalt mixture, and the pavement performance of PCRM (Polyurethane Cold-Recycled Mixture) was analyzed. Firstly, the adhesion performance of polyurethane binder with new and old aggregates was evaluated using the adhesion test. Then, the mix proportion was designed according to the material characteristics, and the reasonable molding process, maintenance conditions, design indexes, and the optimal binder ratio were proposed. Secondly, the high-temperature stability, low-temperature crack resistance, water stability, and compressive resilient modulus of the mixture were evaluated through laboratory tests. Finally, the pore structure and microscopic morphology of polyurethane cold-recycled mixture were analyzed by industrial CT (Computerized Tomography) scanning, and the failure mechanism of polyurethane cold-recycled mixture was revealed. The test results show that the adhesion between polyurethane and RAP (Reclaimed Asphalt Pavement) is good, and the splitting strength of the mixture increases greatly when the ratio of glue to stone reaches 9%. Polyurethane binder has low sensitivity to temperature and poor water stability. With the increase of RAP content, the high-temperature stability, low-temperature crack resistance, and compressive resilient modulus of PCRM showed a decreasing trend. When the RAP content was less than 40%, the freeze–thaw splitting strength ratio of the mixture was improved. After the incorporation of RAP, the interface was more complex and there were many micron-scale holes, cracks, and other defects; after high-temperature immersion, the polyurethane binder appeared to show a certain degree of peeling at the holes of the RAP surface. After freeze–thaw, the polyurethane binder on the surface of the mixture produced many cracks. The study of polyurethane cold-recycled mixture is of great significance to realize green construction. [ABSTRACT FROM AUTHOR]

In recent years, various network attacks have emerged. These attacks are often recorded in the form of Pcap data, which contains many attack details and characteristics that cannot be analyzed through traditional methods alone. Therefore, restoring the network attack scenario through scene reconstruction to achieve data regeneration has become an important entry point for detecting and defending against network attacks. However, current network attack scenarios mainly reproduce the attacker's attack steps by building a sequence collection of attack scenarios, constructing an attack behavior diagram, or simply replaying the captured network traffic. These methods still have shortcomings in terms of traffic regeneration. To address this limitation, this paper proposes an SDN-based network attack scenario recovery method. By parsing Pcap data and utilizing network topology reconstruction, probability, and packet sequence models, network traffic data can be regenerated. The experimental results show that the proposed method is closer to the real network, with a higher similarity between the reconstructed and actual attack scenarios. Additionally, this method allows for adjusting the intensity of the network attack and the generated topology nodes, which helps network defenders better understand the attackers' posture and analyze and formulate corresponding security strategies. [ABSTRACT FROM AUTHOR]

In this study, star anise (Illicium verum) essential oils (SAEOs) were extracted by hydrodistillation (HD), ethanol solvent extraction (ESE), supercritical CO2 (SCD) and subcritical extraction (SE) via electronic nose (E-nose), gas chromatography-mass spectrometry (GC-MS), and GC-ion mobility spectrometry (GC-IMS). GC-MS and GC-IMS were used to identify the volatile compounds, and GC-MS was also used to determine their concentrations. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to visualise volatile compounds and differentiate samples. The results showed that anethole and limonene were the main volatile compounds in SAEOs extracted using the four methods and their components were similar, albeit in different proportions. In addition, the fingerprints of their volatile components were established via E-nose and GC-IMS analyses. In general, GC-MS, GC-IMS, and E-nose combined with PCA and LDA analysis could accurately distinguish SAEOs extracted using different extraction methods, and GC-IMS was identified as the most suitable method because of its accuracy and rapidity. [ABSTRACT FROM AUTHOR]

Gas sensors have played a critical role in healthcare, atmospheric environmental monitoring, military applications and so on. In particular, flexible sensing devices are of great interest, benefitting from flexibility and wearability. However, developing flexible gas sensors with a high sensitivity, great stability and workability is still challenging. In this work, multi-walled carbon nanotubes (MWCNTs) were grown on polydimethylsiloxane (PDMS) films, which were further modified with polyaniline (PANI) using a simple chemical oxidation synthesis. The superior flexibility of the PANI-MWCNTs/PDMS film enabled a stable initial resistance value, even under bending conditions. The flexible sensor showed excellent NH3 sensing performances, including a high response (11.8 ± 0.2 for 40 ppm of NH3) and a low limit of detection (10 ppb) at room temperature. Moreover, the effect of a humid environment on the NH3 sensing performances was investigated. The results show that the response of the sensor is enhanced under high humidity conditions because water molecules can promote the adsorption of NH3 on the PANI-MWCNTs/PDMS films. In addition, the PANI-MWCNTs/PDMS film sensor had the abilities of detecting NH3 in the simulated breath of patients with kidney disease and the freshness of shrimp. These above results reveal the potential application of the PANI-MWCNTs/PDMS sensor for monitoring NH3 in human breath and food. [ABSTRACT FROM AUTHOR]

