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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 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.

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.