Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases.

The use of invasive mechanical ventilation (IMV) is crucial in rescuing patients with respiratory dysfunction. Accurately predicting the demand for IMV is vital for clinical decision-making. However, current techniques are invasive and challenging to implement in pre-hospital and emergency rescue settings. To address this issue, a real-time prediction method utilizing only non-invasive parameters was developed to forecast IMV demand in this study. The model introduced the concept of real-time warning and leveraged the advantages of machine learning and integrated methods, achieving an AUC value of 0.935 (95% CI 0.933-0.937). The AUC value for the multi-center validation using the AmsterdamUMCdb database was 0.727, surpassing the performance of traditional risk adjustment algorithms (OSI(oxygenation saturation index): 0.608, P/F(oxygenation index): 0.558). Feature weight analysis demonstrated that BMI, Gcsverbal, and age significantly contributed to the model's decision-making. These findings highlight the substantial potential of a machine learning real-time dynamic warning model that solely relies on non-invasive parameters to predict IMV demand. Such a model can provide technical support for predicting the need for IMV in pre-hospital and disaster scenarios.

Irreversible coherent matching bonding of van der Waals heterostructure lattice by pressure.

The key of heterostructure is the combinations created by stacking various vdW materials, which can modify interlayer coupling and electronic properties, providing exciting opportunities for designer devices. However, this simple stacking does not create chemical bonds, making it difficult to fundamentally alter the electronic structure. Here, we demonstrate that interlayer interactions in heterostructures can be fundamentally controlled using hydrostatic pressure, providing a bonding method to modify electronic structures. By covering graphene with boron nitride and inducing an irreversible phase transition, the conditions for graphene lattice-matching bonding (IMB) were created. We demonstrate that the increased bandgap of graphene under pressure is well maintained in ambient due to the IMB in the interface. Comparison to theoretical modeling emphasizes the process of pressure-induced interfacial bonding, systematically generalizes, and predicts this model. Our results demonstrate that pressure can irreversibly control interlayer bonding, providing opportunities for high-pressure technology in ambient applications and IMB engineering in heterostructures.

Impacts of AlaAT3 transgenic poplar on rhizosphere soil chemical properties, enzyme activity, bacterial community, and metabolites under two nitrogen conditions.

Poplar stands as one of the primary afforestation trees globally. We successfully generated transgenic poplar trees characterized by enhanced biomass under identical nutrient conditions, through the overexpression of the pivotal nitrogen assimilation gene, pxAlaAT3. An environmental risk assessment was conducted for investigate the potential changes in rhizosphere soil associated with these overexpressing lines (OL). The results show that acid phosphatase activity was significantly altered under ammonium in OL compared to the wild-type control (WT), and a similar difference was observed for protease under nitrate. 16SrDNA sequencing indicated no significant divergence in rhizosphere soil microbial community diversity between WT and OL. Metabolomics analysis revealed that the OL caused minimal alterations in the metabolites of the rhizosphere soil, posing no potential harm to the environment. With these findings in mind, we anticipate that overexpressed plants will not adversely impact the surrounding soil environment.

A multiscale attribution framework for separating the effects of cascade and individual reservoirs on runoff.