In recent years, deep learning models have been more and more widely used in various fields and have become a research hotspot for various tasks in artificial intelligence, but there are significant limitations in non-convex optimization problems. As a model training strategy for non-convex optimization, curriculum learning advocates that models learn in the order of easier to more difficult data, mimicking the basic idea of gradual human learning as they learn curriculum. This strategy has been widely used in the fields of computer vision, natural language processing, and reinforcement learning; it can effectively solve the non-convex optimization problem and improve the generalization ability and convergence speed of models. This paper first introduces the application of curriculum learning at three major levels: data, task, and model, and summarizes the evaluators designed using curriculum learning methods in various domains, including difficulty evaluators, training schedulers, and loss evaluators, which correspond to the three stages of difficulty evaluation, training schedule, and loss evaluation in the application of curriculum learning to model training. We also discuss how to choose an appropriate evaluation system and the differences between terms used in different types of research. Finally, we summarize five methods similar to curriculum learning in the field of machine learning and provide a summary and outlook of the curriculum learning evaluation system. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing need for the development of low-power gas sensors. This paper proposes pulse heating and a corresponding measurement strategy using a Pulse Width Modulation (PWM) signal to realize the ultra-low power consumption for metal oxide semiconductor (MOS) gas sensors. A Micro-Hot-Plate (MHP) substrate was chosen to investigate the temperature and power characteristics of the MHP under different applied heating methods. The temperature of this given substrate could respond to the applied voltage within 0.1 s, proving the prac ticability of a pulse heating strategy. In addition, Pd-doped SnO2 was synthesized as the sensing material in the implementation of an ultra-low power gas sensor. The sensing performance and power consumption under different conditions were compared in the detection of reducing gases such as ethanol (C2H5OH) and formaldehyde (HCHO). Additionally, the results revealed that the sensor could work under PWM excitation while reducing the operating power to less than 1mW. The features shown in the measurements provide the feasibility for MOS gas sensors' application in wearable and portable devices. [ABSTRACT FROM AUTHOR]

The most significant increase of current task is in the desire for operational flexibility and agility in large-scale underwater network application scenarios in recent years. In order to address the challenging problems in Underwater Wireless Sensor Networks (UWSNs), we propose a large-scale UWSN based on the cellular network architecture called Underwater Cellular (uw-Cellular) network. It is designed especially for application scenarios where a large number of both fixed and mobile network nodes exist in a wide area to monitor the underwater environment. We also design protocols in each network layer in order to ensure reasonable channel sharing, data forwarding path selection and data reliability. The purpose of the simulation study we implement is to evaluate the performance of the CLA routing strategy compared to the VBF and the MFLOOD protocols in the uw-Cellular network. We also deploy a twenty-node uw-Cellular network in the real-world environment as the field case study. The experimental results showed that the Data Rate between any nodes could reach above 500 bps, and the network Average Throughput was no less than 550 bps under various load situations. [ABSTRACT FROM AUTHOR]

Remote sensing image classification (RSIC) is a classical and fundamental task in the intelligent interpretation of remote sensing imagery, which can provide unique labeling information for each acquired remote sensing image. Thanks to the potent global context information extraction ability of the multi-head self-attention (MSA) mechanism, visual transformer (ViT)-based architectures have shown excellent capability in natural scene image classification. However, in order to achieve powerful RSIC performance, it is insufficient to capture global spatial information alone. Specifically, for fine-grained target recognition tasks with high inter-class similarity, discriminative and effective local feature representations are key to correct classification. In addition, due to the lack of inductive biases, the powerful global spatial context representation capability of ViT requires lengthy training procedures and large-scale pre-training data volume. To solve the above problems, a hybrid architecture of convolution neural network (CNN) and ViT is proposed to improve the RSIC ability, called P 2 FEViT, which integrates plug-and-play CNN features with ViT. In this paper, the feature representation capabilities of CNN and ViT applying for RSIC are first analyzed. Second, aiming to integrate the advantages of CNN and ViT, a novel approach embedding CNN features into the ViT architecture is proposed, which can make the model synchronously capture and fuse global context and local multimodal information to further improve the classification capability of ViT. Third, based on the hybrid structure, only a simple cross-entropy loss is employed for model training. The model can also have rapid and comfortable convergence with relatively less training data than the original ViT. Finally, extensive experiments are conducted on the public and challenging remote sensing scene classification dataset of NWPU-RESISC45 (NWPU-R45) and the self-built fine-grained target classification dataset called BIT-AFGR50. The experimental results demonstrate that the proposed P 2 FEViT can effectively improve the feature description capability and obtain outstanding image classification performance, while significantly reducing the high dependence of ViT on large-scale pre-training data volume and accelerating the convergence speed. The code and self-built dataset will be released at our webpages. [ABSTRACT FROM AUTHOR]

