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Background Drought and nitrogen (N) deficiency are two major limiting factors for forest productivity in many ecosystems. Elucidating the mechanisms underlying the influence of soil N availability on drought responses of tree species is crucial to improve tree growth under drought. Results The root proliferation under drought was enhanced by adequate N application. Vessel frequency in xylem increased upon drought, with more significant increase under adequate N conditions compared with that under low N conditions, possibly leading to increased hydraulic safety. Nitrogen application under drought increased indole acetic acid (IAA), which contributed to the adaptive changes of xylem. Nitrogen application increased leaf abscisic acid (ABA) concentration, therefore regulated stomata adjustment, and promoted intrinsic water use efficiency ( WUE i ). Moreover, N application promoted antioxidant defense in leaves by showing increased level of free proline and carotenoid, which improved drought tolerance and growth performance of poplars. Conclusions Anatomical and physiological responses of Populus to drought were suppressed by N deficiency. Adequate N application promoted adaptive changes of root and xylem under drought and increased hydraulic safety. Nitrogen addition under drought also increased leaf ABA level which may regulate stomata adjustment and promote WUE i . Moreover, nitrogen application improved antioxidant defense in leaves with increased levels of antioxidants. These positive regulations improved drought tolerance and growth performance of poplars.

The regulatory mechanisms by which AMF modulate the integrated carbon (C)-nitrogen (N)-phosphorus (P) metabolic network in woody plant leaves remain unclear. We investigated how varying nitrate (NO3−) and phosphate (Pi) supply, with or without AMF inoculation, reshapes the leaf metabolic network in poplar seedlings. Key findings reveal that AMF acts as a central metabolic hub, optimizing C-N-P coordination in an environment-dependent manner. Under low Pi, NO3− supply enhanced P remobilization and photosynthetic efficiency, boosting growth. AMF further optimized low-Pi adaptation by promoting P storage and buffering, significantly improving photosynthesis and biomass. Under high Pi, NO3− supply shifted focus towards enhancing Rubisco-mediated carbon assimilation. AMF synergistically improved carbon assimilation efficiency and suppressed non-essential P recycling. N metabolism effects of Pi were contingent on NO3− availability, and AMF reprogrammed N assimilation pathways accordingly, balancing uptake and utilization under different N regimes. Critically, AMF orchestrated environment-specific metabolic adjustments, reinforcing P buffering and photosynthetic gain under Pi limitation, and enhancing C assimilation efficiency while minimizing P waste under Pi sufficiency. This study demonstrates that poplar leaf C-N-P networks are reconfigured through N-P synergisms modulated by AMF, positioning AMF as a pivotal integrator of nutrient acquisition and allocation. These insights provide a physiological foundation for developing efficient forestry nutrient management and mycorrhizal application strategies.

The jujube (Ziziphus jujuba Mill.) is a significant economic fruit tree, valued for its nutritional and medicinal properties. However, advances in functional genomics are hindered by the lack of an efficient transformation system. To overcome the limitations of conventional explant, we established a fruit-pulp-derived, callus-based Agrobacterium-mediated transformation system using fruit-pulp harvested 50 days after pollination. Through orthogonal experimental design, 6-benzylaminopurine and 2,4-dichlorophenoxyacetic acid were identified as key regulators for inducing high-quality, friable callus in two jujube genotypes, ‘JZ60’ and ‘LWCZ’. This system revealed significant genotype-specific variation in auxin requirements for callus proliferation and in differential antibiotic sensitivity. Transformation efficiency, as evaluated by fluorescence screening, was primarily determined by acetosyringone concentration and the binary vector architecture. The results revealed that the compact pCY (kanamycin resistance) vector achieved higher transformation efficiency (up to 77.8%) than pCAMBIA1301, whereas the pCAMBIA1301 (hygromycin resistance) vector enabled more uniform transgene expression. Integration and expression of the ZjCBF3 transgene were confirmed by polymerase chain reaction (PCR), reverse transcription quantitative PCR, and green fluorescent protein fluorescence assays. This study established a fruit-pulp-based callus transformation system for jujube, providing a rapid platform for its functional genomic studies.