Climate change and human activities have great impacts on runoff. With the gradual development of cascade hydropower in the watershed, the reservoirs have increasingly impacted runoff. However, the current study mainly focuses on quantifying the impacts of human activities and climate change on runoff, lacking the exploration of the impacts of cascade reservoirs, and the attribution results are relatively rough. Therefore, this study utilized data-driven models to establish a runoff attribution framework with the basic steps of "interval runoff prediction and scheduling rule extraction", which achieved the spatial scale separation of the impacts of cascade and individual reservoirs on the runoff, and the analysis of the impacts of each factor at multiple time scales. Taking the upper reaches of the Yangtze River mainstem as an example, we verified the applicability and accuracy of the framework, explored the impacts of climate change, human activities (without reservoir scheduling), and reservoir scheduling on runoff during the period 1980-2018. The research found: (1) Compared to the base period 1980-2005, the average multi-year runoff changes at Pingshan Station (during 2013-2018), Yichang Station (during 2006-2012) and Yichang Station (during 2013-2018) were - 2.61 %, -4.33 % and - 0.89 %, respectively, with decreasing, increasing, and flattening trends over time. (2) Reservoir scheduling is the main factor leading to runoff change, showing negative impacts during flood season and positive impacts during non-flood season. (3) Under the control domain of single and cascade reservoirs, the annual scale impacts of climate change, human activities, and reservoir scheduling on runoff accounted for approximately 1:1:8 and 2:2:6, respectively, showing a complex nonlinear relationship between the impacts of single and cascade reservoirs on runoff. This study provides ideas for quantitatively assessing the impacts of cascade reservoirs on runoff and provide a basis for comprehensively assessing the ecosystem and socio-economic impacts of reservoirs on future runoff changes.

Overexpressing PagGS1;2 maintains carbon and nitrogen balance under high-ammonium conditions and shows increased tolerance to ammonium toxicity in 84K Poplar.

In plants, the glutamine synthetase - glutamic acid synthetase (GS/GOGAT) cycle plays important roles in nitrogen metabolism, growth, development, and stress resistance. Excess ammonium (NH4+) restricts plant growth, but GS can help to alleviate NH4+ toxicity. In this study, 84K poplar (Populus alba × P. glandulosa) showed reduced biomass accumulation and leaf chlorosis under high-NH4+ stress. These symptoms were less severe in lines overexpressing the gene encoding glutamine synthetase (PagGS1;2-OE), and more severe in lines with inhibition of PagGS1;2 expression (PagGS1;2-RNAi). Compared with the wild type（WT）, the PagGS1;2-OE lines showed significantly increased GS and GOGAT activities and higher contents of free amino acids, soluble protein, total nitrogen, and chlorophyll under high-NH4+ stress. In contrast, the antioxidant capacity and NH4+ assimilation capacity of PagGS1;2-RNAi lines were decreased under high-NH4+ stress. The total carbon (C) content and C/N ratio (C/N) of roots and leaves of PagGS1;2-OE lines were significantly higher than those of WT under high-NH4+ stress. Overexpression of PagGS1;2 led to increased accumulation of various amino acids (3-methylaspartic acid, glutamic acid, proline, serine, and histidine); reduced contents of carbohydrates (fructose, starch, galactose, glucose 1-phosphate, fructose 6-phosphate); and increased contents of sugar alcohols (sedum heptanose, maltose, mannitol, galactose, sorbitol) in the roots under high-NH4+ stress. Under high-NH4+ stress, genes related to amino acid biosynthesis, sucrose and starch degradation, galactose metabolism, and the antioxidant system were significantly up-regulated in the roots of PagGS1;2-OE lines, compared with those of wild type. Thus, PagGS1;2 overexpression affected C metabolism and amino acid metabolism pathways under high-NH4+ stress, which helped to maintain the balance of C and N metabolism and alleviate the symptoms of NH4+ toxicity. Modification of the GS/GOGAT cycle by genetic engineering is a promising strategy to improve NH4+ tolerance of forest trees.

Network Analysis of Metabolome and Transcriptome Revealed Regulation of Different Nitrogen Concentrations on Hybrid Poplar Cambium Development.

