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Abstract The interfacial microstructures of Josephson junctions are vital for understanding the microscopic mechanism to improve the performance of superconducting qubits further. However, there remain significant concerns about well understanding the correlation between atomic structures and electrical behaviors. Here, we propose a new method to define the interface of the barrier in Josephson junctions, and investigate the factors that affect the electrical properties of junctions using material analysis techniques and first principles. We find that the aluminium–oxygen ratio of the interface contributes greatly to the electrical properties of junctions, which is consistent with the conclusions obtained by utilizing the generative adversarial network for data augmentation. When the aluminium–oxygen ratio of the interface is 0.67–1.1, the model exhibits a lower barrier height and better electrical properties of the junction. Moreover, when the thickness of the barrier is fixed, the impact of the aluminium–oxygen ratio becomes prominent. A detailed analysis of Josephson junctions using a microscopic model has led to identifying of process defects that can enhance the yield rate of chips. It has a great boost for determining the relationship between microstructures and macroscopic performances.

Abstract As a generalized quantum machine learning model, parameterized quantum circuits (PQC) have been found to perform poorly in terms of classification accuracy and model scalability for multi-category classification tasks. To address this issue, we propose a scalable parameterized quantum circuits classifier (SPQCC), which performs per-channel PQC and combines the measurements as the output of the trainable parameters of the classifier. By minimizing the cross-entropy loss through optimizing the trainable parameters of PQC, SPQCC leads to a fast convergence of the classifier. The parallel execution of identical PQCs on different quantum machines with the same structure and scale reduces the complexity of classifier design. Classification simulations performed on the MNIST Dataset show that the accuracy of our proposed classifier far exceeds that of other quantum classification algorithms, achieving the state-of-the-art simulation result and surpassing/reaching classical classifiers with a considerable number of trainable parameters. Our classifier demonstrates excellent scalability and classification performance.

Wild soybean (Glycine soja), a relative of cultivated soybean, shows high adaptability to adverse environmental conditions. We identified and characterized a wild soybean transcription factor gene, GsWRKY40, that promotes plant salt stress. GsWRKY40 was highly expressed in wild soybean roots and was up-regulated by salt treatment. GsWRKY40 was localized in nucleus and demonstrated DNA-binding activities but without transcriptional activation. Mutation and overexpression of GsWRKY40 altered salt tolerance of Arabidopsis plants. To understand the molecular mechanism of GsWRKY40 in regulating plant salt resistance, we screened a cDNA library and identified a GsWRKY40 interacting protein GsbHLH92 by using yeast two-hybrid approach. The physical interaction of GsWRKY40 and GsbHLH92 was confirmed by co-immunoprecipitation (co-IP), GST pull-down, and bimolecular fluorescence complementation (BiFC) techniques. Intriguingly, co-overexpression of GsWRKY40 and GsbHLH92 resulted in higher salt tolerance and lower ROS levels than overexpression of GsWRKY40 or GsbHLH92 in composite soybean plants, suggesting that GsWRKY40 and GsbHLH92 may synergistically regulate plant salt resistance through inhibiting ROS production. qRT-PCR data indicated that the expression level of GmSPOD1 gene encoding peroxidase was cooperatively regulated by GsWRKY40 and GsbHLH92, which was confirmed by using a dual luciferase report system and yeast one-hybrid experiment. Our study reveals a pathway that GsWRKY40 and GsbHLH92 collaboratively up-regulate plant salt resistance through impeding GmSPOD1 expression and reducing ROS levels, providing a novel perspective on the regulatory mechanisms underlying plant tolerance to abiotic stresses.

Compared to other organisms, plants have evolved a greater number of aquaporins with diverse substrates and functions to adapt to ever-changing environmental and internal stimuli for growth and development. Although aquaporins were initially identified as channels that allow water molecules to cross biological membranes, progress has been made in identifying various novel permeable substrates. Many studies have characterized the versatile physiological and biophysical functions of plant aquaporins. Here, we review the recent reports that highlight aquaporin-facilitated regulation of major physiological processes and stress tolerance throughout plant life cycles as well as the potential prospects and possibilities of applying aquaporins to improve agricultural productivity, food quality, environmental protection, and ecological conservation.