Passive location technology has been greatly developed because of its low power consumption, long detection distance, good concealment, and strong anti-interference ability. Orthogonal frequency-division multiplexing (OFDM) is an efficient multi-carrier transmission technology, which is an important signal form of 5G communication. Researching passive locations for OFDM signals can realize the location of base stations, which is of great significance in the military. Space-borne passive location technology has a contradiction between wide coverage and high precision. Therefore, a single-satellite passive location algorithm for OFDM radiation sources based on the virtual synthetic aperture is proposed. The algorithm introduces virtual synthetic aperture technology, using antenna movement to accumulate data coherently over a long time period and synthesizing a long azimuth virtual aperture. In addition, it utilizes fast Fourier transform (FFT) to extract phase information at a specific frequency based on the multi-carrier modulation technology of the OFDM signal. Pilot technology of the communication system is used for phase compensation and noise reduction. Thus, the azimuth linear frequency modulation (LFM) signal containing the location information of the radiation source is obtained. The radiation source location can be obtained by range searching and azimuth focusing. Simulation results verify the effectiveness of the algorithm and show that the algorithm can realize high-precision and wide-coverage location for the OFDM radiation sources using a single antenna, turning the hardware structure into software to reduce the cost and complexity of the system. [ABSTRACT FROM AUTHOR]

Safflower (Carthamus tinctorius. L) possesses anti-tumor, anti-thrombotic, anti-oxidative, immunoregulatory, and cardio-cerebral protective effects. It is used clinically for the treatment of cardio-cerebrovascular disease in China. This study aimed to investigate the effects and mechanisms of action of safflower extract on myocardial ischemia–reperfusion (MIR) injury in a left anterior descending (LAD)-ligated model based on integrative pharmacology study and ultra-performance liquid chromatography–quadrupole time-of-flight-tandem mass spectrometer (UPLC-QTOF-MS/MS). Safflower (62.5, 125, 250 mg/kg) was administered immediately before reperfusion. Triphenyl tetrazolium chloride (TTC)/Evans blue, echocardiography, terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) assay, lactate dehydrogenase (LDH) ability, and superoxide dismutase (SOD) levels were determined after 24 h of reperfusion. Chemical components were obtained using UPLC-QTOF-MS/MS. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting were used to analyze mRNA and protein levels, respectively. Safflower dose-dependently reduced myocardial infarct size, improved cardiac function, decreased LDH levels, and increased SOD levels in C57/BL6 mice. A total of 11 key components and 31 hub targets were filtered based on the network analysis. Comprehensive analysis indicated that safflower alleviated inflammatory effects by downregulating the expression of NFκB1, IL-6, IL-1β, IL-18, TNFα, and MCP-1 and upregulating NFκBia, and markedly increased the expression of phosphorylated PI3K, AKT, PKC, and ERK/2, HIF1α, VEGFA, and BCL2, and decreased the level of BAX and phosphorylated p65. Safflower shows a significant cardioprotective effect by activating multiple inflammation-related signaling pathways, including the NFκB, HIF-1α, MAPK, TNF, and PI3K/AKT signaling pathways. These findings provide valuable insights into the clinical applications of safflower. [ABSTRACT FROM AUTHOR]

A novel ZIF-8 assisted polyacrylamide functionalized silica polar stationary phase was prepared by a facile method of adding ZIF-8 nanoparticles and silica into polyacrylamide (PAM) aqueous solution, and then stirring the mixture until the solution was evaporated absolutely with the help of mechanical agitator. Interestingly, the prepared stationary phase (PAM-Sil@ZIF-8/PAM) showed favorable hydrophilicity, as demonstrated by good separation performance and strong retention ability toward several types of polar compounds, such as amino acids, saccharides, and alkaloids. In addition, PAM-Sil@ZIF-8(50 mg)/PAM showed better separation performance compared with amino-modified silica and Sil/PAM stationary phase due to the addition of ZIF-8. Furthermore, it was demonstrated that the addition of ZIF-8 improved the specific surface area and provided extra interaction sites for the stationary phase and analytes, which is beneficial for the separation of samples. In addition, the column efficiency can reach 7861.9 plates/m for colchicine. Moreover, it exhibited good stability and reproducibility under our operation conditions. In short, a novel stationary phase, which can be used for hydrophilic interaction liquid chromatography, was obtained and proved that the separation performance of polymer modifying silica stationary phase can be greatly improved by the addition of MOF nanoparticles. [ABSTRACT FROM AUTHOR]