Secondary development is a key biological characteristic of woody plants and the basis of wood formation. Exogenous nitrogen can affect the secondary growth of poplar, and some regulatory mechanisms have been found in the secondary xylem. However, the effect of nitrogen on cambium has not been reported. Herein, we investigated the effects of different nitrogen concentrations on cambium development using combined transcriptome and metabolome analysis. The results show that, compared with 1 mM NH4NO3 (M), the layers of hybrid poplar cambium cells decreased under the 0.15 mM NH4NO3 (L) and 0.3 mM NH4NO3 (LM) treatments. However, there was no difference in the layers of hybrid poplar cambium cells under the 3 mM NH4NO3 (HM) and 5 mM NH4NO3 (H) treatments. Totals of 2365, 824, 649 and 398 DEGs were identified in the M versus (vs.) L, M vs. LM, M vs. HM and M vs. H groups, respectively. Expression profile analysis of the DEGs showed that exogenous nitrogen affected the gene expression involved in plant hormone signal transduction, phenylpropanoid biosynthesis, the starch and sucrose metabolism pathway and the ubiquitin-mediated proteolysis pathway. In M vs. L, M vs. LM, M vs. HM and M vs. H, differential metabolites were enriched in flavonoids, lignans, coumarins and saccharides. The combined analysis of the transcriptome and metabolome showed that some genes and metabolites in plant hormone signal transduction, phenylpropanoid biosynthesis and starch and sucrose metabolism pathways may be involved in nitrogen regulation in cambium development, whose functions need to be verified. In this study, from the point of view that nitrogen influences cambium development to regulate wood formation, the network analysis of the transcriptome and metabolomics of cambium under different nitrogen supply levels was studied for the first time, revealing the potential regulatory and metabolic mechanisms involved in this process and providing new insights into the effects of nitrogen on wood development.

Research on Fabrication of Phononic Crystal Soft-Supported Graphene Resonator.

In aviation, aerospace, and other fields, nanomechanical resonators could offer excellent sensing performance. Among these, graphene resonators, as a new sensitive unit, are expected to offer very high mass and force sensitivity due to their extremely thin thickness. However, at present, the quality factor of graphene resonators at room temperature is generally low, which limits the performance improvement and further application of graphene resonators. Enhancing the quality factor of graphene resonators has emerged as a pressing research concern. In a previous study, we have proposed a new mechanism to reduce the energy dissipation of graphene resonators by utilizing phononic crystal soft-supported structures. We verified its feasibility through theoretical analysis and simulations. This article focuses on the fabrication of a phononic crystal soft-supported graphene resonator. In order to address the issues of easy fracture, deformation, and low success rate in the fabrication of phononic crystal soft-supported graphene resonators, we have studied key processes for graphene suspension release and focused ion beam etching. Through parameter optimization, finally, we have obtained phononic crystal soft-supported graphene resonators with varying cycles and pore sizes. Finally, we designed an optical excitation and detection platform based on Fabry-Pérot interference principle and explored the impact of laser power and spot size on phononic crystal soft-supported graphene resonators.

Characteristics identification and evolution patterns analyses of road chain conflicts.

Chain conflicts would cause chain-reaction crashes, which might result in elevated fatality rates. Chain conflicts describe a phenomenon wherein evasive actions taken by a following vehicle's driver after a conflict impact nearby vehicles, which occur frequently but are reported less often. To effectively reduce conflict risk, comprehending the evolution patterns of chain conflicts under varied traffic conditions and road segments is crucial, in order to make chain conflicts management strategies. Initially, rear-end or sideswipe conflicts between two vehicles are identified based on vehicle trajectory data captured by an unmanned aerial vehicle group. Subsequently, a chain conflict identification algorithm is proposed, considering the randomness of occurrence time and fluctuation of impact duration, to link individual conflicts. Chain conflict rates exhibit significant variations across different road segments under diverse traffic conditions. Multiple risk and propagation indicators are extracted to unveil latent characteristics of chain conflicts from a high-level perspective. Based on prominent characteristic disparities, three evolution patterns are identified, i.e., Longitudinal Risk Decrease Pattern, Longitudinal Risk Increase Pattern, and Comprehensive High-risk Persistent Pattern. Spatial-temporal high-risk areas associated with each pattern are determined, and transition probabilities between patterns are calculated. The results indicate that these patterns tend to remain stable, with transitions mainly occurring from low-risk to high-risk patterns. Moreover, strategies to reduce conflict risk are proposed based on the characteristics of different patterns. This study holds great significance in understanding chain conflict evolution patterns and preventing chain-reaction crashes.