In order to solve the problem of predicting the residual life of mechanical products accurately based on small-sample data, this paper proposes a small-sample adaptive residual life prediction model of mechanical products based on feature matching preprocessor-LSTM. First, aiming at the problem of low accuracy of remaining life prediction for small samples of mechanical products caused by multiple time scales and multiple fault states, the failure time data and performance degradation data are fused, and the failure rate and standard deviation are used as the remaining life prediction criteria to intuitively reflect The possibility of failure of a component or system at a certain point in time. Considering the demand of adaptive small-sample residual life prediction data, this paper establishes the adaptive matching pre-processor model of life characteristics. On this basis, the LSTM neural network is used to establish a small-sample adaptive residual life prediction model. Then, the XJTU-SY bearing life data set and the test data of the small-sample life characteristics measured by the RV reducer are used as the research objects, and a small amount of the data set is randomly selected. The remaining life expectancy is predicted from the sample data and compared with its standard remaining life, respectively. The comparison results show that the overall prediction error is small. This study shows that the remaining life prediction model established can better predict the remaining life of mechanical product sub-sample data and provides a feasible method for predicting the remaining life of mechanical product sub-samples.

Drought and nitrogen (N) deficiency are two major limiting factors for forest productivity in many ecosystems. Elucidating the mechanisms underlying the influence of soil N availability on drought responses of tree species is crucial to improve tree growth under drought. The root proliferation under drought was enhanced by adequate N application. Vessel frequency in xylem increased upon drought, with more significant increase under adequate N conditions compared with that under low N conditions, possibly leading to increased hydraulic safety. Nitrogen application under drought increased indole acetic acid (IAA), which contributed to the adaptive changes of xylem. Nitrogen application increased leaf abscisic acid (ABA) concentration, therefore regulated stomata adjustment, and promoted intrinsic water use efficiency (WUE.sub.i). Moreover, N application promoted antioxidant defense in leaves by showing increased level of free proline and carotenoid, which improved drought tolerance and growth performance of poplars. Anatomical and physiological responses of Populus to drought were suppressed by N deficiency. Adequate N application promoted adaptive changes of root and xylem under drought and increased hydraulic safety. Nitrogen addition under drought also increased leaf ABA level which may regulate stomata adjustment and promote WUE.sub.i. Moreover, nitrogen application improved antioxidant defense in leaves with increased levels of antioxidants. These positive regulations improved drought tolerance and growth performance of poplars.

Visible-thermal person re-identification (VT-ReID) is a challenging pedestrian retrieval problem in the field of security. Due to the intra-modality variations and cross-modality discrepancy caused by different spectrums, it is difficult to extract discriminative features. Existing works are devoted to projecting different-modality features into a shared space, which has weak discriminability and ignores the contextual relationship. In this paper, a novel dual-level information transfer framework is proposed to reduce the modality discrepancy in image level and feature level for VT-ReID. An auxiliary mix-modality is proposed and a mix-visible-thermal (MVT) learning strategy is built to reduce the discrepancy in image level. Firstly, the mix-modality is generated by a mixup scheme which alleviates the direct transfer. Secondly, under the MVT framework, we use ID loss and hetero center triplet loss to guide feature extraction for visible, thermal, and mixed modalities on a one-stream Network. To enhance the robustness of feature extraction, we introduce a graph information transfer module to transfer information across intra-modality and inter-modality in feature level. We build the agent node for modality by using the modality center, where the agent node aggregates the information of all samples in one modality, and then the information from one modality is transmitted to other modalities through the agent nodes. Extensive experimental results on SYSU-MM01 and RegDB datasets show that our method achieves excellent performance.

