Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees

Data accessibility statement: Full census data are available upon reasonable request from the ForestGEO data portal, http://ctfs.si.edu/datarequest/ We thank Margie Mayfield, three anonymous reviewers and Jacob Weiner for constructive comments on the manuscript. This study was financially supported by the National Key R&D Program of China (2017YFC0506100), the National Natural Science Foundation of China (31622014 and 31570426), and the Fundamental Research Funds for the Central Universities (17lgzd24) to CC. XW was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB3103). DS was supported by the Czech Science Foundation (grant no. 16-26369S). Yves Rosseel provided us valuable suggestions on using the lavaan package conducting SEM analyses. Funding and citation information for each forest plot is available in the Supplementary Information Text 1.Peer reviewedPostprin

Interaction Forces between Diaspore and Kaolinite in NaOL Solution Probed by EDLVO Theory and AFM Analysis

Molecular force plays an important role in the interaction between collector and minerals, which directly reflects the intrinsic reason for the selectivity and collection of the collector to minerals. In this work, the interaction forces between sodium oleate (NaOL) and minerals (kaolinite and diaspore) were directly characterized by atomic force microscopy (AFM) combined with EDLVO theory. The results show that after interacting with NaOL, the zeta potentials of kaolinite and diaspore were more negative, and the hydrophobicity of minerals increased. EDLVO calculation results indicate that electrostatic repulsion dominated the interaction forces between mineral particles, and the van der Waals interaction energy, electrostatic interaction energy, and hydrophobic interaction energy increased after NaOL treatment. AFM measurements show that the NaOL collector increased the attraction force of diaspore-diaspore and kaolinite-kaolinite particles, and the increase in attraction force for diaspore-diaspore particles was larger than in kaolinite particles, which was consistent with the EDLVO results. The adhesion force between the NaOL collector and the diaspore surface was larger than in kaolinite, confirming the fact that NaOL had better collection and selectivity for diaspore than kaolinite. This work improves understanding of the interaction mechanisms between NaOL collector, diaspore, and kaolinite minerals

Interaction Forces between Diaspore and Kaolinite in NaOL Solution Probed by EDLVO Theory and AFM Analysis

Molecular force plays an important role in the interaction between collector and minerals, which directly reflects the intrinsic reason for the selectivity and collection of the collector to minerals. In this work, the interaction forces between sodium oleate (NaOL) and minerals (kaolinite and diaspore) were directly characterized by atomic force microscopy (AFM) combined with EDLVO theory. The results show that after interacting with NaOL, the zeta potentials of kaolinite and diaspore were more negative, and the hydrophobicity of minerals increased. EDLVO calculation results indicate that electrostatic repulsion dominated the interaction forces between mineral particles, and the van der Waals interaction energy, electrostatic interaction energy, and hydrophobic interaction energy increased after NaOL treatment. AFM measurements show that the NaOL collector increased the attraction force of diaspore-diaspore and kaolinite-kaolinite particles, and the increase in attraction force for diaspore-diaspore particles was larger than in kaolinite particles, which was consistent with the EDLVO results. The adhesion force between the NaOL collector and the diaspore surface was larger than in kaolinite, confirming the fact that NaOL had better collection and selectivity for diaspore than kaolinite. This work improves understanding of the interaction mechanisms between NaOL collector, diaspore, and kaolinite minerals

Influence of hydropower stations on the water microbiota in the downstream of Jinsha River, China

Construction of hydropower stations has been an important approach to meet China's increasing power demand, but the impact of construction of hydropower stations on river microbiota is not fully understood. To evaluate this, the microbial composition from 18 sampling sites in the downstream of Jinsha River of China, upstream and downstream of two completed and two under-construction hydropower stations, were analyzed using high-throughput 16S rRNA gene sequencing. Three independent samples from each site were analyzed. A total of 18,683 OTUs from 1,350 genera were identified at 97% sequence similarity. Our results showed that the completion of hydropower stations would significantly increase the relative abundances of Acidobacteria, Chlorobi, Chloroflexi, Cyanobacteria, Nitrospirae, and Planctomycetes, especially the relative abundance of Synechococcus dOTUs and thus increase the risk of algal blooms. PCA based on all KEGG pathways and the significantly different KEGG pathways showed the predicted metabolic characteristics of the water microbiota by PICRUSt in the activated hydropower station group were significant difference to the other groups. Results from canonical correspondence analysis showed that water temperature and dissolved oxygen had significant effects on microbiota composition. These results are important for assessing the impact of hydropower stations on river microbiota and their potential environmental risks

