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Abstract Background Phosphorus-solubilizing bacteria (PSB) are vital in converting insoluble phosphorus into a soluble form that plants can readily absorb and utilize in soil. While previous studies have mainly focused on the extracellular secretion of microorganisms, few have explored the intricate intracellular metabolic processes involved in PSB-mediated phosphorus solubilization. Results Here, we uncovered that Ca3(PO4)2 could serve as a source of insoluble phosphorus for the PSB, Pseudomonas sp. NK2. High-performance liquid chromatography (HPLC) results indicated higher levels of organic acids released from insoluble phosphorus compared to a soluble phosphorus source (KH2PO4), with acetic acid released exclusively under insoluble phosphorus condition. Moreover, non-target metabolomics was employed to delve into the intracellular metabolic profile. It unveiled that insoluble phosphorus significantly enhanced the tricarboxylic acid cycle, glycolysis, glyoxylic acid metabolism, and other pathways, leading to the production of acetic acid, gluconic acid, oxalic acid, and citric acid for insoluble phosphorus solubilization. In our quest to identify suitable biochar carriers, we assessed seven types of biochar through the conjoint analysis of NBRIP medium culture and application to soil for 30 days, with cotton straw-immobilized NK2 emerging as the most potent phosphorus content provider. Lastly, NK2 after cotton straw immobilization demonstrated the ability to enhance biomass, plant height, and root development of Solanum lycopersicum L. cv. Micro Tom. Conclusions Pseudomonas sp. NK2 with cotton straw biochar could enhance phosphorus availability and tomato growth. These findings bear significant implications for the practical application of phosphorus-solubilizing bacteria in agricultural production and the promotion of environmentally sustainable farming practices.

In terms of payload capacity and visual effects, the existing image steganography technology based on deep neural networks still needs improvement, to solve this problem, this article proposes a new deep convolutional steganography network based on the pyramid pooling module to achieve better image steganography. The deep convolutional neural network itself can extract features efficiently. Based on the combination of up-sampling structure, we added a pyramid pool module, under the premise of ensuring safety, fully integrated the previous important global features, achieved good hiding and extraction effects, fully integrated the previous important global features, and effective it reduces the loss of contextual information between different sub-regions in the feature extraction process and achieves better hiding and extraction effects under the premise of ensuring security. Experiments show that the average peak signal-to-noise ratio (PSNR)/structure similarity (SSIM) and other indicators between the images obtained by this method have achieved good results in the experiment. Also, we have verified through ablation experiments that the pyramid pooling module can enhance the steganography effect of the network model and can further cut down the loss function of the model.

Aiming at the problem that the traditional steganography based on carrier modification has the low steganographic capacity, a steganographic scheme based on Fully Convolutional Dense Connection Network (FC-DenseNet) is proposed. Since FC-DenseNet can effectively overcome the problems of gradient dissipation and gradient explosion, and a large number of features are multiplexed, the cascaded secret image and carrier image can reconstruct good image quality after entering the network. Effectively improve steganographic capacity. First, we reset the number of input channels of the first convolution block of FC-DenseNet and the number of output channels of the last convolution block and deleted the LogSoftmax() function. On the sender side, after the concatenated secret image and carrier image pass through the hidden network FC-DenseNet, the secret image is embedded in the carrier image to obtain a stego-image. At the receiving side, the extraction network reconstructs the secret image from the stego-image. Experimental results show that our proposed steganography scheme not only has a high Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity(SSIM) but also can realize large-capacity image steganography, with an average image payload capacity of 23.96 bit per pixel.