Abstract In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and language. Since NLP usually consumes a lot of computing resources, its combination with quantum computing is becoming a valuable research direction. In this paper, we present a Recurrent Quantum Embedding Neural Network (RQENN) for vulnerability detection. It aims to reduce the memory consumption of classical models for vulnerability detection tasks and improve the performance of quantum natural language processing (QNLP) methods. We show that the performance of RQENN achieves the above goals. Compared with the classic model, the space complexity of each stage of its execution is exponentially reduced, and the number of parameters used and the number of bits consumed are significantly reduced. Compared with other QNLP methods, RQENN uses fewer qubit resources and achieves a 15.7% higher accuracy in vulnerability detection.

In order to provide theoretical basis for developing and utilizing new varieties of hot peppers, five new varieties of pod peppers (Capsicum annum var. Conoides) hybridized with different combinations by three lines cultivation were selected and numbered as Sample 1, 2, 3, 4 and 5, and their agronomic traits and quality characteristics with an emphasis on capsaicinoids and volatile components were evaluated. The results showed that the peppers had different agronomic traits with their ranges of fruit length, fruit mass, seed number ratio and pulp ratio being 6.7~9.3 cm, 2.40~6.26 g, 15.3~28.1 grain/g, and 71.10%~83.93%, respectively. All the samples possessed high nutritional values but their contents of major nutrients were not the same. Sample 1 contained the highest carbohydrates, crude fats and ash. Sample 2 had the highest water content. Sample 3 contained the highest vitamin C. Sample 5 had the highest protein content. All the samples possessed higher capsaicinoids content and pungency degree, of which Sample 5 had the highest content of capsaicinoids (1.128 g/kg), Scoville index (17399 SHU) and pungency degree (116) while Sample 2 showed the lowest content of capsaicinoids (0.464 g/kg), Scoville index (7157 SHU) and pungency degree (48). A total of 100 volatile components were detected in the five samples, among which olefins were the most abundant component, ranging from 39.20% to 61.73%. Olefins, esters and alcohols were the common aromatic substances of the samples, but their types and contents were different. Pearson correlation analysis displayed that the pungency degrees of the five samples were extremely significantly negatively correlated with their fruit mass and pulp raio (P

Abstract There is evidence of increased soil organic carbon (SOC) and inorganic carbon (SIC) under fertilization in dry croplands of arid and semi-arid areas. However, not much is known about the responses of SOC and SIC in coastal saline − alkaline paddy soils that undergo flooding − draining cycles. Here, we assess the impacts of various combinations of organic and phosphorus fertilization on SOC and SIC and other soil properties in a saline − alkaline paddy field of the Yellow River Delta. Our study showed that organic fertilization resulted in an increase of SOC by 11.9% over 0 − 20 cm and 13.3% over 20 − 100 cm (i.e., 140 − 250 g C m−2y−1 over 0 − 100 cm) whereas phosphorus fertilization only led to a significant increase of SOC in subsoils (or ~ 75 g C m−2y−1 over 0 − 100 cm). There were little differences in SIC over 0 − 20 cm among the treatments; but SIC showed a significant decrease over 20 − 100 cm under organic fertilization combined with lower rate of phosphorus fertilization. However, high rate of phosphorus fertilization combined with organic amendment led to an increase in SIC stock, but a decrease in SOC stock in the subsoil. There was a significant negative relationship between SIC and SOC stocks in this paddy soil. This study demonstrated that fertilization practices could have complex influences on SOC and SIC in saline − alkaline paddy fields due to the flooding − draining cycles that lead to changes in soil conditions.

The replacement of plastic film with biodegradable film is important for reducing plastic film residual pollution in semi-arid regions of northern China. However, information on biodegradable film improvements based on the hydrothermal requirements of major food crops in the typical plastic film mulching area is limited. This study used a combination of field data measurements and curve fitting to assess the spatiotemporal distribution of soil hydrothermal variation and the corresponding maize yield in the field under biodegradable film mulching (BM), polyethylene film mulching (PM), and without mulching (CT). The warming effect of BM was between those of PM and CT and all treatments differed significantly from each other. The duration of the warming effect of BM was shorter than that of PM, and the warming effect of both disappeared at the early jointing stage of maize. The BM soil moisture was significantly lower compared with PM and CT throughout the soil profile (0–180 cm). Moreover, moisture fluctuations were observed in the 0–40 and 40–120 cm soil layers of BM after degradation, and the timing of fluctuations coincided with the filling period of maize. Consequently, the water use efficiency and yield of BM were lower than those of PM. Hence, we suggest that the conservation effect of biodegradable film mulching on soil moisture should persist until the end of the maize filling period, and that the start-up stage of degradation cannot be based solely on the disappearance of the warming effect.

