Tuning stability, rheology, and fire-extinguishing performance of advanced firefighting foam material by inorganic nanoparticle flame retardants.

HYPOTHESIS: Nanoparticle-stabilized foams are extremely stable, and flame retardant inorganic nanoparticles should be able to add sealing capacity of firefighting foams on flammable liquid fuels, and hence enhance fire extinguishment performance on liquid fuel fire. In practice, how do flame retardant nanoparticles resist the destructive effect of oil molecules on foam and tune foam properties? EXPERIMENTS: We have prepared a nanoparticle-enhanced foam comprising of hydrocarbon surfactant, short-chain fluorocarbon surfactant, and nanoparticles. The interactions among nanoparticles and surfactant molecules were characterized by using dynamic surface tension and conductivity. Stability, rheology, and oil resistivity on liquid fuel of the nanoparticle-enhanced foam were evaluated systematically. Fire suppression effectiveness of the foams was verified based on a standard experiment. FINDINGS: Foam stability and oil resistivity were enhanced due to self-assembled network structures formed by jammed aggregates composed by nanoparticles and surfactants in Plateau borders and bubble films, providing structural recoverability and enhanced viscoelasticity within foam. Foams containing nano-SiO2, nano-CaCO3, nano-Al(OH)3, and nano-Mg(OH)2 show difference in fire extinguishment due to different ability to enhance foam properties. Foam containing nano-Al(OH)3 shows the strongest adaptation and could shorten fire extinguishing time by 2 times and prolong burn-back time by 2.3 times compared with commercial product.

Flavin-Dependent Monooxygenase Kmy13 Mediates Formation of the Carbocyclic ansa System during Kendomycin B Biosynthesis.

Kendomycin B is distinguished from other ansamycins by its unique, fully carbogenic ansa scaffold. We show here that FAD-dependent monooxygenase Kmy13 is solely responsible for installing the rare ansa structural framework; in vivo gene disruption/complementation experiments and in vitro enzymatic assays are described in detail. Moreover, the compound with a ß-keto ester, kendolactone A (2), was confirmed as the natural substrate of Kmy13 and a bona fide biosynthetic intermediate en route to kendomycin B. Further structural prediction and biochemical assays have provided significant insights into the catalytic mechanism of Kmy13.

The value and accuracy of intracoronary electrocardiogram in the diagnosis of myocardial ischemia in coronary heart disease.

BACKGROUND: Although intracoronary electrocardiography (IC-ECG) offers direct electrophysiological insights into myocardial ischemia caused by insufficient coronary blood supply, compared to common diagnostic methods like electrocardiography (ECG), it lacks widespread adoption and robust clinical research. OBJECTIVE: To analyze the value and accuracy of intracoronary electrocardiogram in myocardial ischemia diagnosis in coronary heart disease patients. METHODS: Three hundred patients treated at our hospital were included in the study. Patients were categorized into non-ischemic group A (Fraction Flow Reserve [FFR] > 0.8) and ischemic group B (FFR < 0.75) based on FFR examination results. Both groups underwent IC-ECG examination. The ischemic group received percutaneous coronary intervention (PCI) treatment followed by another FFR examination, dividing them into non-ischemic subgroup B1 (FFR > 0.8) and ischemic subgroup B2 (FFR < 0.75). Both subgroups underwent IC-ECG examination. Receiver operating curves were constructed using FFR to assess the clinical utility of different IC-ECG parameters. RESULTS: Group A patients showed a significant decrease in ST-segment shift at J-point, ST-segment integral, T-peak, T-wave integral, and T-peak to end-time, while the Corrected Q-T interval (QTc-time) was significantly higher in the B group (p< 0.05). The parameters, including ST-segment shift at J-point, ST-segment integral, T-wave integral, T-peak, T-peak to end-time, and QTc-time, were found to have clinical significance in predicting the occurrence of myocardial ischemia (p< 0.05). CONCLUSION: Intracoronary electrocardiogram QT interval dispersion and Q-T peak (QTp) interval dispersion have a high diagnostic accuracy for myocardial ischemia in coronary heart disease.

