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Fire is a crucial event regulating the structure and functioning of many ecosystems. Yet few studies have focused on how fire affects taxonomic and functional diversities of soil microbial communities, along with changes in plant communities and soil carbon (C) and nitrogen (N) dynamics. Here, we analyze these effects in a grassland ecosystem 9 months after an experimental fire at the Jasper Ridge Global Change Experiment site in California, USA. Fire altered soil microbial communities considerably, with community assembly process analysis showing that environmental selection pressure was higher in burned sites. However, a small subset of highly connected taxa was able to withstand the disturbance. In addition, fire decreased the relative abundances of most functional genes associated with C degradation and N cycling, implicating a slowdown of microbial processes linked to soil C and N dynamics. In contrast, fire stimulated above- and belowground plant growth, likely enhancing plant-microbe competition for soil inorganic N, which was reduced by a factor of about 2. To synthesize those findings, we performed structural equation modeling, which showed that plants but not microbial communities were responsible for significantly higher soil respiration rates in burned sites. Together, our results demonstrate that fire 'reboots' the grassland ecosystem by differentially regulating plant and soil microbial communities, leading to significant changes in soil C and N dynamics.

The ecological concept of the r-K life history strategy is widely applied in macro-ecology to characterize functional traits of taxa. However, its adoption in microbial communities is limited, owing to the lack of a measureable, convenient functional trait for classification. In this study, we performed an experiment of stepwise organic amendments in triplicate anaerobic digesters. We found that high resource availability significantly favored microbial r-strategists such as Bacillus spp. Incremental resource availability heightened average rRNA operon copy number of microbial community, resulting in a strong, positive correlation (r>0.74, P

Microbial decomposition of soil organic carbon (SOC) in thawing Arctic permafrost is important in determining greenhouse gas feedbacks of tundra ecosystems to climate. However, the changes in microbial community structure during SOC decomposition are poorly known. Here we examine these changes using frozen soils from Barrow, Alaska, USA, in anoxic microcosm incubation at -2 and 8°C for 122 days. The functional gene array GeoChip was used to determine microbial community structure and the functional genes associated with SOC degradation, methanogenesis, and Fe(III) reduction. Results show that soil incubation after 122 days at 8°C significantly decreased functional gene abundance (P < 0.05) associated with SOC degradation, fermentation, methanogenesis, and iron cycling, particularly in organic-rich soil. These observations correspond well with decreases in labile SOC content (e.g., reducing sugar and ethanol), methane and CO2 production, and Fe(III) reduction. In contrast, the community functional structure was largely unchanged in the -2°C incubation. Soil type (i.e., organic vs. mineral) and the availability of labile SOC were among the most significant factors impacting microbial community structure. These results demonstrate the important roles of microbial community in SOC degradation and support previous findings that SOC in organic-rich Arctic tundra is highly vulnerable to microbial degradation under warming.

Global warming has shifted climate zones poleward or upward. However, understanding the responses and mechanism of microbial community structure and functions relevant to natural climate zone succession is challenged by the high complexity of microbial communities. Here, we examined soil microbial community in three broadleaved forests located in the Wulu Mountain (WLM, temperate climate), Funiu Mountain (FNM, at the border of temperate and subtropical climate zones), or Shennongjia Mountain (SNJ, subtropical climate). Although plant species richness decreased with latitudes, the microbial taxonomic α-diversity increased with latitudes, concomitant with increases in soil total and available nitrogen and phosphorus contents. Phylogenetic NRI (Net Relatedness Index) values increased from -0.718 in temperate zone (WLM) to 1.042 in subtropical zone (SNJ), showing a shift from over dispersion to clustering likely caused by environmental filtering such as low pH and nutrients. Similarly, taxonomy-based association networks of subtropical forest samples were larger and tighter, suggesting clustering. In contrast, functional α-diversity was similar among three forests, but functional gene networks of the FNM forest significantly (P < 0.050) differed from the others. A significant correlation (R = 0.616, P < 0.001) between taxonomic and functional β-diversity was observed only in the FNM forest, suggesting low functional redundancy at the border of climate zones. Using a strategy of space-for-time substitution, we predict that poleward climate range shift will lead to decreased microbial taxonomic α-diversities in broadleaved forest.