Preparation of leaf protoplasts from Populus (Populus × xiaohei T. S. Hwang et Liang) and establishment of transient expression system.

Poplar, as a typical woody plant, is an ideal raw material for the production of lignocellulose biofuel. However, the longer life cycle is not conducive to the rapid identification of poplar genes. At present, protoplasts have been used for gene function identification and high-throughput analysis in many model plants. In this paper, a simplified and efficient protoplast isolation and transient expression system of Populus (Populus × xiaohei T. S. Hwang et Liang) is described. Firstly, we proposed an efficient enzyme hydrolysis method for isolating protoplasts from leaves of Populus × xiaohei. Secondly, we optimized the conditions of protoplast transformation mediated by PEG, and established an efficient transient expression system of protoplasts of Populus × xiaohei. Finally, the subcellular localization of three identified Dof transcription factors (PnDof19, PnDof20 and PnDof30) was also observed in the nucleus by using this scheme, which proved that the method was feasible. In general, this efficient method of protoplast isolation and transformation can be used for the study of protein subcellular localization and can be applied to other fields of molecular biology, such as protein interaction, gene activation and so on.

Pressure-induced disorder and nanosizing inhibits superconductivity in In2Te3.

The generation of disorder often gives rise to profound and irreversible physical phenomena. Here, we explore the influence of disorder on the superconducting properties of In2Te3through comprehensive high-pressure investigations. Building upon previous findings, we investigated the progressive suppression of superconductivity in In2Te3during the depressurization process: the increased disorder that ultimately leads to the complete disappearance of the superconducting state. Simultaneously, our high-pressure x-ray diffraction analysis reveals an irreversible structural phase transition. Furthermore, microstructure analysis using transmission electron microscopy clearly demonstrates both grain refinement and a substantial enhancement of disorder. These findings not only provide valuable insights into the mechanism by which disorder suppresses superconductivity, but also offer guidance for future advancements in the fabrication of atmospheric-pressure superconductors.

A Parameter Estimation of Photovoltaic Models Using a Boosting Flower Pollination Algorithm.

An accurate and reliable estimation of photovoltaic models holds immense significance within the realm of energy systems. In pursuit of this objective, a Boosting Flower Pollination Algorithm (BFPA) was introduced to facilitate the robust identification of photovoltaic model parameters and enhance the conversion efficiency of solar energy into electrical energy. The incorporation of a Gaussian distribution within the BFPA serves the dual purpose of conserving computational resources and ensuring solution stability. A population clustering strategy is implemented to steer individuals in the direction of favorable population evolution. Moreover, adaptive boundary handling strategies are deployed to mitigate the adverse effects of multiple individuals clustering near problem boundaries. To demonstrate the reliability and effectiveness of the BFPA, it is initially employed to extract unknown parameters from well-established single-diode, double-diode, and photovoltaic module models. In rigorous benchmarking against eight control methods, statistical tests affirm the substantial superiority of the BFPA over these controls. Furthermore, the BFPA successfully extracts model parameters from three distinct commercial photovoltaic cells operating under varying temperatures and light irradiances. A meticulous statistical analysis of the data underscores a high degree of consistency between simulated data generated by the BFPA and observed data. These successful outcomes underscore the potential of the BFPA as a promising approach in the field of photovoltaic modeling, offering substantial enhancements in both accuracy and reliability.

Genome-Wide Identification and Characterization of WRKY Transcription Factors in Betula platyphylla Suk. and Their Responses to Abiotic Stresses.