Polarization-independent quasi-BIC supported by non-rotationally symmetric dimer metasurfaces.

Asymmetric metasurfaces supporting quasi-bound states in the continuum (-BICs) have recently attracted significant interest in the field of nanophotonics due to their high quality factor and strong light-matter interaction properties. However, asymmetric metasurface structures are susceptible to the polarization state of the incident light, which constrains their potential applications. In this Letter, we present a new, to our knowledge, scheme of polarization-independent quasi-BIC resonance supported by a non-rotationally symmetric nanorod dimer metasurface. By tuning the asymmetry parameter, the designed metasurface exhibits a consistent quasi-BIC response for incident plane waves of arbitrary polarization. The physical mechanism of the quasi-BIC resonance is elucidated by the study of the far-field multipole decomposition and the near-field electromagnetic distribution. We then point out that the realization of the polarization-independent quasi-BIC resonance depends on the transition between magnetic and electric quadrupoles. Furthermore, the designed metasurface is demonstrated to have excellent refractive index sensing performance. This work provides a new idea for the design of polarization-independent and high-performance resonators.

Targeting Recycling Endosomes to Potentiate mRNA Lipid Nanoparticles.

mRNA lipid nanoparticles (LNPs) have emerged as powerful modalities for gene therapies to control cancer and infectious and immune diseases. Despite the escalating interest in mRNA-LNPs over the past few decades, endosomal entrapment of delivered mRNAs vastly impedes therapeutic developments. In addition, the molecular mechanism of LNP-mediated mRNA delivery is poorly understood to guide further improvement through rational design. To tackle these challenges, we characterized LNP-mediated mRNA delivery using a library of small molecules targeting endosomal trafficking. We found that the expression of delivered mRNAs is greatly enhanced via inhibition of endocytic recycling in cells and in live mice. One of the most potent small molecules, endosidine 5 (ES5), interferes with recycling endosomes through Annexin A6, thereby promoting the release and expression of mRNA into the cytoplasm. Together, these findings suggest that targeting endosomal trafficking with small molecules is a viable strategy to potentiate the efficacy of mRNA-LNPs.

Crop insect pest detection based on dilated multi-scale attention U-Net.

BACKGROUND: Crop pests seriously affect the yield and quality of crops. Accurately and rapidly detecting and segmenting insect pests in crop leaves is a premise for effectively controlling insect pests. METHODS: Aiming at the detection problem of irregular multi-scale insect pests in the field, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop insect pest detection. In its encoder, dilated Inception is designed to replace the convolution layer in U-Net to extract the multi-scale features of insect pest images. An attention module is added to its decoder to focus on the edge of the insect pest image. RESULTS: The experiments on the crop insect pest image IP102 dataset are implemented, and achieved the detection accuracy of 92.16% and IoU of 91.2%, which is 3.3% and 1.5% higher than that of MSR-RCNN, respectively. CONCLUSION: The results indicate that the proposed method is effective as a new insect pest detection method. The dilated Inception can improve the accuracy of the model, and the attention module can reduce the noise generated by upsampling and accelerate model convergence. It can be concluded that the proposed method can be applied to practical crop insect pest monitoring system.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

Aphid Recognition and Counting Based on an Improved YOLOv5 Algorithm in a Climate Chamber Environment.

