Atomically Precise Silver Clusters Stabilized by Lacunary Polyoxometalates with Photocatalytic CO2 Reduction Activity.

The syntheses of atomically precise silver (Ag) clusters stabilized by multidentate lacunary polyoxometalate (POM) ligands have been emerging as a promising but challenging research direction, the combination of redox-active POM ligands and silver clusters will render them unexpected geometric structures and catalytic properties. Herein, we report the successful construction of two structurally-new lacunary POM-stabilized Ag clusters, TBA6 H14 Ag14 (DPPB)4 (CH3 CN)9 [Ag24 (Si2 W18 O66 )3 ] ⋅ 10CH3 CN ⋅ 9H2 O ({Ag24 (Si2 W18 O66 )3 }, TBA=tetra-n-butylammonium, DPPB=1,4-Bis(diphenylphosphino)butane) and TBA14 H6 Ag9 Na2 (H2 O)9 [Ag27 (Si2 W18 O66 )3 ] ⋅ 8CH3 CN ⋅ 10H2 O ({Ag27 (Si2 W18 O66 )3 }), using a facile one-pot solvothermal approach. Under otherwise identical synthetic conditions, the molecular structures of two POM-stabilized Ag clusters could be readily tuned by the addition of different organic ligands. In both compounds, the central trefoil-propeller-shaped {Ag24 }14+ and {Ag27 }17+ clusters bearing 10 delocalized valence electrons are stabilized by three C-shaped {Si2 W18 O66 } units. The femtosecond/nanosecond transient absorption spectroscopy revealed the rapid charge transfer between {Ag24 }14+ core and {Si2 W18 O66 } ligands. Both compounds have been pioneeringly investigated as catalysts for photocatalytic CO2 reduction to HCOOH with a high selectivity.

Attention-Based Convolutional Neural Network for Ingredients Identification.

In recent years, with the development of artificial intelligence, smart catering has become one of the most popular research fields, where ingredients identification is a necessary and significant link. The automatic identification of ingredients can effectively reduce labor costs in the acceptance stage of the catering process. Although there have been a few methods for ingredients classification, most of them are of low recognition accuracy and poor flexibility. In order to solve these problems, in this paper, we construct a large-scale fresh ingredients database and design an end-to-end multi-attention-based convolutional neural network model for ingredients identification. Our method achieves an accuracy of 95.90% in the classification task, which contains 170 kinds of ingredients. The experiment results indicate that it is the state-of-the-art method for the automatic identification of ingredients. In addition, considering the sudden addition of some new categories beyond our training list in actual applications, we introduce an open-set recognition module to predict the samples outside the training set as the unknown ones. The accuracy of open-set recognition reaches 74.6%. Our algorithm has been deployed successfully in smart catering systems. It achieves an average accuracy of 92% in actual use and saves 60% of the time compared to manual operation, according to the statistics of actual application scenarios.

A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation.

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient's water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.

Deep Reinforcement Learning Based Decision Making for Complex Jamming Waveforms.

With the development of artificial intelligence, intelligent communication jamming decision making is an important research direction of cognitive electronic warfare. In this paper, we consider a complex intelligent jamming decision scenario in which both communication parties choose to adjust physical layer parameters to avoid jamming in a non-cooperative scenario and the jammer achieves accurate jamming by interacting with the environment. However, when the situation becomes complex and large in number, traditional reinforcement learning suffers from the problems of failure to converge and a high number of interactions, which are fatal and unrealistic in a real warfare environment. To solve this problem, we propose a deep reinforcement learning based and maximum-entropy-based soft actor-critic (SAC) algorithm. In the proposed algorithm, we add an improved Wolpertinger architecture to the original SAC algorithm in order to reduce the number of interactions and improve the accuracy of the algorithm. The results show that the proposed algorithm shows excellent performance in various scenarios of jamming and achieves accurate, fast, and continuous jamming for both sides of the communication.

Path Planning Research of a UAV Base Station Searching for Disaster Victims' Location Information Based on Deep Reinforcement Learning.

Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm.

Adversarial attacks and adversarial training for burn image segmentation based on deep learning.