Graph neural networks (GNNs) have shown great potential in representation learning for various graph tasks. However, the distribution shift between the training and test sets poses a challenge to the efficiency of GNNs. To address this challenge, HomoTTT proposes a fully test-time training framework for GNNs to enhance the model's generalization capabilities for node classification tasks. Specifically, our proposed HomoTTT designs a homophily-based and parameter-free graph contrastive learning task with adaptive augmentation to guide the model's adaptation during the test-time training, allowing the model to adapt for specific target data. In the inference stage, HomoTTT proposes to integrate the original GNN model and the adapted model after TTT using a homophily-based model selection method, which prevents potential performance degradation caused by unconstrained model adaptation. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of our proposed framework. Additionally, the exploratory study further validates the rationality of the homophily-based graph contrastive learning task with adaptive augmentation and the homophily-based model selection designed in HomoTTT. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely limits the performance of the GNNs in this field. To alleviate the collapse of node representations in semi-supervised scenario, we propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN). In our method, we improve the discriminative capability of the latent embeddings by an interpolation-based augmentation strategy and a correlation reduction mechanism. Specifically, we first conduct the interpolation-based augmentation in the latent space and then force the prediction model to change linearly between samples. Second, we enable the learned network to tell apart samples across two interpolation-perturbed views through forcing the correlation matrix across views to approximate an identity matrix. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discriminative representation learning. Extensive experimental results on six datasets demonstrate the effectiveness and the generality of MGCN compared to the existing state-of-the-art methods. The code of MGCN is available at https://github.com/xihongyang1999/MGCN on Github. [ABSTRACT FROM AUTHOR]

Deep graph clustering, efficiently dividing nodes into multiple disjoint clusters in an unsupervised manner, has become a crucial tool for analyzing ubiquitous graph data. Existing methods have acquired impressive clustering effects by optimizing the clustering network under the parametric condition—predefining the true number of clusters (Ktr). However, Ktr is inaccessible in pure unsupervised scenarios, in which existing methods are incapable of inferring the number of clusters (K), causing limited feasibility. This article proposes the first Parameter-Agnostic Deep Graph Clustering method (PADGC), which consists of two core modules: K-guidence clustering and topological-hierarchical inference, to infer K efficiently and gain impressive clustering predictions. Specifically, K-guidence clustering is employed to optimize the cluster assignments and discriminative embeddings in a mutual promotion manner under the latest updated K, even though K may deviate from Ktr. In turn, such optimized cluster assignments are utilized to explore more accurate K in the topological-hierarchical inference, which can split the dispersive clusters and merge the coupled ones. In this way, these two modules are complementarily optimized until generating the final convergent K and discriminative cluster assignments. Extensive experiments on several benchmarks, including graphs and images, can demonstrate the superiority of our method. The mean values of our inferred K, in 11 out of 12 datasets, deviates from Ktr by less than 1. Our method can also achieve competitive clustering effects with existing parametric deep graph clustering. [ABSTRACT FROM AUTHOR]

Modeling is a core practice in science and is a meaningful way to learn the subject. This article introduces a modeling-based approach that highlights the idea that modeling is an iterative process and integrates the fundamental parts of scientists' work and key suggestions for teaching through modeling. The lesson "The Structure and Function of Kidneys" from a middle school biology course serves as an example of how to conduct the suggested modeling-based approach. By the end of the lesson, almost all students demonstrated a scientific understanding of the structure of nephrons and their functions. On the basis of the implementation of this lesson, we also provide further suggestions for modeling-based teaching. [ABSTRACT FROM AUTHOR]

Due to their important role in maintaining temperature and soil moisture, agricultural plastic covers have been widely utilized around the globe for improving crop-growing conditions, which include both plastic-covered greenhouses (PCGs) and plastic-mulched farmlands (PMFs). However, it is a challenging and long-neglected issue to separate PCGs from PMFs due to their spectral similarity. The objective of this study is to propose a deep semantic segmentation model for accurate PCG and PMF mapping based on very high-resolution satellite images and to improve the model's spatial generalization capability using a transfer learning strategy. Specifically, the proposed semantic segmentation model has an encoder-decoder structure, where the encoder is composed of a new convolutional neural network for discriminative spatial feature learning, while the decoder utilizes a multi-task strategy to improve the predictions on the boundaries. Meanwhile, a transfer learning framework is adopted to increase mapping performance and generalization ability under limited samples. Experimental results in several typical regions across the Eurasian continent show that the proposed model could separate PCGs from PMFs accurately with a mean overall accuracy of 94.49% and an average mIoU of 0.8377. Ablation studies verify the role of encoder-decoder and transfer learning strategy in improving classification performance. [ABSTRACT FROM AUTHOR]