Due to changes in light intensity, varying degrees of aphid aggregation, and small scales in the climate chamber environment, accurately identifying and counting aphids remains a challenge. In this paper, an improved YOLOv5 aphid detection model based on CNN is proposed to address aphid recognition and counting. First, to reduce the overfitting problem of insufficient data, the proposed YOLOv5 model uses an image enhancement method combining Mosaic and GridMask to expand the aphid dataset. Second, a convolutional block attention mechanism (CBAM) is proposed in the backbone layer to improve the recognition accuracy of aphid small targets. Subsequently, the feature fusion method of bi-directional feature pyramid network (BiFPN) is employed to enhance the YOLOv5 neck, further improving the recognition accuracy and speed of aphids; in addition, a Transformer structure is introduced in front of the detection head to investigate the impact of aphid aggregation and light intensity on recognition accuracy. Experiments have shown that, through the fusion of the proposed methods, the model recognition accuracy and recall rate can reach 99.1%, the value mAP@0.5 can reach 99.3%, and the inference time can reach 9.4 ms, which is significantly better than other YOLO series networks. Moreover, it has strong robustness in actual recognition tasks and can provide a reference for pest prevention and control in climate chambers.

[Spatio-temporal Variations in PM2.5and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration].

To coordinate the contradiction between economic development and environmental pollution and achieve the sustainable development of the economy and society, the spatio-temporal variations in PM2.5 were analyzed based on PM2.5 concentration and meteorological data of the Yangtze River Delta (YRD) urban agglomeration. Wavelet transform coherence (WTC), partial wavelet coherence (PWC), and multiple wavelet coherence (MWC) were used to analyze the multi-scale coupling oscillation between PM2.5 and meteorological factors in the time-frequency domain. The results showed that:① the concentration of PM2.5 in the YRD decreased from northwest to southeast, and the spatial range with high PM2.5 concentration decreased annually. The spatial distribution characteristics of the seasonal average PM2.5 concentration were similar to those of the annual average PM2.5 concentration. PM2.5 concentration exhibited the seasonal variation characteristics of high in winter, low in summer, and transitioning between spring and autumn. ② PM2.5 concentration decreased from 2015 to 2021, and the compliance rate increased. The difference in annual average PM2.5 concentration was decreased with dynamic convergence characteristics. The convergence of PM2.5 concentration in summer was greater than that in winter. During the whole study period, the daily average PM2.5 concentration showed a "U" distribution, and the proportion of days with excellent and good PM2.5 levels were 49.72% and 41.45%, respectively. ③ The wavelet coherence between PM2.5 and meteorological factors was different in different time-frequency domains. The main factors affecting PM2.5 were different in different time-frequency scales. At all time-frequency scales, WTC and PWC showed that wind speed and temperature were the best explanatory variables of PM2.5 variation, respectively. ④ The larger the time-frequency scale, the stronger the interaction of multi-factor combinations to explain PM2.5 variations. The synergistic effect of temperature and wind speed could better explain the variation in PM2.5. These results can provide reference for air pollution control in the YRD.

Effect on the diffraction efficiency from the surface absorption of echelle grating in Littrow mounting.

[Opt. Express31, 26156202310.1364/OE.495525] shows an anomalous absorption of the echelle grating in TM polarization light near the pseudo-Brewster angle. On top of that, more generally, we relate the absorptions of echelle grating to the Al materials with an absorption spectral band. The blaze diffraction efficiencies (DEs), absorption strengths and electric field distributions, and the energy of non-blaze diffraction orders of the echelle are analyzed in detail. The computing reveals that the interaction between the incident light for TM polarization and the echelle structure leads to amplifying the absorption strength of Al materials with an absorption spectral band from visible to near IR. The deepening groove depth not only suppresses the absorption strength of the Al-echelle grating under TM polarization closer to the absorption spectra of Al materials but improves the light-collecting ability (LCA) at both polarizations. Therefore, the DE differences of different blaze wavelengths for the wideband blaze are explained. The Ag materials echelle with lower absorption is to further validate the results. From the point of view of the effects of absorption and LCA, the novel echelles with high DE can be designed and fabricated.

Anomalous absorption in TM polarization light by echelle gratings.