Deep learning has been widely applied in the fields of image classification and segmentation, while adversarial attacks can impact the model's results in image segmentation and classification. Especially in medical images, due to constraints from factors like shooting angles, environmental lighting, and diverse photography devices, medical images typically contain various forms of noise. In order to address the impact of these physically meaningful disturbances on existing deep learning models in the application of burn image segmentation, we simulate attack methods inspired by natural phenomena and propose an adversarial training approach specifically designed for burn image segmentation. The method is tested on our burn dataset. Through the defensive training using our approach, the segmentation accuracy of adversarial samples, initially at 54%, is elevated to 82.19%, exhibiting a 1.97% improvement compared to conventional adversarial training methods, while substantially reducing the training time. Ablation experiments validate the effectiveness of individual losses, and we assess and compare training results with different adversarial samples using various metrics.

Insights into ACO genes across Rosaceae: evolution, expression, and regulatory networks in fruit development.

BACKGROUND: ACO (1-aminocyclopropane-1-carboxylic acid) serves as a pivotal enzyme within the plant ethylene synthesis pathway, exerting influence over critical facets of plant biology such as flowering, fruit ripening, and seed development. OBJECTIVE: This study aims to identify ACO genes from representative Rosaceae genomes, reconstruct their phylogenetic relationships by integrating synteny information, and investigate their expression patterns and networks during fruit development. METHODS: we utilize a specialized Hidden Markov Model (HMM), crafted on the sequence attributes of ACO gene-encoded proteins, to systematically identify and analyze ACO gene family members across 12 representative species within the Rosaceae botanical family. Through transcriptome analysis, we delineate the expression patterns of ACO genes in six distinct Rosaceae fruits. RESULTS: Our investigation reveals the presence of 62 ACO genes distributed among the surveyed Rosaceae species, characterized by hydrophilic proteins predominantly expressed within the cytoplasm. Phylogenetic analysis categorizes these ACO genes into three discernible classes, namely Class I, Class II, and Class III. Further scrutiny via collinearity assessment indicates a lack of collinearity relationships among these classes, highlighting variations in conserved motifs and promoter types within each class. Transcriptome analysis unveils significant disparities in both expression levels and trends of ACO genes in fruits exhibiting respiratory bursts compared to those that do not. Employing Weighted Gene Co-Expression Network Analysis (WGCNA), we discern that the co-expression correlation of ACO genes within loquat fruit notably differs from that observed in apples. Our findings, derived from Gene Ontology (GO) enrichment results, signify the involvement of ACO genes and their co-expressed counterparts in biological processes linked to terpenoid metabolism and carbohydrate synthesis in loquat. Moreover, our exploration of gene regulatory networks (GRN) highlights the potential pivotal role of the GNAT transcription factor (Ejapchr1G00010380) in governing the overexpression of the ACO gene (Ejapchr10G00001110) within loquat fruits. CONCLUSION: The constructed HMM of ACO proteins offers a precise and systematic method for identifying plant ACO proteins, facilitating phylogenetic reconstruction. ACO genes from representative Rosaceae fruits exhibit diverse expression and regulative patterns, warranting further function characterizations.

[Study of Th1/Th2 balance in peripheral blood of chronic gastritis patients with Pi-Wei damp-heat syndrome].

OBJECTIVE: To study the correlation between the Th1/Th2 balance in the peripheral blood and Pi-Wei damp-heat syndrome (PDS) in chronic gastritis (CG). METHODS: Fifty-one patients with CG of PDS were recruited, including 22 cases with predominant damp (PDS-D), 9 case with predominant heat (PDS-H), and 20 case with simultaneous onset of damp and Heat (PDS-DH). Besides, 10 healthy volunteers were recruited as the healthy control group. H. pylori (HP) infection was detected by fast urea enzyme, and the expressions of Th1 type cytokines interferon-gamma (IFN-gamma), interleukin-12 (IL-12), and Th2 type cytokines interleukin-4 (IL-4), interleukin-10 (IL-10) in serum were detected by luminex technology. RESULTS: The HP infection rate was 41.18% (21/51) in the PDS patients, obviously higher than that in the healthy control group (10.00%,1/10), showing statistical difference (P<0.05). The HP infection rate was 45.45% (10/22) in PDS-D, 22.22% (2/9) in PDS-H, and 45.00% (9/20) in PDS-DH. The HP infection rate in PDS-D and PDS-DH was significantly higher than that of the healthy control group, showing statistical difference (P<0.05). There was no statistically significant difference in the expressions of peripheral blood IFN-gamma, IL-12, IL-4, and IL-10 between the PDS patient group and the healthy control group (P>0.05). But the expressions of IFN-gamma and IL-12 showed an increasing trend in the PDS patient group, while the expression of IL-4 showed a decreasing trend. The expressions of IFN-gamma, IL-12, IL-4, and the ratios of IFN-gamma/IL-4 and IL-12/IL-4 were also higher in PDS-DH group than in the PDS-D group and the PDS-H group, but with no statistical significance (P>0.05). CONCLUSION: The occurrence of Pi-Wei damp-heat CG was possibly correlated with the imbalance of Th1/Th2. Damp and heat pathogen might be important pathogenic factors leading to Th1 type cytokine immunoreaction.