Simple Summary: The fibroin heavy chain (FibH) is the core protein of silkworm silk, and a systematic analysis of its sequence (primary structure) is essential for understanding silkworm silk fibers. However, only a few FibH sequences have been identified. The recently published high-resolution silkworm pan-genome was useful in analyzing the FibH gene systematically. In this study, we extracted the complete FibH sequences of wild silkworms, local and improved strains from the silkworm pan-genome, bringing the total number of known silkworm FibH genes to 286. We characterized in detail the FibH variation during domestication and breeding and established a silkworm pedigree based on the FibH gene sequence. Additionally, we investigated the relationship between the cis-regulatory elements of FibH gene and silk yield in wild and domesticated silkworms. Our study laid the foundation for artificial silk as well as provided guidance for silkworm breeding. The highly repetitive and variable fibroin heavy chain (FibH) gene can be used as a silkworm identification; however, only a few complete FibH sequences are known. In this study, we extracted and examined 264 FibH gene complete sequences (FibHome) from a high-resolution silkworm pan-genome. The average FibH lengths of the wild silkworm, local, and improved strains were 19,698 bp, 16,427 bp, and 15,795 bp, respectively. All FibH sequences had a conserved 5′ and 3′ terminal non-repetitive (5′ and 3′ TNR, 99.74% and 99.99% identity, respectively) sequence and a variable repetitive core (RC). The RCs differed greatly, but they all shared the same motif. During domestication or breeding, the FibH gene mutated with hexanucleotide (GGTGCT) as the core unit. Numerous variations existed that were not unique to wild and domesticated silkworms. However, the transcriptional factor binding sites, such as fibroin modulator-binding protein, were highly conserved and had 100% identity in the FibH gene's intron and upstream sequences. The local and improved strains with the same FibH gene were divided into four families using this gene as a marker. Family I contained a maximum of 62 strains with the optional FibH (Opti-FibH, 15,960 bp) gene. This study provides new insights into FibH variations and silkworm breeding. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid wetting surface (HWS) with Au/Ag alloy nanocomposites was proposed for rapid, cost-effective, stable and sensitive SERS application. This surface was fabricated in a large area by facile electrospinning, plasma etching and photomask-assisted sputtering processes. The high-density 'hot spots' and rough surface from plasmonic alloy nanocomposites promoted the significant enhancement of the electromagnetic field. Meanwhile, the condensation effects induced by HWS further improved the density of target analytes at the SERS active area. Thus, the SERS signals increased ~4 orders of magnitude compared to the normal SERS substrate. In addition, the reproducibility, uniformity, as well as thermal performance of HWS were also examined by comparative experiments, indicating their high reliability, portability and practicability for on-site tests. The efficient results suggested that this smart surface had great potential to evolve as a platform for advanced sensor-based applications. [ABSTRACT FROM AUTHOR]

The dried root of Codonopsis pilosula (Franch.) Nannf., referred to as Dangshen in Chinese, is a famous traditional Chinese medicine. Polysaccharides, lobetyolin, and atractylenolide III are the major bioactive components contributing to its medicinal properties. Here, we investigated the dynamic changes of the main substances in annual Dangshen harvested at 12 time points from 20 May to 20 November 2020 (from early summer to early winter). Although the root biomass increased continuously, the crude polysaccharides content increased and then declined as the temperature fell, and so did the content of soluble proteins. However, the content of total phenolics and flavonoids showed an opposite trend, indicating that the carbon flux was changed between primary metabolism and secondary metabolism as the temperature and growth stages changed. The changes in the contents of lobetyolin and atractylenolide III indicated that autumn might be a suitable harvest time for Dangshen. The antioxidant capacity in Dangshen might be correlated with vitamin C. Furthermore, we analyzed the expression profiles of a few enzyme genes involved in the polysaccharide biosynthesis pathways at different growth stages, showing that CpUGpase and CPPs exhibited a highly positive correlation. These results might lay a foundation for choosing cultivars using gene expression levels as markers. [ABSTRACT FROM AUTHOR]

Building atrium design is crucial to maintaining a sustainable built environment and providing thermal comfort to occupants. This study proposes a parametric framework to optimize the atrium's geometry for environmental performance and thermal comfort improvement. It integrates the parametric design, performance simulation, and multi-objective optimization in the Rhino and Grasshopper platform to realize automatic optimization. The atrium's well index, shape ratio, volume ratio, position index, and inner interface window-to-wall ratio were set as optimized factors. For the optimization objectives, useful daylight illuminance (UDI), energy use intensity (EUI), and the discomfort time percentage (DTP) were chosen as metrics for the measurement of daylighting, energy use efficiency, and thermal comfort, respectively. Moreover, a geometry mapping method is developed; it can turn atrium shape into rectangular profiles. Thus, the framework can apply to general buildings. To validate the effectiveness of the proposed framework, an atrium optimization case study is conducted for a villa in Poland. According to the optimization results, the performance of the compared three objectives are improved by 43.20%, 15.52%, and 3.89%, respectively. The running time for the optimization is about 36 s per solution, which greatly reduce the human and time cost compared to the traditional working method. [ABSTRACT FROM AUTHOR]

Phytoplasmas are uncultivable, phloem-limited, phytopathogenic bacteria that represent a major threat to agriculture worldwide. Phytoplasma membrane proteins are in direct contact with hosts and presumably play a crucial role in phytoplasma spread within the plant as well as by the insect vector. Three highly abundant types of immunodominant membrane proteins (IDP) have been identified within the phytoplasmas: immunodominant membrane protein (Imp), immunodominant membrane protein A (IdpA), and antigenic membrane protein (Amp). Although recent results indicate that Amp is involved in host specificity by interacting with host proteins such as actin, little is known about the pathogenicity of IDP in plants. In this study, we identified an antigenic membrane protein (Amp) of rice orange leaf phytoplasma (ROLP), which interacts with the actin of its vector. In addition, we generated Amp-transgenic lines of rice and expressed Amp in tobacco leaves by the potato virus X (PVX) expression system. Our results showed that the Amp of ROLP can induce the accumulation of ROLP and PVX in rice and tobacco plants, respectively. Although several studies have reported interactions between major phytoplasma antigenic membrane protein (Amp) and insect vector proteins, this example demonstrates that Amp protein can not only interact with the actin protein of its insect vector but can also directly inhibit host defense responses to promote the infection. The function of ROLP Amp provides new insights into the phytoplasma-host interaction. [ABSTRACT FROM AUTHOR]