Echelle grating plays an essential role in spectral analysis due to its broadband blaze, high dispersion, and high resolution; however, its absorption properties have received no attention. In this paper, we investigate the effect of parameters including incident wavelength, incident angle, and blaze angle on the absorption properties of the Al echelle grating. Based on calculations by the boundary integral equation method, we observe that the Al grating with a large blaze angle has an absorption enhancement effect on TM-polarized waves, and its intensity increases as the incident angle approaches the pseudo-Brewster angle (maximum absorption over 87%). In particular, this absorption enhancement effect is present in the wideband and somewhat generalizable to other metallic materials. In addition, the potential physical mechanisms underlying the absorption enhancement of the echelle grating are analyzed in detail through the electric field distribution. The resonance between the grating anomaly and the pseudo-Brewster effect results in the appearance of surface plasmon polariton and strong absorption. These findings will bring new understanding to the study of echelle gratings in case of high energy loss when the light incidents with a high angle for high resolution and will also show potential applications in electromagnetic stealth, photothermal conversion, and photodetection.

Pepper leaf disease recognition based on enhanced lightweight convolutional neural networks.

Pepper leaf disease identification based on convolutional neural networks (CNNs) is one of the interesting research areas. However, most existing CNN-based pepper leaf disease detection models are suboptimal in terms of accuracy and computing performance. In particular, it is challenging to apply CNNs on embedded portable devices due to a large amount of computation and memory consumption for leaf disease recognition in large fields. Therefore, this paper introduces an enhanced lightweight model based on GoogLeNet architecture. The initial step involves compressing the Inception structure to reduce model parameters, leading to a remarkable enhancement in recognition speed. Furthermore, the network incorporates the spatial pyramid pooling structure to seamlessly integrate local and global features. Subsequently, the proposed improved model has been trained on the real dataset of 9183 images, containing 6 types of pepper diseases. The cross-validation results show that the model accuracy is 97.87%, which is 6% higher than that of GoogLeNet based on Inception-V1 and Inception-V3. The memory requirement of the model is only 10.3 MB, which is reduced by 52.31%-86.69%, comparing to GoogLeNet. We have also compared the model with the existing CNN-based models including AlexNet, ResNet-50 and MobileNet-V2. The result shows that the average inference time of the proposed model decreases by 61.49%, 41.78% and 23.81%, respectively. The results show that the proposed enhanced model can significantly improve performance in terms of accuracy and computing efficiency, which has potential to improve productivity in the pepper farming industry.

Rheological Properties of Gel Foam Co-Stabilized with Nanoparticles, Xanthan Gum, and Multiple Surfactants.

Gel foam has the advantages of gel and foam and shows good prospects for applications in the fields of fire prevention and extinguishing. Rheology has a significant impact on the application of gel foam, but there is little related research. In the present study, hydrophilic silica nanoparticles (NPs) and water-soluble polymer xanthan gum (XG) were combined with fluorocarbon surfactant (FS-50) and hydrocarbon surfactant (APG0810) to create gel foam. The foaming ability and foam drainage were evaluated. The gel foam's rheology, including its flow behavior and viscoelasticity, was systematically investigated. The results show that the foaming of the FS-50/APG0810 mixture decreases but the foam drainage increases in the presence of NPs and/or XG. All of the foams belong to the category of non-Newtonian fluids with shear thinning behavior. The flow curves of the foams are consistent with the Cross model. The presence of XG/NPs enhanced the foam viscoelasticity of the FS-50/APG0810 mixture. The silica NPs showed a better ability to enhance foam viscoelasticity but a worse ability to stabilize the foam compared to XG. This research can offer theoretical support for the industrial usage of gel foam.

Crop pest detection by three-scale convolutional neural network with attention.

Crop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy. Crop pest detection in field is an essential step to control the pests. The existing convolutional neural network (CNN) based pest detection methods are not satisfactory for small pest recognition and detection in field because the pests are various with different colors, shapes and poses. A three-scale CNN with attention (TSCNNA) model is constructed for crop pest detection by adding the channel attention and spatial mechanisms are introduced into CNN. TSCNNA can improve the interest of CNN for pest detection with different sizes under complicated background, and enlarge the receptive field of CNN, so as to improve the accuracy of pest detection. Experiments are carried out on the image set of common crop pests, and the precision is 93.16%, which is 5.1% and 3.7% higher than ICNN and VGG16, respectively. The results show that the proposed method can achieve both high speed and high accuracy of crop pest detection. This proposed method has certain practical significance of real-time crop pest control in the field.