Soil microbes are among the most complex and diverse communities on Earth, actively participating in biogeochemical cycles while responding positively to environmental changes. Saprophytic fungi are important factors in soil carbon cycle, and the response and driving factors in grassland to forestland and grassland to farmland conversion in desert steppe are still poorly understood. Therefore, we analyzed the soil microbial community structure and saprophytic fungal communities in grasslands, shrub lands, and farmlands in typical desert grassland areas of northwest China. It was found that the cultivation of desert steppe plants/crops increased the proportion of silt in soil aggregates while decreasing soil salinity and increasing soil total carbon, nitrogen, and organic matter content. Proteobacteria, Actinobacteriota and Ascomycota were the dominant phyla in desert steppe soils, with Proteobacteria, Gemmatimonadota and Mucoromycota were more responsive to soil environment changes. The co-occurrence network relationship further characterized the process of niche sharing/competition between bacteria and fungi. The complexity of the co-occurrence network was deepened by land use, and fungi occupied more of the niche in shrub planting than in desert steppe and maize planting. Network relationships further characterized the process of niche sharing/competition between bacteria and fungi, and land use deepened the complexity of the co-occurrence network, with soil fungi occupying more ecological niches in shrub cultivation than in grassland and maize cultivation. In addition, saprophytic fungi are the main functional fungi in bacterial-fungal co-occurrence networks and are influenced by soil properties. Random forest analysis further explained the importance of saprophytic fungi in response to environmental changes in desert steppes. The enrichment of soil saprophytic fungi had an obvious correlation with soil aggregate stability, soil physical properties, and soil nutrients, especially in the background of shrub forestation in desert steppes. This study further highlights afforestation as one of the ways to benefit land management in desert steppes, while emphasizing the importance of saprophytic fungi as ecosystem indicators.

In the field of machine learning, the multi-category classification problem plays a crucial role. Solving the problem has a profound impact on driving the innovation and development of machine learning techniques and addressing complex problems in the real world. In recent years, researchers have begun to focus on utilizing quantum computing to solve the multi-category classification problem. Some studies have shown that the process of processing information in the brain may be related to quantum phenomena, with different brain regions having neurons with different structures. Inspired by this, we design a quantum multi-category classifier model from this perspective for the first time. The model employs a heterogeneous population of quantum neural networks (QNNs) to simulate the cooperative work of multiple different brain regions. When processing information, these heterogeneous clusters of QNNs allow for simultaneous execution on different quantum computers, thus simulating the brain’s ability to utilize multiple brain regions working in concert to maintain the robustness of the model. By setting the number of heterogeneous QNN clusters and parameterizing the number of stacks of unit layers in the quantum circuit, the model demonstrates excellent scalability in dealing with different types of data and different numbers of classes in the classification problem. Based on the attention mechanism of the brain, we integrate the processing results of heterogeneous QNN clusters to achieve high accuracy in classification. Finally, we conducted classification simulation experiments on different datasets. The results show that our method exhibits strong robustness and scalability. Among them, on different subsets of the MNIST dataset, its classification accuracy improves by up to about 5% compared to other quantum multiclassification algorithms. This result becomes the state-of-the-art simulation result for quantum classification models and exceeds the performance of classical classifiers with a considerable number of trainable parameters on some subsets of the MNIST dataset.

There have been numerous quantum neural networks reported, but they struggle to match traditional neural networks in accuracy. Given the huge improvement of the neural network models’ accuracy by two-dimensional tensor network (TN) states in classical tensor network machine learning (TNML), it is promising to explore whether its application in quantum machine learning can extend the performance boundary of the models. Here, we transform two-dimensional TNs into quantum circuits for supervised learning. Specifically, we encode two-dimensional TNs into quantum circuits through rigorous mathematical proofs for constructing model ansätze, including string-bond states, entangled-plaquette states and isometric TN states. In addition, we propose adaptive data encoding methods and combine with TNs. We construct a tensor-network-inspired quantum circuit (TNQC) supervised learning framework for transferring TNML from classical to quantum, and build several novel two-dimensional TN-inspired quantum classifiers based on this framework. Finally, we propose a parallel quantum machine learning method for multi-class classification to construct 2D TNQC-based multi-class classifiers. Classical simulation results on the MNIST benchmark dataset show that our proposed models achieve the state-of-the-art accuracy performance, significantly outperforming other quantum classifiers on both binary and multi-class classification tasks, and beat simple convolutional classifiers on a fair track with identical inputs. The noise resilience of the models makes them successfully run and work in a real quantum computer.