Decidualization is necessary for the successful establishment of early pregnancy in rodents and humans. Disturbed decidualization results in recurrent implantation failure, recurrent spontaneous abortion, and preeclampsia. Tryptophan (Trp), one of the essential amino acids in humans, has a positive effect on mammalian pregnancy. Interleukin 4-induced gene 1 (IL4I1) is a recently identified enzyme that can metabolize L-Trp to activate aryl hydrocarbon receptor (AHR). Although IDO1-catalyzed kynurenine (Kyn) from Trp has been shown to enhance human in vitro decidualization via activating AHR, whether IL4I1-catalyzed metabolites of Trp are involved in human decidualization is still unknown. In our study, human chorionic gonadotropin stimulates IL4I1 expression and secretion from human endometrial epithelial cells through ornithine decarboxylase-induced putrescine production. Either IL4I1-catalyzed indole-3-pyruvic acid (I3P) or its metabolite indole-3-aldehyde (I3A) from Trp is able to induce human in vitro decidualization by activating AHR. As a target gene of AHR, Epiregulin induced by I3P and I3A promotes human in vitro decidualization. Our study indicates that IL4I1-catalyzed metabolites from Trp can enhance human in vitro decidualization through AHR-Epiregulin pathway. [ABSTRACT FROM AUTHOR]

The automatic identification of construction projects, which can be considered as complex scenes, is a technical challenge for the supervision of soil and water conservation in urban areas. Construction projects in high-resolution remote sensing images have no unified semantic definition, thereby exhibiting significant differences in image features. This paper proposes an identification method for construction projects based on the detection of detailed ground objects, which construction projects comprise, including movable slab houses, buildings under construction, dust screens, and bare soil (rock). To create the training data set, we select highly informative detailed ground objects from high-resolution remote sensing images. Then, the Faster RCNN (region-based convolutional neural network) algorithm is used to detect construction projects and the highly informative detailed ground objects separately. The merging of detection boxes and the correction of detailed ground object combinations are used to jointly improve the confidence of construction project detection results. The empirical experiments show that the accuracy evaluation indicators of this method on a data set of Wuhan construction projects outperform other comparative methods, and its AP value and F1 score reached 0.773 and 0.417, respectively. The proposed method can achieve satisfactory identification results for construction projects with complex scenes, and can be applied to the comprehensive supervision of soil and water conservation in construction projects. [ABSTRACT FROM AUTHOR]

In recent years, some research results have been achieved in the field of image steganalysis. However, there are still problems of difficulty in extracting steganographic features from images with low embedding rates and unsatisfactory detection performance of steganalysis. In this paper, we propose an image steganalysis method based on the attention mechanism and transfer learning. The method constructs a network model based on a convolutional neural network, including a preprocessing layer, a transposed convolutional layer, an ordinary convolutional layer, and a fully connected layer. We introduce the efficient channel attention module after the ordinary convolutional layer to focus on the steganographic region of the image, capture the local cross-channel interaction information, realize the adaptive adjustment of feature weights, and enhance the ability to extract steganographic features. Meanwhile, we apply the transfer learning method to use the training model parameters of high embedding rate images as the initialization parameters of the training model of the low embedding rate to achieve feature migration and further improve the steganalysis performance of the low embedding rate. The experimental results show that compared to the typical Xu-Net and Yedroudj-Net models, the detection accuracy of the proposed method is improved by 16.36% to 30.66% and by 35.59 to 37.83% for the embedding rates of 0.05 bpp, 0.1 bpp, and 0.2 bpp, respectively. Compared to the state-of-the-art Shen-Net model with low embedding rates, the detection accuracy is improved by 3.43% to 6.41%. This demonstrates the higher detection performance of the proposed method for steganalysis of low embedding rate images. [ABSTRACT FROM AUTHOR]

In recent years, ozone and PM 2.5 pollution has often occured in the Fenwei Plain due to heavy emission and favorable geographical conditions. In this study, we used the weather research and forecasting/chemistry (WRF-Chem) model to reproduce the complex air pollution of the ozone and PM 2.5 in the Fenwei Plain (FWP) from 20 May to 29 May 2015. By comparing the simulation results with the observed data, we found that although in some cities there was a bias between the simulated values and observed data, the model captured the trend of pollutants generally. Moreover, according to the assessment parameters, we validated that the deviations are acceptable. However, according to these parameters, we found that the WRF-Chem performed better on ozone simulation rather than PM 2.5 . Based on the validation, we further analyzed the pollutant distribution during the contaminated period. Generally speaking, the polluted area is mainly located in the cities of the Shanxi province and Henan province. Moreover, in this time period, pollution mainly occurred on 27 May and 28 May. In addition, due to different formation conditions of ozone and PM 2.5 pollution, the distribution characteristics of these two pollutants were also found to be different. Ozone pollution mainly occurred north of FWP due to the prevailing wind and the chemistry of ozone production. As for PM 2.5 , the pollution occurred at night and the polluted area was located in the FWP. Furthermore, high PM 2.5 areas were closed to emission sources in the FWP, showing a high correlation with primary emissions. [ABSTRACT FROM AUTHOR]