Exogenous GABA improves the resistance of apple seedlings to long-term drought stress by enhancing GABA shunt and secondary cell wall biosynthesis.

Drought stress is an important factor limiting apple production. γ-Aminobutyric acid (GABA) exists widely in plants and participates in the response to abiotic stress as a metabolite or signaling molecule. The role of exogenous GABA in apple plants, response to long-term drought stress remains unclear. Our study confirmed that exogenous GABA affects the drought resistance of apple plants under long-term drought stress. We found that 1 mM exogenous GABA improved the resistance of apple seedlings to long-term drought stress. The plants showed better growth, less reactive oxygen radical accumulation, less damage to cell membranes and greater active photosynthetic capacity. Under long-term drought stress, exogenous GABA facilitated GABA shunt, resulting in more accumulation of organic acids, namely citric acid, succinic acid and malic acid, in roots and stems of apple seedlings. In addition, exogenous GABA upregulated the expression of cellulose-related genes and lignin-related genes, and activated secondary cell wall-related transcription factors to synthesize more cellulose and lignin. A multiple factorial analysis confirmed that the GABA shunt and the biosynthesis of cellulose and lignin substantially contributed to the growth of apple seedlings with the application of exogenous GABA under long-term drought stress. Our results suggested that exogenous GABA improved the resistance of apple seedlings to long-term drought stress by enhancing GABA shunt and secondary cell wall biosynthesis.

Identification of PLATZ genes in Malus and expression characteristics of MdPLATZs in response to drought and ABA stresses.

Plant AT-rich sequences and zinc-binding proteins (PLATZ) play crucial roles in response to environmental stresses. Nevertheless, PLATZ gene family has not been systemically studied in Rosaceae species, such as in apple, pear, peach, or strawberry. In this study, a total of 134 PLATZ proteins were identified from nine Rosaceae genomes and were classified into seven phylogenetic groups. Subsequently, the chromosomal localization, duplication, and collinearity relationship for apple PLATZ genes were investigated, and segmental duplication is a major driving-force in the expansion of PLATZ in Malus. Expression profiles analysis showed that PLATZs had distinct expression patterns in different tissues, and multiple genes were significantly changed after drought and ABA treatments. Furthermore, the co-expression network combined with RNA-seq data showed that PLATZ might be involved in drought stress by regulating ABA signaling pathway. In summary, this study is the first in-depth and systematic identification of PLATZ gene family in Rosaceae species, especially for apple, and provided specific PLATZ gene resource for further functional research in response to abiotic stress.

MbHY5-MbYSL7 mediates chlorophyll synthesis and iron transport under iron deficiency in Malus baccata.

Iron (Fe) plays an important role in cellular respiration and catalytic reactions of metalloproteins in plants and animals. Plants maintain iron homeostasis through absorption, translocation, storage, and compartmentalization of iron via a cooperative regulative network. Here, we showed different physiological characteristics in the leaves and roots of Malus baccata under Fe sufficiency and Fe deficiency conditions and propose that MbHY5 (elongated hypocotyl 5), an important transcription factor for its function in photomorphogenesis, participated in Fe deficiency response in both the leaves and roots of M. baccata. The gene co-expression network showed that MbHY5 was involved in the regulation of chlorophyll synthesis and Fe transport pathway under Fe-limiting conditions. Specifically, we found that Fe deficiency induced the expression of MbYSL7 in root, which was positively regulated by MbHY5. Overexpressing or silencing MbYSL7 influenced the expression of MbHY5 in M. baccata.