With the wide application of drone technology, there is an increasing demand for the detection of radar return signals from drones. Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition. This method suffers from the problem of large dimensionality of image features, which leads to large input data size and noise affecting learning. Therefore, this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512 × 4 to 16 dimensions. However, the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease, so we propose a new hybrid quantum neural network with signal feature overlay projection (HQNN-SFOP), which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal, introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features, and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features, thus improving the accuracy and migration generalization ability of drone detection. In order to validate the effectiveness of the proposed method, we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment. The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB. On the time-domain feature data set, HQNN-SFOP obtains the highest accuracy compared to other conventional methods. In addition, HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions. Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing, it provides a highly efficient method for the drone detection using radar return signals. [ABSTRACT FROM AUTHOR]

The present study investigates the equivalent bulk modulus and density of square frame underwater acoustic metafluid (AMF) cells with four distinct frame forms. It is observed that, besides frame thickness, a certain degree of shrinkage at the center of the frame also influences the equivalent parameters of cells. Specifically, this shrinkage leads to a decrease in the equivalent volume modulus and an increase in the equivalent density, ultimately resulting in the slow sound velocity phenomenon of the cell. The frame shrinkage causes part of the air to be replaced, resulting in an increase in density. At the same time, it makes the cell more prone to "shrink and expand" type volume deformation, resulting in the reduction of volume modulus. Finally, an impedance-matched slow sound velocity (0.5 times) metafluid cell is designed by frame shrinkage and verified by analyzing the transmission effect of a waveguide containing 20 cells and the sound pressure distribution at different positions in the waveguide. [ABSTRACT FROM AUTHOR]

Soil microbial taxa have different functional ecological characteristics that influence the direction and intensity of plant–soil feedback responses to changes in the soil environment. However, the responses of soil microbial survival strategies to wet and dry events are poorly understood. In this study, soil physicochemical properties, enzyme activity, and high–throughput sequencing results were comprehensively anal0079zed in the irrigated cropland ecological zone of the northern plains of the Yellow River floodplain of China, where Oryza sativa was grown for a long period of time, converted to Zea mays after a year, and then Glycine max was planted. The results showed that different plant cultivations in a paddy–dryland rotation system affected soil physicochemical properties and enzyme activity, and G. max field cultivation resulted in higher total carbon, total nitrogen, soil total organic carbon, and available nitrogen content while significantly increasing α–glucosidase, β–glucosidase, and alkaline phosphatase activities in the soil. In addition, crop rotation altered the r/K–strategist bacteria, and the soil environment was the main factor affecting the community structure of r/K–strategist bacteria. The co–occurrence network revealed the inter–relationship between r/K–strategist bacteria and fungi, and with the succession of land rotation, the G. max sample plot exhibited more stable network relationships. Random forest analysis further indicated the importance of soil electrical conductivity, total carbon, total nitrogen, soil total organic carbon, available nitrogen, and α–glucosidase in the composition of soil microbial communities under wet–dry events and revealed significant correlations with r/K–strategist bacteria. Based on the functional predictions of microorganisms, wet–dry conversion altered the functions of bacteria and fungi and led to a more significant correlation between soil nutrient cycling taxa and environmental changes. This study contributes to a deeper understanding of microbial functional groups while helping to further our understanding of the potential functions of soil microbial functional groups in soil ecosystems.