Cyanidin-3-O-glucoside (C3G), an active ingredient in anthocyanins, mainly exists in dark cereals. C3G was investigated for its effect on human gastric cancer (GC) cells, together with its molecular mechanism. The CCK-8 assay results showed that C3G had significant antiproliferative effects on GC cells, but it had little effect on normal cells. Western blot and flow cytometry results showed that C3G regulated the reduction of mitochondrial membrane potential and arrested the cell cycle in the G2/M phase through the AKT signaling pathway, causing the cells to undergo apoptosis. Additionally, in MKN-45 cells, C3G markedly raised intracellular reactive oxygen species (ROS) levels. The wound healing assay and Transwell assay results showed that MKN-45 cell migration was significantly inhibited. Western blot results showed that the expression of E-cadherin protein was upregulated and the expressions of β-catenin, N-cadherin, and Vimentin were downregulated. Additionally, following N-acetylcysteine treatment, the expression levels of these proteins were reduced. In conclusion, C3G caused MKN-45 cells to undergo apoptosis; arrested the cell cycle in the G2/M phase; hindered cell migration; and activated the MAPK, STAT3, and NF-κB signaling pathways, by inducing an increase in ROS levels. Thus, C3G may be a promising new medication for the treatment of GC. [ABSTRACT FROM AUTHOR]

Population distribution data with high spatiotemporal resolution are of significant value and fundamental to many application areas, such as public health, urban planning, environmental change, and disaster management. However, such data are still not widely available due to the limited knowledge of complex human activity patterns. The emergence of location-based service big data provides additional opportunities to solve this problem. In this study, we integrated ambient population data, nighttime light data, and building volume data; innovatively proposed a spatial downscaling framework for Baidu heat map data during work time and sleep time; and mapped the population distribution with high spatiotemporal resolution (i.e., hourly, 100 m) in Beijing. Finally, we validated the generated population distribution maps with high spatiotemporal resolution using the highest-quality validation data (i.e., mobile signaling data). The relevant results indicate that our proposed spatial downscaling framework for both work time and sleep time has high accuracy, that the distribution of the population in Beijing on a regular weekday shows "centripetal centralization at daytime, centrifugal dispersion at night" spatiotemporal variation characteristics, that the interaction between the purpose of residents' activities and the spatial functional differences leads to the spatiotemporal evolution of the population distribution, and that China's "surgical control and dynamic zero COVID-19" epidemic policy was strongly implemented. In addition, our proposed spatial downscaling framework can be transferred to other regions, which is of value for governmental emergency measures and for studies about human risks to environmental issues. [ABSTRACT FROM AUTHOR]

Lepidopteran species are mostly pests, causing serious annual economic losses. High-quality genome sequencing and assembly uncover the genetic foundation of pest occurrence and provide guidance for pest control measures. Long-read sequencing technology and assembly algorithm advances have improved the ability to timeously produce high-quality genomes. Lepidoptera includes a wide variety of insects with high genetic diversity and heterozygosity. Therefore, the selection of an appropriate sequencing and assembly strategy to obtain high-quality genomic information is urgently needed. This research used silkworm as a model to test genome sequencing and assembly through high-coverage datasets by de novo assemblies. We report the first nearly complete telomere-to-telomere reference genome of silkworm Bombyx mori (P50T strain) produced by Pacific Biosciences (PacBio) HiFi sequencing, and highly contiguous and complete genome assemblies of two other silkworm strains by Oxford Nanopore Technologies (ONT) or PacBio continuous long-reads (CLR) that were unrepresented in the public database. Assembly quality was evaluated by use of BUSCO, Inspector, and EagleC. It is necessary to choose an appropriate assembler for draft genome construction, especially for low-depth datasets. For PacBio CLR and ONT sequencing, NextDenovo is superior. For PacBio HiFi sequencing, hifiasm is better. Quality assessment is essential for genome assembly and can provide better and more accurate results. For chromosome-level high-quality genome construction, we recommend using 3D-DNA with EagleC evaluation. Our study references how to obtain and evaluate high-quality genome assemblies, and is a resource for biological control, comparative genomics, and evolutionary studies of Lepidopteran pests and related species. [ABSTRACT FROM AUTHOR]

