The immobile lifestyle of plants determines their strong environmental adaptability. There fore, plants need to continuously modulate their metabolism to adapt to different light conditions. Sucrose non-fermenting 1-related protein kinase 1 ( SnRK1) in plant cells can be activated by energy stress in plants, regulating a series of biochemical reactions of carbon and nitrogen metabolism to adapt to energy stress. This review systematically summarizes the metabolic pathways regulated by SnRK1 under energy stress conditions through transcriptional regulation and post-translational phos-phorylation modification, and concludes the general rules of SnRK1 function for carbon and nitrogen metabolism, including SnRK1 inhibits sucrose synthesis and photosynthetic efficiency; SnRK1 represses nitrogen assimilation and nitrogen signal transduction, coordinating a carbon and nitrogen balance; SnRK1 promotes gluconeogenesis to maintain sugar metabolism homeostasis; as well as, SnRK1 pro motes autophagy and amino acid oxidation metabolism. These summarized results indicate that SnRK1 is the core regulatory element of plant carbon and nitrogen metabolism regulation, which will be a useful reference for functional analysis of SnRK1 in crops.

Soil salinity is one of the major environmental challenges facing global agriculture today, and it is of great importance to conduct research on salt stress response mechanisms in order to make plants grow better. The plant specific homologous structural domain leucine zipper ( HDZIP) family transcription factor HAT1 regulates multiple stress responses in Arabidopsis, and so far, it is not clear whether HAT1 is involved in regulating plant responses to salt stress. This paper focuses on the regulatory role of HAT1 in response to salt stress in Arabidopsis, with the aim of expanding the molecular regulatory network of salt stress. This study demonstrates that the EIN3HAT1CSDs molecular module regulates the plant response to salt stress. Salt stress induces the accumulation of EIN3, which inhibites the expression of HAT1, thereby weakening the transcriptional inhibition of HAT1 on downstream genes CSD1 and CSD2 and activating the antioxidant system to remove excess reactive oxygen species to enhance the salt tolerance of plants. This signaling pathway provides a new potential target for improving plant salt tolerance through molecular breeding.

Peptide hormones play indispensable roles in regulating plant growth, development, and environmental adaptation. GLVs peptide family has been demonstrated to participate in regulating various processes such as root meristem development, root gravitropism, lateral root initiation, and plant immunity. However, it remains unclear whether members of GLVs family are also involved in regulating the development of aerial parts of a plant. We examined the expression patterns of all 11 GLV genes, and found that some members are expressed in leaves, with GLV2 showing more expression than other GLVs. Using a CRISPRCas9 gene editing approach, we generated a glv2 mutant. Com pared to the wild type, glv2 exhibited curled and elongated leaf phenotypes. Detailed examinations via histological sections and scanning electron microscopy analyses revealed that leaf curling in the glv2 mutant primarily resulted from the abnormal development of the spongy tissues. Existing researches demonstrate that GLV family members act primarily as the ligands of the receptor kinase family, RGIs. We also found that most RGI family members are expressed in leaves. A quintuple mutant of RGIs exhibited abnormal leaf curl phenotype similar to glv2. Qrt-PCR analysis identified a number of auxin-related genes which are up regulated in glv2 leaves, suggesting the potential activation of auxin signaling as a contributing factor to the altered leaf morphology in the glv2 mutant. In summary, our results suggest that GLV2, likely perceived by RGIs, modulates plant leaf mesophyll cell development by regulating auxin signaling, thereby influencing leaf morphogenesis.

Since late 2019, the widespread outbreak of the novel coronavirus has had a severe impact on public health and social order. Machine learning based prediction methods have the capability to determine the infectivity phenotype and pandemic risk of coronaviruses. Presently, six classes of coronaviruses that infect humans have been identified. These viruses exhibit significant differences in their genomic sequences, and the continuous genetic variation in these viruses has resulted in a decline in the performance of machine learning models, potentially causing issues related to learned forgetting. This study, based on an incremental learning model framework, employed a One class SVM algorithm for continuous discrimination of novel coronavirus subgroups. Furthermore, a combined strategy of parameter sharing and knowledge distillation to adapt a backpropagation ( BP) neural network for continuous learning and prediction of the human infecting phenotype of coronaviruses was employed. The results indicate that the One class SVM, with a combination of balancing parameters v at 0.92, 0. 81, 0.24, 0.11, 0.55, and 0. 2, achieved the optimal classification performance for the six virus classes. It was found that the prediction model achieved the best performance when the number of hidden layer nodes was increased to 6, with a maximum Index of Agreement ( IAC) value of 0 903 5 and a maxi mum Bias Total ( BT) value of – 0.039 9. This effectively suppressed the learning amnesia trend in the network model, with the model’s predictive performance being close to that of joint data training( IAC: 0 923 6 ) . This performance was significantly better than that of neural networks without knowledge distillation ( IAC: 0.776 4) . Moreover, in comparison to other incremental methods, our approach outperformed sample-based methods such as ESRIL ( IAC: 0.866 2) and model parameter based methods like CCLL ( IAC: 0. 885 3) . This research holds important implications for public health applications.