The WRKY transcription factor (TF) family is one the largest plant-specific transcription factor families. It has been proven to play significant roles in multiple plant biological processes, especially stress response. Although many WRKY TFs have been identified in various plant species, WRKYs in white birch (Betula platyphylla Suk.) remain to be studied. Here, we identified a total of 68 BpWRKYs, which could be classified into four main groups. The basic physiochemical properties of these TFs were analyzed using bioinformatics tools, including molecular weight, isoelectric point, chromosome location, and predicted subcellular localization. Most BpWRKYs were predicted to be located in the nucleus. Synteny analysis found 17 syntenic gene pairs among BpWRKYs and 52 syntenic gene pairs between BpWRKYs and AtWRKYs. The cis-acting elements in the promoters of BpWRKYs could be enriched in multiple plant biological processes, including stress response, hormone response, growth and development, and binding sites. Tissue-specific expression analysis using qRT-PCR showed that most BpWRKYs exhibited highest expression levels in the root. After ABA, salt (NaCl), or cold treatment, different BpWRKYs showed different expression patterns at different treatment times. Furthermore, the results of the Y2H assay proved the interaction between BpWRKY17 and a cold-responsive TF, BpCBF7. By transient expression assay, BpWRKY17 and BpWRKY67 were localized in the nucleus, consistent with the previous prediction. Our study hopes to shed light for research on WRKY TFs and plant stress response.

Strain-induced electronic structures and band-gap of few-layer AgInP2S6.

The band gap and mechanical control ability of two-dimensional materials largely determine the application value of two-dimensional devices in optical and electronic properties, so the bandgap controllability of two-dimensional materials broadens the application range of multi-functional devices. In the layered van der Waals (vdW) material AgInP2S6, the band gap can be adjusted by the number of layers and flexible strain, and the few layers AgInP2S6have discrete band gap values, which are also relevant for optoelectronic applications. In the strain range of up to 2.7% applied, the band gap can be adjusted, and the film is relatively stable under strain. We further analyzed the physical mechanism of flexible strain band gap regulation and found that strain-regulation reduced the band gap and increased the chemical bond length. These studies open up new opportunities for the future development of vdW material photoelectric resonators represented by AgInP2S6, and have important reference value.

Risk factors for agitation in home-cared older adults with dementia: evidence from 640 elders in East China.

Background: Agitation is common among older adults with dementia, negatively affecting their quality of life and their caregivers'. Since home care remains the dominant approach for older adults, this study investigates the risk factors for agitation in older adults with dementia in China. Methods: We perform a cross-sectional study of home-cared older adults with dementia in Ningbo, China, using 2020 data. We use a self-made questionnaire to investigate the risks of agitated behavior and its related factors. We perform descriptive, univariate, and regression analyses. Findings: We address 640 older Chinese adults; 42.8% of the sample exhibits one or more agitated behaviors. We find that basic health issues, such as activities of daily living (ADL), family support issues, such as Zarit Burden Interview (ZBI) scale and Family APGAR Questionnaire (APGAR), and behavioral awareness issues, such as fall and scald, significantly influence the occurrence of agitation behaviors (p < 0.05). Older adults with severe ADL disorder (b = 6.835, ß = 0.196, p < 0.001), ZBI score of 67.00-88.0 (b = 10.212, ß = 0.248, p = 0.005), severe APGAR disorder (b = 3.699, ß = 0.100, p = 0.012) and a history of fall (b = 9.311, ß = 0.199, P = <0.001) or scald (b = 9.288, ß = 0.125, p = 0.002) are more likely to exhibit agitated behaviors. Interpretation: Agitated behavior in home-cared older adults with dementia are diverse and related to mental state, family support, and behavioral awareness issues. Caregivers, often family members, should be attentive to the needs of dementia patients and take active and effective measures to improve their quality of life. They should be aware of the causes and triggers of agitated behavior and take steps to reduce its occurrence.

PIWI-interacting RNA-17458 is oncogenic and a potential therapeutic target in cervical cancer.