Essential oils (EOs) have good natural antioxidant and antimicrobial properties; however, their volatility, intense aroma, poor aqueous solubility, and chemical instability limit their applications in the food industry. The encapsulation of EOs in β-cyclodextrins (β-CDs) is a widely accepted strategy for enhancing EO applications. The complexation of cinnamon essential oil (CEO) with five types of β-CDs, containing different substituent groups (β-CD with primary hydroxyl, Mal-β-CD with maltosyl, CM-β-CD with carboxymethyl, HP-β-CD with hydroxypropyl, and DM-β-CD with methyl), inclusion process behaviors, volatile components, and antioxidant and antibacterial activities of the solid complexes were studied. The CEOs complexed with Mal-β-CD, CM-β-CD, and β-CD were less soluble than those complexed with DM-β-CD and HP-β-CD. Molecular docking confirmed the insertion of the cinnamaldehyde benzene ring into various β-CD cavities via hydrophobic interactions and hydrogen bonds. GC-MS analysis revealed that HP-β-CD had the greatest adaptability to cinnamaldehyde. The CEO encapsulated in β-, Mal-β-, and CM-β-CD showed lower solubility but better control-release characteristics than those encapsulated in DM- and HP-β-CD, thereby increasing their antioxidant and antibacterial activities. This study demonstrated that β-, Mal-β-, and CM-β-CD were suitable alternatives for the encapsulation of CEO to preserve its antioxidant and antibacterial activities for long-time use. [ABSTRACT FROM AUTHOR]

Forest landscape spaces have positive effects on human physical and mental health. Meanwhile, gender is an important biological factor in differences in human physical and mental responses when facing stress. Therefore, it is necessary to discuss the gender characteristics and differences of people's experiences of restoration in forest landscapes. Meanwhile, it is urgent to attend to the issue of young adults' physical and mental health. This study aimed to clarify the impact of forest landscape exposure on physical and mental restoration and preferences in young adults of different genders and to explore the relationship between them. Six representative forest landscape spaces found in field research in Liaoning were presented to participants through virtual reality (VR) video. Physiological indicators (blood pressure, heart rate, and pulse), mood indicators (simplified profile of mood states), and preference scores of young adults (n = 319) before and after viewing the forest landscape videos were collected. Analysis of differences and Spearman's rho correlation analysis were used to statistically analyse the data. Our results indicated that overlook landscape space, static water landscape space, and coniferous forest landscape space had differential restorative effects on participants' physical and mental health. Male and female participants had different preferences regarding the forest landscape spaces. Meanwhile, there were strong correlations between participants' preferences and restorative effects. Our findings provide preliminary practical basis for forest landscape planning that corresponds to the health needs of tourists of different genders to achieve optimization of health benefits of urban forest resources. [ABSTRACT FROM AUTHOR]

Scutellaria baicalensis Georgi is an annual herb from the Scutellaria genus that has been extensively used as a traditional medicine for over 2000 years in China. Baicalin and other flavonoids have been identified as the principal bioactive ingredients. The biosynthetic pathway of baicalin in S. baicalensis has been elucidated; however, the specific functions of R2R3-MYB TF, which regulates baicalin synthesis, has not been well characterized in S. baicalensis to date. Here, a S20 R2R3-MYB TF (SbMYB12), which encodes 263 amino acids with a length of 792 bp, was expressed in all tested tissues (mainly in leaves) and responded to exogenous hormone methyl jasmonate (MeJA) treatment. The overexpression of SbMYB12 significantly promoted the accumulation of flavonoids such as baicalin and wogonoside in S. baicalensis hairy roots. Furthermore, biochemical experiments revealed that SbMYB12 is a nuclear-localized transcription activator that binds to the SbCCL7-4, SbCHI-2, and SbF6H-1 promoters to activate their expression. These results illustrate that SbMYB12 positively regulates the generation of baicalin and wogonoside. In summary, this work revealed a novel S20 R2R3-MYB regulator and enhances our understanding of the transcriptional and regulatory mechanisms of baicalin biosynthesis, as well as sheds new light on metabolic engineering in S. baicalensis. [ABSTRACT FROM AUTHOR]

Isoorientin (ISO) is a flavonoid compound containing a luteolin structure, which can induce autophagy in some tumor cells. This study investigated the impact of ISO in gastric cancer AGS cells, and performed an experimental analysis on the main signaling pathways and transduction pathways it regulates. CCK–8 assay results showed that ISO reduced the survival rate of gastric cancer AGS cells, but the toxicity to normal cells was minimal. Hoechst 33342/PI double staining assay results showed that ISO induced apoptosis in gastric cancer AGS cells. Further analysis by flow cytometry and Western blot showed that ISO induced apoptosis via a mitochondria-dependent pathway. In addition, the level of reactive oxygen species (ROS) in gastric cancer AGS cells also increased with the extension of the ISO treatment time. However, cell apoptosis was inhibited by preconditioning cells with N–acetylcysteine (NAC). Moreover, ISO arrested the cell cycle at the G2/M phase by increasing intracellular ROS levels. Cell migration assay results showed that ISO inhibited cell migration by inhibiting the expression of p–AKT, p–GSK–3β, and β–catenin and was also related to the accumulation of ROS. These results suggest that ISO-induced cell apoptosis by ROS–mediated MAPK/STAT3/NF–κB signaling pathways inhibited cell migration by regulating the AKT/GSK–3β/β–catenin signaling pathway in gastric cancer AGS cells. [ABSTRACT FROM AUTHOR]