Design of single-layer color echelle grating optical waveguide for augmented-reality display.

We proposed a single-layer color echelle grating combined optical waveguide structure for an augmented-reality display. In this structure, we used echelle gratings with super-wavelength periodic scale as in-coupling, relay, and out-coupling elements. The combined propagation of three light beams in the waveguide was realized by overlapping different high diffraction orders of the RGB three primary colors, and deflection of the beam direction between gratings was achieved by conical diffraction generated by the inclined grating. Using the vector diffraction theory, the structural parameters and tolerance ranges of the three types of gratings were optimized, rendering average diffraction efficiencies of the three primary colors of the in-coupling, relay, and out-coupling gratings greater than 74%, 21%, and 35%, respectively. As a result, we obtained dual-channel one-dimensional pupil dilation of the original image and a field-of-view angle of h18.9° × v36.87°.

A New Pest Detection Method Based on Improved YOLOv5m.

Pest detection in plants is essential for ensuring high productivity. Convolutional neural networks (CNN)-based deep learning advancements recently have made it possible for researchers to increase object detection accuracy. In this study, pest detection in plants with higher accuracy is proposed by an improved YOLOv5m-based method. First, the SWin Transformer (SWinTR) and Transformer (C3TR) mechanisms are introduced into the YOLOv5m network so that they can capture more global features and can increase the receptive field. Then, in the backbone, ResSPP is considered to make the network extract more features. Furthermore, the global features of the feature map are extracted in the feature fusion phase and forwarded to the detection phase via a modification of the three output necks C3 into SWinTR. Finally, WConcat is added to the fusion feature, which increases the feature fusion capability of the network. Experimental results demonstrate that the improved YOLOv5m achieved 95.7% precision rate, 93.1% recall rate, 94.38% F1 score, and 96.4% Mean Average Precision (mAP). Meanwhile, the proposed model is significantly better than the original YOLOv3, YOLOv4, and YOLOv5m models. The improved YOLOv5m model shows greater robustness and effectiveness in detecting pests, and it could more precisely detect different pests from the dataset.

Designer Nanodiscs to Probe and Reprogram Membrane Biology in Synapses.

Signal transduction at the synapse is mediated by a variety of protein-lipid interactions, which are vital for the spatial and temporal regulation of synaptic vesicle biogenesis, neurotransmitter release, and postsynaptic receptor activation. Therefore, our understanding of synaptic transmission cannot be completed until the elucidation of these critical protein-lipid interactions. On this front, recent advances in nanodiscs have vastly expanded our ability to probe and reprogram membrane biology in synapses. Here, we summarize the progress of the nanodisc toolbox and discuss future directions in this exciting field.

MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA-Disease Association.

A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA.

Dilated convolution capsule network for apple leaf disease identification.

Accurate and rapid identification of apple leaf diseases is the basis for preventing and treating apple diseases. However, it is challenging to identify apple leaf diseases due to their various symptoms, different colors, irregular shapes, uneven sizes, and complex backgrounds. To reduce computational cost and improve training results, a dilated convolution capsule network (DCCapsNet) is constructed for apple leaf disease identification based on a capsule network (CapsNet) and two dilated Inception modules with different dilation rates. The network can obtain multi-scale deep-level features to improve the classification capability of the model. The dynamic routing algorithm is used between the front and back layers of CapsNet to make the model converge quickly. In DCCapsNet, dilated Inception instead of traditional convolution is used to increase the convolution receptive fields and extract multi-scale features from disease leaf images, and CapsNet is used to capture the classification features of changeable disease leaves and overcome the overfitting problem in the training network. Extensive experiment results on the apple disease leaf image dataset demonstrate that the proposed method can effectively identify apple diseases. The method can realize the rapid and accurate identification of apple leaf disease.