The domain adaptive person re-identification (Re-ID) has be more popular among researchers. Because it can save a lot of resources as it only exploits the source domain knowledge and does not need the complex annotation efforts in target domain. It aims to extend a model trained on a labeled dataset to another dataset which is unlabeled. Many works reduce feature distribution gap between two different datasets to solve the problem. However, these works ignore the problem which is the variations within an unlabeled dataset. In the paper, we propose a domain adaptive person Re-ID framework based on multiple granularity network and dynamic label (MGDL). Specifically, we send the images of two different datasets into multiple granularity network at the same time for joint training to reduce feature distribution gap which is between the two different datasets. The network is trained by two different kinds of pseudo labels, namely, conservative label and radical label. The two kinds of pseudo labels are used to alternating pull and push the feature distribution in the target domain to reduce the variations within an unlabeled dataset. Experiments have shown that the MGDL achieves competitive performance in person Re-ID which is under the cross-domain setting.

Person re-identification (Re-ID) means matching people across different camera views based on different locations. It is challengeing for person images where there are background clutter, pose variations, illumination changes, etc. Attention mechanisms have become attractive for person re-identification algorithms as they aim at strengthening discriminative features, which accord with the purpose of person re-id, i.e., learning discriminative features for diffirent pedestrains. Previous approches mostly learn attention by useing local convolutions which have limited receptive filds. In this paper, an Relation Aware Attention(RAA) module is proposed to address this issue. RAA infers attention maps along two dimensions, channel and spatial, then them are mutiplied to the feature as the output map. For each feature position, RAA harvests the pairwise relationship with others as its response. Furthermore, in order to grasp the structure information of global scope and the local apperance information, we stack the relations and the feature to lean the final attention with a convlutional model. We designe the experiment and compare it with the existing benchmark. The resulits show that our attention model can increas the abillity of feature representation.

Key messageThe expression of drought responsive genes were enhanced by N application and they contributed to drought acclimation.The interactive effects of water and nutrient are crucial for plants. The aim of this study was to elucidate how nitrogen (N) status influence drought acclimation of Populus. A two-factorial design consisting of two N levels (adequate-N and low-N) and two watering treatments (drought stress and well-watered) was used, and an integrative investigation was conducted at the anatomical, physiological and molecular levels. Adequate N supply alleviated the adverse effects of drought stress on root growth in poplars, which may increase water uptake under drought. Nitrogen enhanced leaf anatomical changes and stomata adjustment upon drought stress, lead to less water losses and better growth performance under drought stress. The expression levels of phytohormone signaling components and genes responsible for antioxidative systems and secondary metabolites such as phenylpropanoids were promoted by N application. The expression of abscisic acid (ABA) signaling components was induced by drought when soil N was adequate, which participated in stomata regulation and drought acclimation. The expression of indole acetic acid (IAA) signaling components also was enhanced by N application, which participated in anatomical changes of leaves under drought. These adaptive changes at molecular and anatomical levels contributed to drought acclimation in a synergistic way. Under adequate-N condition, nitrogen and carbon metabolism pathways are being recruited to combat drought, and the C-N interaction play a pivotal role in drought acclimation.

Due to the complex coupling between phenology and climatic factors, the influence mechanism of climate, especially preseason temperature and preseason precipitation, on vegetation phenology is still unclear. In the present study, we explored the long-term trends of phenological parameters of different vegetation types in China north of 30°N from 1982 to 2014 and their comprehensive responses to preseason temperature and precipitation. Simultaneously, annual double-season phenological stages were considered. Results show that the satellite-based phenological data were corresponding with the ground-based phenological data. Our analyses confirmed that the preseason temperature has a strong controlling effect on vegetation phenology. The start date of the growing season (SOS) had a significant advanced trend for 13.5% of the study area, and the end date of the growing season (EOS) showed a significant delayed trend for 23.1% of the study area. The impact of preseason precipitation on EOS was overall stronger than that on SOS, and different vegetation types had different responses. Compared with other vegetation types, SOS and EOS of crops were greatly affected by human activities while the preseason precipitation had less impact. This study will help us to make a scientific decision to tackle global climate change and regulate ecological engineering.