Binary code similarity measurement is a popular research area in binary analysis with the recent development of deep learning-based models. Current state-of-the-art methods often use the pre-trained language model (PTLM) to embed instructions into basic blocks as representations of nodes within a control flow graph (CFG). These methods will then use the graph neural network (GNN) to embed the whole CFG and measure the binary similarities between these code embeddings. However, these methods almost directly treat the assembly code as a natural language text and ignore its code-specific features when training PTLM. Moreover, They barely consider the direction of edges in the CFG or consider it less efficient. The weaknesses of the above approaches may limit the performances of previous methods. In this paper, we propose a novel method called function similarity using code-specific PPTs and order-sensitive GNN (FUSION). Since the similarity of binary codes is a symmetric/asymmetric problem, we were guided by the ideas of symmetry and asymmetry in our research. They measure the binary function similarity with two code-specific PTLM training strategies and an order-sensitive GNN, which, respectively, alleviate the aforementioned weaknesses. FUSION outperforms the state-of-the-art binary similarity methods by up to 5.4% in accuracy, and performs significantly better. [ABSTRACT FROM AUTHOR]

As ordered porous materials, metal–organic frameworks (MOFs) have attracted tremendous attention in the field of energy conversion and storage due to their high specific surface area, permanent porosity, and tunable pore sizes. Here, MOF-derived MnO/C nanocomposites with regular octahedral shape were synthesized using a Mn-based analogue of the MIL-100 framework (Mn-MIL-100, MIL: Matérial Institut Lavoisier) as the precursor. Using aberration-corrected environmental transmission electron microscopy (ETEM), MnO nanocages with a diameter of approximately 20 nm were recognized in the MnO/C nanocomposites fabricated, dispersed in a microporous carbon matrix homogeneously. The nanocages are composed of MnO nanoparticles with a diameter of approximately 2 nm and with a single crystal structure. The specific surface area of the as-prepared MnO/C octahedra decreases to 256 m2 g−1 from 507 m2 g−1 of the Mn-MIL-100 precursor, whereas the total pore volume increases to 0.245 cm3 g−1, which is approximately 29% higher than that of the precursor (0.190 cm3 g−1). Additionally, when utilized as an electrode for supercapacitors, the MOF-derived MnO/C nanocomposite demonstrates a towering specific capacitance of 421 F g−1 at 0.5 A g−1 and good cycle stability (94%) after 5000 cycles. Our work reveals that the MnO nanoparticles in MOF-derived MnO/C nanocomposites exhibit nanocage structure characteristics, which might be inherited from the Mn-MIL-100 precursor with analogous supertetrahedron units. [ABSTRACT FROM AUTHOR]

Humidity sensors are important in industrial fields and human activities. Metal−organic frameworks (MOFs) and their derivatives are a class of promising humidity−sensing materials with the characteristics of a large specific surface area, high porosity, modifiable frameworks, and high stability. The drawbacks of MOFs, such as poor film formation, low electrical conductivity, and limited hydrophilicity, have been gradually overcome with the development of material science. Currently, it is moving towards a critical development stage of MOF−based humidity sensors from usability to ease of use, of which great challenges remain unsolved. In order to better understand the related challenges and point out the direction for the future development of MOF−based humidity sensors, we reviewed the development of such sensors based on related published work, focusing on six primary types (impedance, capacitive, resistive, fluorescent, quartz crystal microbalance (QCM), and others) and analyzed the sensing mechanism, material design, and sensing performance involved, and presented our thoughts on the possible future research directions. [ABSTRACT FROM AUTHOR]

Simple Summary: Interactions between insects and microorganisms facilitate the spread of the microorganisms, which in turn affect insect behaviors. In the current paper, we reviewed how fungi, bacteria, and viruses act on insect olfaction, ranging from volatiles of microorganism, microorganism-induced changes in host volatiles, and symbiotic microorganisms that directly affect insect olfaction. Finally, we highlighted the importance of olfaction in insect-microorganism interactions and provided insights into future research directions. Olfaction plays an essential role in various insect behaviors, including habitat selection, access to food, avoidance of predators, inter-species communication, aggregation, and reproduction. The olfactory process involves integrating multiple signals from external conditions and internal physiological states, including living environments, age, physiological conditions, and circadian rhythms. As microorganisms and insects form tight interactions, the behaviors of insects are constantly challenged by versatile microorganisms via olfactory cues. To better understand the microbial influences on insect behaviors via olfactory cues, this paper summarizes three different ways in which microorganisms modulate insect behaviors. Here, we deciphered three interesting aspects of microorganisms-contributed olfaction: (1) How do volatiles emitted by microorganisms affect the behaviors of insects? (2) How do microorganisms reshape the behaviors of insects by inducing changes in the synthesis of host volatiles? (3) How do symbiotic microorganisms act on insects by modulating behaviors? [ABSTRACT FROM AUTHOR]