Sodium bicarbonate stress caused by NaHCO3 is one of the most severe abiotic stresses affecting agricultural production worldwide. However, little attention has been given to the molecular mechanisms underlying plant responses to sodium bicarbonate stress. To understand phosphorylation events in signaling pathways triggered by sodium bicarbonate stress, TMT-labeling-based quantitative phosphoproteomic analyses were performed on soybean leaf and root tissues under 50 mM NaHCO3 treatment. In the present study, a total of 7856 phosphopeptides were identified from cultivated soybeans (Glycine max L. Merr.), representing 3468 phosphoprotein groups, in which 2427 phosphoprotein groups were newly identified. These phosphoprotein groups contained 6326 unique high-probability phosphosites (UHPs), of which 77.2% were newly identified, increasing the current soybean phosphosite database size by 43.4%. Among the phosphopeptides found in this study, we determined 67 phosphopeptides (representing 63 phosphoprotein groups) from leaf tissue and 554 phosphopeptides (representing 487 phosphoprotein groups) from root tissue that showed significant changes in phosphorylation levels under sodium bicarbonate stress (fold change >1.2 or p < 0.05). Localization prediction showed that most phosphoproteins localized in the nucleus for both leaf and root tissues. GO and KEGG enrichment analyses showed quite different enriched functional terms between leaf and root tissues, and more pathways were enriched in the root tissue than in the leaf tissue. Moreover, a total of 53 different protein kinases and 7 protein phosphatases were identified from the differentially expressed phosphoproteins (DEPs). A protein kinase/phosphatase interactor analysis showed that the interacting proteins were mainly involved in/with transporters/membrane trafficking, transcriptional level regulation, protein level regulation, signaling/stress response, and miscellaneous functions. The results presented in this study reveal insights into the function of post-translational modification in plant responses to sodium bicarbonate stress.

The stellar refraction indirect horizon sensing positioning method has the characteristics of strong auto-nomy, high accuracy and low cost, which is a new celestial navigation method with great application prospect. At pre-sent, the traditional stellar refraction indirect horizon sensing positioning method is a celestial positioning method based on the carrier outside the atmosphere. However, most of carriers mainly work in the atmosphere. In order to analyze the feasibility of this stellar refraction positioning method on the carrier inside the atmosphere, the starlight atmospheric refraction model is established based on the refraction law and spherical atmospheric model, and the working mechanism of stellar refraction indirect horizon sensing positioning is analyzed according to the relationship between the stellar refraction angle and the carrier position. Then, the feasibility of the application of the stellar refraction indirect horizon sensing positioning method inside the atmosphere is demonstrated and analyzed by quantitatively analyzing the refraction angle deviation of the carrier inside and outside the atmosphere. The results show that the conventional stellar refraction positioning method is only applicable to the carrier flying above 40 km. If the rough height of the carrier can be obtained by altimeter, the stellar refraction indirect horizon sensing positioning method can be applied to the carrier with a flying altitude of 20~40 km inside the atmosphere.

Quercus wutaishanica is the dominant tree species in the natural ecosystem restoration of temperate forests in China, and it plays an active role in maintaining ecological balance. However, little is known about how ecosystem versatility develops during the restoration of forest ecosystems dominated by Q. wutaishanica. In this study, we investigated the species composition of the Q. wutaishanica community, soil nutrients, and their functional traits at various restoration stages, and comprehensively analyzed the correlations among them. At the early stage of restoration (10 years of restoration), there were Spiraea pubescens and Syringa pubescens in Q. wutaishanica community (87% of the total species), while had a larger niche width. In the middle of restoration (30 years of restoration), shannon and evenness indices were the largest, while soil total carbon, ammonium nitrogen and chlorophyll content of Q. wutaishanica leaves were the highest; among them, soil total carbon was 15.7% higher than that in 10 years of restoration, 32.4% higher than that in 40 years of restoration, ammonium nitrogen was 71.7% higher than that in 40 years of restoration, and chlorophyll content was 217.9% higher than that in 10 years of restoration, and 51.8% higher than that in 40 years of restoration. At the later stage of restoration (40 years of restoration), Lonicera ferdinandii occupied the dominant ecological niche, and soil available nitrogen, available phosphorus content and leaf thickness were the largest; while AN was 10.9% higher than that of 10 years of restoration, 16.5% higher than that of 30 years of restoration, AP was 60.6% higher than that of 10 years of restoration, 21.6% higher than that of 30 years of restoration, leaf thickness was 22.3% higher than that of 10 years of restoration, 84.9% higher than that of 30 years of restoration. However, the restriction of various soil nutrients was reduced. Our study highlighted the effectiveness of soil resource availability in plant communities during restoration, reduced competition for light among plants, and altered species richness. Furthermore, changes in the interrelationship between plant community composition and leaf functional traits of the dominant species responded positively to community restoration. These results further deepen our understanding of forest management and restoration of forest communities. In the future, it is necessary to comprehensively consider the influence of various factors on forest community restoration.