Risk Assessment and Analysis of Rock Burst under High-Temperature Liquid Nitrogen Cooling

Rock burst, an important kind of geological disaster, often occurs in underground construction. Rock burst risk assessment, as an important part of engineering risk assessment, cannot be ignored. Liquid nitrogen fracturing is a new technology used in the geological, oil, and gas industries to enhance productivity. It involves injecting liquid nitrogen into reservoir rocks to induce fractures and increase permeability, effectively reducing rock burst occurrences and facilitating the flow of oil or gas toward the wellbore. The research on rock burst risk assessment technology is the basis of reducing rock burst geological disasters, which has important theoretical and practical significance. This article examines the temperature treatment of two types of rocks at 25 °C, 100 °C, 200 °C, 300 °C, and 400 °C, followed by immersion in a liquid nitrogen tank. The temperature difference between the liquid nitrogen and the rocks may trigger rock bursting. The research focused on analyzing various characteristics of rock samples when exposed to liquid nitrogen. This included studying the stress–strain curve, elastic modulus, strength, cross-section analysis, wave velocity, and other relevant aspects. Under the influence of high temperature and a liquid nitrogen jet, the wave velocity of rocks often changes. The structural characteristics and possible hidden dangers of rocks can be understood more comprehensively through section scanning analysis. The stress–strain curve describes the deformation and failure behavior of rocks under different stress levels, which can help to evaluate their stability and structural performance. The investigation specifically focused on the behavior of rocks subjected to high temperatures and liquid nitrogen. By analyzing the stress–strain curves, researchers were able to identify the precursors and deformation processes that occur before significant deformation or failure. These findings have implications for the mechanical properties and stability of the rocks

ResAttn-recon: Residual self-attention based cortical surface reconstruction

Introduction: The accurate cerebral cortex surface reconstruction is crucial for the study of neurodegenerative diseases. Existing voxelwise segmentation-based approaches like FreeSurfer and FastSurfer are limited by the partial volume effect, meaning that reconstruction details highly rely on the resolution of the input volume. In the computer version area, the signed distance function has become an efficient method for 3D shape representation, the inherent continuous nature makes it easy to capture the fine details of the target object at an arbitrary resolution. Additionally, as one of the most valuable breakthroughs in deep learning research, attention is a powerful mechanism developed to enhance the performance of the encoder-decoder architecture.Methods: To further improve the reconstruction accuracy of the cortical surface, we proposed ResAttn-Recon, a residual self-attention based encoder-decoder framework. In this framework, we also developed a lightweight decoder network with skip connections. Furthermore, a truncated and weighted L1 loss function are proposed to accelerate network convergence, compared to simply applying the L1 loss function.Results: The intersection over union curve in the training process achieved a steeper slope and a higher peak (0.948 vs. 0.920) with a truncated L1 loss. Thus, the average symmetric surface distance (AD) for the inner and outer surfaces is 0.253 ± 0.051 and the average Hausdorff distance (HD) is 0.629 ± 0.186, which is lower than that of DeepCSR, whose absolute distance equals 0.283 ± 0.059 and Hausdorff distance equals 0.746 ± 0.245.Discussion: In conclusion, the proposed residual self-attention-based framework can be a promising approach for improving the cortical surface reconstruction performance

Catalytic co-pyrolysis of cellulosic ethanol–processing residue with high-density polyethylene over biomass bottom ash catalyst

In this study, the bagasse ash (BA) from biorefinery process was recovered and used as a catalyst in the co-pyrolysis of solid residue from second-generation bioethanol plant with high-density polyethylene (HDPE). The co-pyrolytic behaviors were studied using thermogravimetric analyzer at three heating rates of 10, 20, and 40 K min−1. The synergistic effects between BA and HDPE and their co-pyrolysis kinetics were investigated using two model-free methods: Kissinger–Akahira–Sunose (KAS) and Flynn–Wall–Ozawa (FWO). The pyrolysis products were determined by pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) as well. The results indicated that the addition of BA could increase the production yield. The average apparent active energy (Ea) of co-pyrolysis was 171.3 kJ mol−1 from KAS and 174 kJ mol−1 from FWO, which were lower than that for catalyst-free pyrolysis (174.8 kJ mol−1 from KAS and 177.3 kJ mol−1 from FWO). The novel co-pyrolysis process showed great potential in improving both the economic and environment sides of the second-generation biorefineries