In this paper, the application of the supervised descent method (SDM) for 2-D microwave thorax imaging is studied. The forward modeling problem is solved by the finite element-boundary integral (FE-BI) method. According to the prior information of human thorax, a 3-ellipse training set is generated offline. Then, the average descent direction between an initial background model and the training models is calculated. Finally, the reconstruction of the testing thorax model is achieved based on the average descent directions online. The feasibility using One-Step SDM for thorax imaging is studied. Numerical results indicate that the structural information of thorax can be reconstructed. It has potential for real-time imaging in future clinical diagnosis. [ABSTRACT FROM AUTHOR]

Nowadays, the droplet–particle collision characteristics in the gas‐phase ethylene polymerization process are still unclear. The high‐speed photography and a quasi‐circle imaging approach are employed to study the collision interaction characteristics between liquid droplets and polyethylene particles. The liquid film evolution is studied through variations of the film thickness on the particle north pole, the dynamic contact angle, center angle and film thickness at the maximum extension. Results have found that for n‐hexane the threshold temperature of the recoil happening increases with increasing initial Weber number, but for 1‐hexene it is stable. Over 70°C evaporation and splash occurs immediately. Under low Weber numbers, the water droplet stays for damping oscillations, the reference stable height of which is linearly related to temperatures. Moreover, three regimes of film thickness variation with time are identified and mathematically described, while Regime 3 characteristics are found strongly dependent on the liquid species, Weber number, and particle temperature. [ABSTRACT FROM AUTHOR]

This paper investigated the influence of 2, 6‐di‐tert‐butyl‐p‐cresol (DBPC) on the long‐term ageing characteristics of oil–paper insulation and the deposition and migration of copper sulphide induced by the reaction of copper and dibenzyl disulphide (DBDS) in oil‐immersed transformers. A thermal ageing experiment at 130°C were designed and conducted by adding different concentrations of DBPC to the naphthenic mineral oil and after the experiments a set of characteristic parameters were tested. The result indicates that the excessive concentration (≥0.6%) of DBPC will slightly increase the ageing of insulating oil and increase the carboxyl group in the mineral oil, affecting the reaction of DBDS and copper, and the insulating paper wrapped on the copper strip was also aged seriously accordingly. Meanwhile, the deposition of copper sulphide on the insulating paper is found to be slightly increased with the increase of the concentration of DBPC. When insulating oil contains DBPC and DBDS, due to migration of DBDS–Cu and DBPC–Cu in oil, the copper ions in the oil would be fluctuating at a certain period of time. Although the addition of a small concentration of DBPC can improve the antioxygenic property of the insulting oil to some extent, the transformers that contain excessive concentration of DBPC may be more harmful than beneficial. [ABSTRACT FROM AUTHOR]

This study investigated the inhibitory effects of adsorption treatment on the degradation of oil–paper insulation and corrosive sulphur in oil. A thermal ageing experiment at 130°C was conducted using five different adsorbents to adsorb some impurities in oil on the 15th day of a 30 day ageing test, the related characteristics of oil–paper insulation before and after the adsorption treatment were measured and analysed. Meanwhile, a thermal ageing experiment at 150°C was also conducted before using five different adsorbents to adsorb dibenzyl disulphide (DBDS) in oil, energy dispersive X‐ray was used to evaluate the degree of the corrosion of windings. The result indicates that adsorption treatment is an effective method to inhibit the degradation of oil–paper insulation. A molecular sieve, silica gel and activated alumina can effectively improve the performance of oil–paper insulation. By contrast, the antioxidant (2, 6‐di‐tert‐butyl‐p‐cresol) can be also adsorbed by adsorbents, which accelerate the ageing degradation of the insulating oil. On the other hand, the adsorbents can adsorb DBDS in oil, but cannot remove all DBDS. The effect achieved from silica gel is especially significant, which not only effectively improves the performance of oil–paper insulation but also adsorbs DBDS in oil. [ABSTRACT FROM AUTHOR]