Soil microbes act as “players” in regulating biogeochemical cycles, whereas environmental heterogeneity drives microbial community assembly patterns and is influenced by stochastic and deterministic ecological processes. Currently, the limited understanding of soil microbial community assembly patterns and interactions under temperate forest stand differences pose a challenge in studying the soil microbial involvement during the succession from coniferous to broad-leaved forests. This study investigated the changes in soil bacterial and fungal community diversity and community structure at the regional scale and identified the pathways influencing soil microbial assembly patterns and their interactions. The results showed that broad-leaved forest cover in temperate forests significantly increased soil pH, and effectively increased soil water content, total carbon (TC), total nitrogen (TN), and total phosphorus (TP) contents. Both soil bacterial and fungal alpha diversity indices were correlated with soil physicochemical properties, especially in broad-leaved forest. The bacterial and fungal community composition of coniferous forest was dominated by deterministic process (bacteria: 69.4%; fungi: 88.9%), while the bacterial community composition of broad-leaved forest was dominated by stochastic process (77.8%) and the fungal community composition was dominated by deterministic process (52.8%). Proteobacteria, Acidobacteriota, Actinobacteriota, and Verrucomicrobiota were the dominant phyla of soil bacterial communities in temperate forests. Whereas Ascomycota, Mortierellomycota, Basidiomycota, and Rozellomycota were the dominant phyla of soil fungal communities in temperate forests. Most members of dominant phylum were regulated by soil physical and chemical properties. In addition, the succession from temperate coniferous forest to broad-leaved forest was conducive to maintaining the complex network of soil bacteria and fungi, and the top 20 degree of the major taxa in the network reflected the positive response of microbial interactions to the changes of soil nutrients during forest succession. This study not only shows the mechanism by which species differences in temperate forests of northern China affect soil microbial community assembly processes, but also further emphasizes the importance of the soil microbiome as a key ecosystem factor through co-occurrence network analysis.

Salt stress is one of the most devastating environmental factors threatening soybean growth and yield. However, the molecular link between salt stress and epigenetics has not been well-elucidated in soybean. In this study, from the wild soybean cDNA library, we isolated a GsSnRK1 kinase interacting protein (GsMSTY1) which is phylogenetically homologous with histone acetyltransferase MYST family with unknown function. GsMSTY1 gene is dominantly expressed in wild soybean roots and is highly responsive to abiotic stresses. GsMYST1 was able to be phosphorylated at the Ser44 site by GsSnRK1 and demonstrated in vivo acetyltransferase activity in transgenic soybean roots revealed by an anti-H4ace antibody. A transcription factor protein GsNAC83 was identified to interact with both GsMYST1 and GsSnRK1, and GsNAC83 could recruit the GsMYST1-GsSnRK1 module to COR15B gene promoter determined by ChIP-qPCR assay. To dissect the molecular functions of this ternary complex, we treated the transgenic soybean roots with salt stress and found that the stress could activate GsSnRK1, and the activated GsSnRK1 subsequently phosphorylated GsMYST1 to enhance its acetyltransferase activity which may epigenetically promote the target gene expression. To explore the physiological functions, we coexpressed GsSnRK1 and GsMYST1 genes in soybean hairy roots and found that only GsSnRK1(wt)/GsMYST1(wt) but not the mutant genes could promote soybean resistance to salt stress, implicating that phosphorylation of GsMYST1 is required for it to acetylate histone H4 on the target genes to upregulate expression of the stress-related genes. Our data shed new light on the functions of the GsSnRK1-GsMYST1-GsNAC83 module and its regulatory mechanism on plant tolerance to abiotic stresses.

The K-nearest neighbor (KNN) algorithm is one of the most extensively used classification algorithms, while its high time complexity limits its performance in the era of big data. The quantum K-nearest neighbor (QKNN) algorithm can handle the above problem with satisfactory efficiency; however, its accuracy is sacrificed when directly applying the traditional similarity measure based on Euclidean distance. Inspired by the Polar coordinate system and the quantum property, this work proposes a new similarity measure to replace the Euclidean distance, which is defined as Polar distance. Polar distance considers both angular and module length information, introducing a weight parameter adjusted to the specific application data. To validate the efficiency of Polar distance, we conducted various experiments using several typical datasets. For the conventional KNN algorithm, the accuracy performance is comparable when using Polar distance for similarity measurement, while for the QKNN algorithm, it significantly outperforms the Euclidean distance in terms of classification accuracy. Furthermore, the Polar distance shows scalability and robustness superior to the Euclidean distance, providing an opportunity for the large-scale application of QKNN in practice.