One-Class LSTM Network for Anomalous Network Traffic Detection

Artificial intelligence-assisted security is an important field of research in relation to information security. One of the most important tasks is to distinguish between normal and abnormal network traffic (such as malicious or sudden traffic). Traffic data are usually extremely unbalanced, and this seriously hinders the detection of outliers. Therefore, the identification of outliers in unbalanced datasets has become a key issue. To help solve this challenge, there is increasing interest in focusing on one-class classification methods that train models based on the samples of a single given class. In this paper, long short-term memory (LSTM) is introduced into one-class classification, and one-class LSTM (OC-LSTM) is proposed based on the traditional one-class support vector machine (OC-SVM). In contrast with other hybrid deep learning methods based on auto-encoders, the proposed method is an end-to-end training network that uses a loss function such as the OC-SVM optimization objective for model training. A comprehensive experiment on three large complex network traffic datasets showed that this method is superior to the traditional shallow method and the most advanced deep method. Furthermore, the proposed method can provide an effective reference for anomaly detection research in the field of network security, especially for the application of one-class classification

Transcriptome and Gene Co-Expression Network Analysis Identifying Differentially Expressed Genes and Signal Pathways Involved in the Height Development of Banana (<i>Musa</i> spp.)

Plant height is an important and valuable agronomic trait associated with yield and resistance to abiotic and biotic stresses. Dwarfism has positive effects on plant development and field management, especially for tall monocotyledon banana (Musa spp.). However, several key genes and their regulation mechanism of controlling plant height during banana development are unclear. In the present study, the popular cultivar ‘Brazilian banana’ (‘BX’) and its dwarf mutant (‘RK’) were selected to identify plant height-related genes by comparing the phenotypic and transcriptomic data. Banana seedlings with 3–4 leaves were planted in the greenhouse and field. We found that the third and fourth weeks are the key period of plant height development of the selected cultivars. A total of 4563 and 10507 differentially expressed genes (DEGs) were identified in the third and fourth weeks, respectively. Twenty modules were produced by the weighted gene co-expression network analysis (WGCNA). Eight modules were positively correlated with the plant height, and twelve other modules were negatively correlated. Combining with the analysis of DEGs and WGCNA, 13 genes in the signaling pathway of gibberellic acid (GA) and 7 genes in the signaling pathway of indole acetic acid (IAA) were identified. Hub genes related to plant height development were obtained in light of the significantly different expression levels (|log2FC| ≥ 1) at the critical stages. Moreover, GA3 treatment significantly induced the transcription expressions of the selected candidate genes, suggesting that GA signaling could play a key role in plant height development of banana. It provides an important gene resource for the regulation mechanism of banana plant development and assisted breeding of ideal plant architecture

Self-Assembling VO₂ Nanonet with High Switching Performance at Wafer-Scale

Technologically controlling nanostructures is essential to tailoring the functionalities and properties of nanomaterials. Various methods free from lithography-based techniques have been employed to fabricate 2D nanostructures; however it is still hard to achieve a well interconnected 2D regular nanostructure. Here, we demonstrate a facile chemical solution method to self-assemble a regular and interconnected VO₂ nanonet on the wafer scale. The nanonet shows a well-defined 2D truss network constructed by VO₂ nanorods with twinning relationships. The growth direction and crystallographic orientation of nanorods are synchronously controlled, leading to horizontally epitaxial growth of nanorods along three symmetric directions of the (001) single-crystal sapphire substrate. The unique nanonets enable the acquisition of excellent resistance switching properties and dramatic fatigue endurance. A large resistance change of near 5 orders with a 1.7 °C width of the hysteresis loop is characterized comparably to the properties of single crystals without detectable degradation after 500 cycles over the metal-to-insulator transition. It indicates that the nanonet can serve as an exceptional candidate for practical application in switching functional devices. Our findings offer a novel pathway for self-assembly of 2D ordered nanostructures, which would provide new opportunities for the bottom-up integration of nanodevices