The influenza virus genome consists of eight genetic segments of varying lengths, with a total ength of approximately 14 ~ 16 kb. Due to the special molecular genetic mechanism of the virus, it undergoes rapid mutations through gene point mutation and genome rearrangement, which leads to changes in its biological infection characteristics and poses a continuous threat to health. Therefore, accurate prediction of natural avian influenza virus spillovers is crucial for public health. This paper, employs a combination of convolutional neural network ( CNN) and recurrent neural network ( RNN) to represent viral genome sequences. The model’s transferability on both specific group datasets and entire datasets was evaluated. The experimental results demonstrate excellent prediction performance of the specific group model on the respective datasets, with AUROC exceeding 0 966 and AUPR values surpassing 0 876. However, its generalization ability is limited. Conversely, except for the H9N2 group, the global model performs well with AUROC and AUPR values reaching 1.000 across all groups. Based on ablation experiments, it was found that attention mechanism and sequence embed ding method significantly impact model performance while further testing its generalization ability reveals AUROC values above 0 984 and AUPR values over 0 941 for transfer predictions respectively. Visualizing the attention weight matrix provides biological interpretability for the model. The high performing deep learning prediction model can be effectively utilized for early warning systems against cross species infections caused by avian influenza viruses.

With the rapid development of quantum computers, post-quantum cryptography has become a research hotspot. Lattice cryptography has become the mainstream in post-quantum cryptography due to its balanced performance, solid security foundation, and rich functions. Pre-image sampling is the core algorithm in lattice cryptography and is widely used in the construction of many advanced cryptography schemes. Hash-and-Sign digital signature on lattice is its simplest and most direct application. Technically, pre-image sampling algorithms are divided into GPV and Peikert. The former is characterized by high output quality, but the algorithm can usually only be executed serially; the latter sup ports parallel operations, but the output quality is poor. This article applies non-spherical Gaussian technology to the Peikert sampling algorithm on the NTRU lattice, aiming to improve its efficiency. Specifically, two parameter modes were selected. Compared with the Peikert sampling algorithm on the original NTRU lattice, mode 1 can improve the security strength of digital signatures based on this sampling algorithm and reduce the size of the signature; mode 2 does not reduce security. Under the premise, the signature size can be further reduced. Experimental results show that in mode 1, the security is improved by about 18% ~ 20% and the signature size is reduced by about 15% ; in mode 2,the security remains unchanged, but the signature size is reduced by about 30% ~ 35% .

UAV-aided communication; wireless-powered communication; convex optimization; energy harvesting

In this work, Polyvinyl alcohol ( PVA) was crosslinked via Boric acid( H3 BO3 ) and prepared to be the hydrogel film. MPP is used to improve the flame retardant properties of the PVA hydrogel films. The experimental results showed that the PVA / MPP hydrogel films could achieve UL94 V0 rating with a 20 wt% addition of MPP which could effectively reduce the HRR and the smoke emission of the PVA hydrogel films. The flame retardant mechanism of the PVA / MPP hydrogel films is a synergistic effect of gasphase with condensed phase flame retardant mechanism. MPP decomposed into melamine and phosphoric acid, then melamine converted to N2 and CO2; Phosphoric acid promoted the PVA hydrogels formation of a continuous and dense char layer which hinders the exchange of oxygen and heat with the outside. At the same time, because of the interaction be tween MPP and PVA, the mechanical properties of PVA are maintained while the flame retardancy of PVA is improved.

Parametric level set topology optimization addresses numerical complexities associated with traditional level set methods. Since topological optimization using level sets requires introducing more advanced functions to construct topological models, most research on parametric level set topology optimization has been limited to two dimensions. This article extends the foundation of two-dimensional parametric level set topology optimization to three dimensions. By incorporating the concept of point clouds, it tackles the representation challenges of three-dimensional topological configurations. The algorithm is validated through typical numerical examples involving cantilever beams and simply supported beams, demonstrating its ability to address issues that arise when introducing higher-dimensional functions in three-dimensional level sets. Additionally, the article discusses the application of the SIMP ( Solid Isotropic Material with Penalization) multi-material interpolation model for multi-material topology optimization, providing valuable insights for further research in this field.

This paper discusses the utilization of computer vision technology to capture a series of structural vibration images. The resulting sub-pixel vibration displacement of the monitoring nodes are then obtained by Digital Image Correlation ( DIC) technology, the fast Bayesian FFT method is then adopted to identify the dynamic modal parameters of the tested structure. To assess the precision and reliability of the dynamic modal parameter identified for a structure, a combination of computer vision based vibration test and fast Bayesian FFT methodology is conducted in this paper. A 5 6 m steel truss is adopted as an example to extract its vibration displacement data from captured videos for the undamaged and other 5 damaged conditions of the test structure. The size of the errors and reasons in the identified displacement of monitoring nodes at various locations in the truss are analyzed in this study. Additionally, the fast Bayesian FFT method is adopted to evaluate the accuracy and uncertainty of the identified dynamic modal parameters by investigating the vibration video data extracted from different monitoring points in the tested structure. The results from the proposed method show that the utilization of DIC technology and fast Bayesian FFT can accurately identify dynamic modal parameters of the test structure.