Cervical cancer (CC) is one of the leading cancers among the female reproductive system. The piwi-interacting RNA (piRNA) function and biogenesis has been studied in various cancers, including CC. But the precise mechanism of piRNA in CC is still unknown. In our study, we found that piRNA-17458 was overexpressed in CC tissues and cells. piRNA-17458 mimic and inhibitor promoted and suppressed proliferation, migration and invasion ability of CC cells, respectively. We also demonstrated that piRNA-17458 mimic could contribute to tumor growth in mice xenograft models. Besides, we also found that the piRNA-17458 mimic could enhance mRNA N6-methyladenosine(m6A) levels and increase WTAP stability in CC cells, while the effects of the mimic was reversed by the WTAP knockdown. The results of dual luciferase reporter assay showed that WTAP was a direct target of piRNA-17458. Knockdown of WTAP attenuated proliferation, migration and invasion of CC cells in piRNA-17458 mimic group. Our finding not only demonstrates for the first time that piRNA-17458 is overexpressed in CC tissues and cells, but also shows that piRNA-17458 promotes tumorigenesis of CC in a WTAP-mediated m6A methylation manner.

A Robust Mean-Field Actor-Critic Reinforcement Learning Against Adversarial Perturbations on Agent States.

Multiagent deep reinforcement learning (DRL) makes optimal decisions dependent on system states observed by agents, but any uncertainty on the observations may mislead agents to take wrong actions. The mean-field actor-critic (MFAC) reinforcement learning is well-known in the multiagent field since it can effectively handle a scalability problem. However, it is sensitive to state perturbations that can significantly degrade the team rewards. This work proposes a Robust MFAC (RoMFAC) reinforcement learning that has two innovations: 1) a new objective function of training actors, composed of a policy gradient function that is related to the expected cumulative discount reward on sampled clean states and an action loss function that represents the difference between actions taken on clean and adversarial states and 2) a repetitive regularization of the action loss, ensuring the trained actors to obtain excellent performance. Furthermore, this work proposes a game model named a state-adversarial stochastic game (SASG). Despite the Nash equilibrium of SASG may not exist, adversarial perturbations to states in the RoMFAC are proven to be defensible based on SASG. Experimental results show that RoMFAC is robust against adversarial perturbations while maintaining its competitive performance in environments without perturbations.

Functional Identification and Genetic Transformation of the Ammonium Transporter PtrAMT1;6 in Populus.

The ammonium transporter (AMT) family gene is an important transporter involved in ammonium uptake and transfer in plants and is mainly engaged in the uptake and transport of ammonium from the environment by roots and the reabsorption of ammonium in the aboveground parts. In this study, the expression pattern, functional identification, and genetic transformation of the PtrAMT1;6 gene, a member of the ammonium transporter protein family in P. trichocarpa, were investigated as follows: (1) Fluorescence quantitative PCR demonstrated that the PtrAMT1;6 gene was preferentially expressed in the leaves, with both dark-induced and light-inhibited expression patterns. (2) A functional restoration assay using the yeast ammonium transporter protein mutant strain indicated that the PtrAMT1;6 gene restored the ability of the mutant to transport ammonium with high affinity. (3) Arabidopsis was transformed with pCAMBIA-PtrAMT1;6P, and the transformed lines were stained with GUS, which showed that the rootstock junction, cotyledon petioles, and the leaf veins and pulp near the petioles of the transformed plants could be stained blue, indicating that the promoter of the PtrAMT1;6 gene had promoter activity. (4) The overexpression of the PtrAMT1;6 gene caused an imbalance in carbon and nitrogen metabolism and reduced nitrogen assimilation ability in '84K' poplar and ultimately reduced biomass. The above results suggest that PtrAMT1;6 may be involved in ammonia recycling during nitrogen metabolism in aboveground parts, and overexpression of PtrAMT1;6 may affect the process of carbon and nitrogen metabolism, as well as nitrogen assimilation in plants, resulting in stunted growth of overexpression plants.

CEModule: A Computation Efficient Module for Lightweight Convolutional Neural Networks.