SnRK1 protein kinase plays hub roles in plant carbon and nitrogen metabolism. However, the function of SnRK1 in legume nodulation and symbiotic nitrogen fixation is still elusive. In this study, we identified GmNodH, a putative sulfotransferase, as an interacting protein of GmSnRK1 by yeast two-hybrid screen. The qRT-PCR assays indicate that GmNodH gene is highly expressed in soybean roots and could be induced by rhizobial infection and nitrate stress. Fluorescence microscopic analyses showed that GmNodH was colocalized with GsSnRK1 on plasma membrane. The physical interaction between GmNodH and GmSnRK1 was further verified by using split-luciferase complementary assay and pull-down approaches. In vitro phosphorylation assay showed that GmSnRK1 could phosphorylate GmNodH at Ser193. To dissect the function and genetic relationship of GmSnRK1 and GmNodH in soybean, we co-expressed the wild-type and mutated GmSnRK1 and GmNodH genes in soybean hairy roots and found that co-expression of GmSnRK1/GmNodH genes significantly promoted soybean nodulation rates and the expression levels of nodulation-related GmNF5α and GmNSP1 genes. Taken together, this study provides the first biological evidence that GmSnRK1 may interact with and phosphorylate GmNodH to synergistically regulate soybean nodulation.

Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.

Soil organic carbon (SOC) plays a crucial role in controlling the nitrate-dependent Fe(II) oxidation (NDFO) process, especially for saline-alkaline soils. The effects of straw returning combined with Nitrogen (N) fertilizer application on soil NO3−-N content, Fe(II) form and nirK genes in saline-alkaline soil were studied in a five-year field experiment to explore the regulatory mechanism of SOC on NDFO process. Six treatments were designed with two factors (1) three straw returning rates (C0, C1 and C2, which was 0, 4500 and 9000 kg C ha−1, respectively) and (2) two N fertilization rates (N1 and N2, which was 255 and 400 kg N ha−1, respectively). Under both N levels, compared with C0 and C2 rates, NO3−-N content was increased by 65% and 50% in C1 rate, respectively. NirK genes were decreased with straw returning, in which they were 42.9–58.8% lower in C1 and C2 treatments than that in C0 treatment, respectively. In the N1C1 treatment, the increase of SOC reduced the denitrification by converting aqueous Fe(II) (Fe(II)aq) into Fe(II)-OM complexes and reducing the abundance of nirK genes. Overall, appropriate straw returning (C1) under optimal N fertilization rate (N1) could reduce N loss by decreasing the NDFO process in saline-alkaline paddy soils.

Astragalus galactites is a medicinal plant. The total plastome length of A. galactites is 126,117 bp. It contains a large single-copy region of 69,805 bp, two inverted repeat regions of 20,638 bp, and a small single-copy region of 15,036 bp. The cp genome contains 110 complete genes, including 75 protein-coding genes (75 PCGs), 4 ribosomal RNA genes (4 rRNAs), and 30 tRNA genes (30 tRNAs). The overall GC content of cp DNA is 33.9%, the corresponding values of the LSC, SSC, and IR regions are 33.0%, 30.4%, and 43.3% respectively. The phylogenetic tree shows that A. galactites has the closest relationship with A. laxmannii.

Characterizing the spatiotemporal variability of the East Asian summer monsoon (EASM) advances our understanding of its rhythm, dynamics, and future impacts. East Asian summer monsoon variations during the Holocene have been reconstructed from a variety of geological archives and proxies. However, the spatiotemporal heterogeneity of EASM rainfall during the Holocene remains controversial. Taiwan is geographically suitable for studying the EASM history, through its geological archives. Herein, we synthesize the reported lake and peat sedimentary records of the entire Holocene, in addition to the records of mass-wasting and on-land deposition from cores collected from Taiwan to illustrate the EASM induced hydroclimate changes in Taiwan throughout the Holocene. Records from Taiwan indicate that the EASM rainfall maximum occurred during the early Holocene, concurring with other EASM records from monsoon regions in southern China. We suggest that the early Holocene EASM rainfall maximum in southern China was mainly forced by the higher Northern Hemisphere summer insolation and sea surface temperatures (SSTs) in the Western Pacific Warm Pool (WPWP). A synthesis of EASM rainfall records from across China shows that the timing of the Holocene EASM rainfall maximum occurred progressively later than that from southern to northern China. This time-transgressive EASM rainfall maximum may be due to the latitudinal shift of the westerlies and Western Pacific subtropical high (WPSH) that was induced by changes of interhemispheric temperature gradients (ΔTN–S) and the northern high latitude ice volume. Moreover, records from Taiwan suggest a significant collapse of the EASM in Taiwan at ∼4–2 ka BP. Based on the records from Taiwan, coastal East Asia, and the Tropical Pacific, we propose that the SSTs of the WPWP and/or El Niño-Southern Oscillation activity may have exerted a strong influence on the EASM rainfall changes during the late Holocene. Moreover, increased EASM rainfall in southern China during the last 2 ka was likely caused by a southward shift of WPSH, which is associated with gradual decreases in ΔTN–S during the late Holocene.

Magnesium (Mg) deficiency in greenhouse vegetable plants affects crop yields and quality. Assessing the degree of Mg deficiency and influencing factors is necessary for evidence-based Mg supplementation. In this study, an investigation was conducted at 71 tomato cultivation greenhouses with calcareous soil, located in the main greenhouse vegetable production area of China. Plant and soil Mg contents were analysed, as well as nitrogen (N), phosphorus (P), potassium (K) and other nutrients. The tomatoes showed Mg deficiency, with none of the farms supplementing this element. The critical leaf Mg concentration of the tomatoes was 3.40 mg kg-1. Using Ward's hierarchical cluster analysis, three clusters of Mg leaf content were obtained, which were 1.07-3.00 mg kg-1 (SD cluster), 2.56-5.64 mg kg-1 (MD cluster) and 5.12-6.88 mg kg-1 (ND cluster). Tomato yields in the SD and MD clusters were 31.4% and 16.5% lower than the ND cluster, respectively. Both soil exchangeable Mg content and soil pH in the SD cluster were the lowest, while total NPK, available NPK, K/Mg and the Ca/Mg ratio were the highest. A principal component analysis (PCA) showed that Mg leaf content was negatively correlated with the exchangeable K/Mg ratio, and positively correlated with soil exchangeable Mg content, soil pH and tomato yield. Mg deficiency in the tomatoes was attributed to the high accumulation of available K in the calcareous soil and the fact that the SD cluster with greatest Mg deficiency also had the lowest soil total Mg concentration. Considering the accumulation of NPK and the causes of Mg deficiency, foliar Mg spraying is suggested to mitigate Mg deficiency in calcareous greenhouse tomato soil. [ABSTRACT FROM AUTHOR]

Acoustic metamaterials are materials with artificially designed structures, which have characteristics that surpass the behavior of natural materials, such as negative refraction, anomalous Doppler effect, plane focusing, etc. This article mainly introduces and summarizes the related research progress of acoustic metamaterials in the past two decades, focusing on meta-atomic acoustic metamaterials, metamolecular acoustic metamaterials, meta-atomic clusters and metamolecule cluster acoustic metamaterials. Finally, the research overview and development trend of acoustic metasurfaces are briefly introduced.

Dominantly inherited mutations in leucine-rich repeat kinase 2 (LRRK2) are the most common causes of familial Parkinson's disease (PD) and LRRK2 polymorphisms are associated with increased risk for idiopathic PD. However, the molecular mechanisms by which these mutations cause PD remain uncertain. In vitro studies indicate that disease-linked mutations in LRRK2 increase LRRK2 kinase activity and LRRK2-mediated cell toxicity. Identifying LRRK2-interacting proteins and determining their effects on LRRK2 are important for understanding LRRK2 function and for delineating the pathophysiological mechanisms of LRRK2 mutations. Here we identified a novel protein, F-box and leucine-rich repeat domain-containing protein 18 (Fbxl18) that physically associates with LRRK2. We demonstrated that Fbxl18 is a component of a Skp1-Cullin1-F-box ubiquitin ligase complex that regulates the abundance of LRRK2 by selectively targeting phosphorylated LRRK2 for ubiquitination and proteasomal degradation. Knockdown of endogenous Fbxl18 stabilized LRRK2 abundance while protein kinase C activation enhanced LRRK2 degradation by Fbxl18. Dephosphorylation of LRRK2 blocked Fbxl18 association with LRRK2. Taken together, we have identified potential mechanisms for LRRK2 regulation by kinase signaling pathways. Furthermore, Fbxl18 prevented caspase activation and cell death caused by LRRK2 and PD-linked mutant LRRK2. This reveals novel targets for developing potential therapies for familial and idiopathic PD.