Lightweight convolutional neural networks (CNNs) rely heavily on the design of lightweight convolutional modules (LCMs). For an LCM, lightweight design based on repetitive feature maps (LoR) is currently one of the most effective approaches. An LoR mainly involves an extraction of feature maps from convolutional layers (CE) and feature map regeneration through cheap operations (RO). However, existing LoR approaches carry out lightweight improvements only from the aspect of RO but ignore the problems of poor generalization, low stability, and high computation workload incurred in the CE part. To alleviate these problems, this article introduces the concept of key features from a CNN model interpretation perspective. Subsequently, it presents a novel LCM, namely CEModule, focusing on the CE part. CEModule increases the number of key features to maintain a high level of accuracy in classification. In the meantime, CEModule employs a group convolution strategy to reduce floating-point operations (FLOPs) incurred in the training process. Finally, this article brings forth a dynamic adaptation algorithm ( α -DAM) to enhance the generalization of CEModule-enabled lightweight CNN models, including the developed CENet in dealing with datasets of different scales. Compared with the state-of-the-art results, CEModule reduces FLOPs by up to 54% on CIFAR-10 while maintaining a similar level of accuracy in classification. On ImageNet, CENet increases accuracy by 1.2% following the same FLOPs and training strategies.

Genome-wide identification of glutamate synthase gene family and expression patterns analysis in response to carbon and nitrogen treatment in Populus.

Glutamate synthase (GOGAT) is a key enzyme in glutamine synthetase (GS)/GOGAT cycle and at the hub of carbon and nitrogen metabolism, catalyzing the formation of glutamate from α-oxoglutarate and glutamine. In this study, members of GOGAT family in Populus trichocarpa were identified and analyzed by bioinformatics. The four PtGOGATs were divided into two subgroups: subgroup A (Fd-GOGAT1 and Fd-GOGAT2) and subgroup B (NADH-GOGAT1 and NADH-GOGAT2). Many important elements have been identified in the promoters of different PtGOGATs, including hormone- and light-responsive elements. Meanwhile, the transcript levels of PxGOGATs were affected by light and diurnal cycle. Quantitative real-time PCR showed PxFd-GOGATs and PxNADH-GOGATs were mainly expressed in leaves and roots in Populus × xiaohei T. S. Hwang et Liang, respectively. Under elevated CO2, PxGOGATs were suppressed in all tissues except the stem. And PxFd-GOGATs and PxNADH-GOGATs were strongly induced by nitrogen in leaves and roots, respectively. In addition, PxGOGATs were stimulated significantly in roots in response to NH4+and glutamine directly. Our results provide new insights about GOGATs in poplar and their expression patterns under exogenous substances, to lay molecular basis for studying gene function and provide a reference for exploring putative roles of GOGATs in carbon-nitrogen balance.

Assessing spatial connectivity effects on daily streamflow forecasting using Bayesian-based graph neural network.

Data-driven models have been widely developed and achieved impressive results in streamflow prediction. However, the existing data-driven models mostly focus on the selection of input features and the adjustment of model structure, and less on the impact of spatial connectivity on daily streamflow prediction. In this paper, a basin network based on graph-structured data is constructed by considering the spatial connectivity of different stations in the real basin. Furthermore, a novel graph neural network model, variational Bayesian edge-conditioned graph convolution model, which consists of edge-conditioned convolution networks and variational Bayesian inference, is proposed to assess the spatial connectivity effects on daily streamflow forecasting. The proposed graph neural network model is applied to forecast the next-day streamflow of a hydrological station in the Yangtze River Basin, China. Six comparative models and three comparative experimental groups are used to validate model performance. The results show that the proposed model has excellent performance in terms of deterministic prediction accuracy (NSE ≈ 0.980, RMSE≈1362.7 and MAE ≈ 745.8) and probabilistic prediction reliability (ICPC≈0.984 and CRPS≈574.1), which demonstrates that establishing appropriate connectivity and reasonably identifying connection relationships in the basin network can effectively improve the deterministic and probabilistic forecasting performance of the graph convolutional model.