Iron Tailings as Mineral Fillers and Their Effect on the Fatigue Performance of Asphalt Mastic.

Incorporating iron tailings (ITs) into asphalt represents a new method for waste-to-resource conversion. The objective of this study is to evaluate the fatigue performance of ITs as fillers in asphalt mastic and investigate the interaction and interfacial adhesion energy between asphalt and ITs. To achieve that, the particle size distributions of two ITs and limestone filler (LF) were tested through a laser particle size analyzer; the morphology and structure characteristics were obtained by scanning electronic microscopy (SEM), the mineral compositions were conducted through X-ray diffraction (XRD), and the chemical compositions were tested through X-ray Fluorescence Spectrometer (XRF). Furthermore, the fatigue properties of asphalt mastic and the interaction between asphalt binder and mineral fillers (ITs and LFs) were evaluated by Dynamic Shear Rheometer (DSR). The interfacial adhesion energy between ITs and asphalt binder were calculated through molecular dynamics simulation. In the end, the correlation between the test results and the fatigue life is established based on the gray correlation analysis, the environmental and economic benefits of iron tailings asphalt pavement are further evaluated. The results show that the particle size distribution of ITs is concentrated between 30 µm and 150 µm, and the main component is quartz. ITs have rich angularity and a higher interaction ability with asphalt. The adhesion energy of iron tailings filler to asphalt is less than that of limestone. The correlation degree of the interfacial adhesion energy and interaction between asphalt and mineral filler with asphalt mastic fatigue life is close to 0.58. Under the combined action of interaction ability and interfacial adhesion energy, the fatigue life of IT asphalt mastic meets the requirements. ITs as a partial replacement for mineral fillers in asphalt pavement have great environmental and social effectiveness.

Poplar glutathione S-transferase PtrGSTF8 contributes to reactive oxygen species scavenging and salt tolerance.

Glutathione S-transferases (GSTs) constitute a protein superfamily encoded by a large gene family and play a crucial role in plant growth and development. However, their precise functions in wood plant responses to abiotic stress are not fully understood. In this study, we isolated a Phi class glutathione S-transferase-encoding gene, PtrGSTF8, from poplar (Populus alba × P. glandulosa), which is significantly up-regulated under salt stress. Moreover, compared with wild-type (WT) plants, transgenic tobacco plants exhibited significant salt stress tolerance. Under salt stress, PtrGSTF8-overexpressing tobacco plants showed a significant increase in plant height and root length, and less accumulation of reactive oxygen species. In addition, these transgenic tobacco plants exhibited higher superoxide dismutase, peroxidase, and catalase activities and reduced malondialdehyde content compared with WT plants. Quantitative real-time PCR experiments showed that the overexpression of PtrGSTF8 increased the expression of numerous genes related to salt stress. Furthermore, PtrMYB108, a MYB transcription factor involved in salt resistance in poplar, was found to directly activate the promoter of PtrGSTF8, as demonstrated by yeast one-hybrid assays and luciferase complementation assays. Taken together, these findings suggest that poplar PtrGSTF8 contributes to enhanced salt tolerance and confers multiple growth advantages when overexpressed in tobacco.

Giant Carrier Mobility in a Room-Temperature Ferromagnetic VSi2N4 Monolayer.

Using density functional theory (DFT), we investigate that two possible phases of VSi2N4 (VSN) may be realized, one called the "H phase" corresponding to what is known from calculation and herein the other new "T phase" being stabilized by a biaxial tensile strain of 3%. Significantly, the H phase is predicted to display a giant carrier mobility of 1 × 106 cm2 V-1 s-1, which exceeds that for most 2D magnetic materials, with a Curie temperature (TC) exceeding room temperature and a band gap of 2.01 eV at the K point. Following the H-T phase transition, the direct band gap shifts to the Γ point and increases to 2.59 eV. The Monte Carlo (MC) simulations also indicate that TC of the T phase VSN can be effectively modulated by strain, reaching room temperature under a biaxial strain of -4%. These results show that VSN should be a promising functional material for future nanoelectronics.

Variation pattern in the macromolecular (cellulose, hemicelluloses, lignin) composition of cell walls in Pinus tabulaeformis tree trunks at different ages as revealed using multiple techniques.

The plant cell wall is a complex, heterogeneous structure primarily composed of cellulose, hemicelluloses, and lignin. Exploring the variations in these three macromolecules over time is crucial for understanding wood formation to enhance chemical processing and utilization. Here, we comprehensively analyzed the chemical composition of cell walls in the trunks of Pinus tabulaeformis using multiple techniques. In situ analysis showed that macromolecules accumulated gradually in the cell wall as the plant aged, and the distribution pattern of lignin was opposite that of polysaccharides, and both showed heterogenous distribution patterns. In addition, gel permeation chromatography (GPC) results revealed that the molecular weights of hemicelluloses decreased while that of lignin increased with age. Two-dimensional heteronuclear single quantum coherence nuclear magnetic resonance (2D-HSQC NMR) analysis indicated that hemicelluloses mainly comprised galactoglucomannan and arabinoglucuronoxylan, and the lignin types were mainly comprised guaiacyl (G) and p-hydroxyphenyl (H) units with three main linkage types: ß-O-4, ß-ß, and ß-5. Furthermore, the C-O bond (ß-O-4) signals of lignin decreased while the C-C bonds (ß-ß and ß-5) signals increased over time. Taken together, these findings shed light on wood formation in P. tabulaeformis and lay the foundation for enhancing the processing and use of wood and timber products.

3D reconstruction and multi-omics analysis reveal a unique pattern of embryogenesis in Ginkgo biloba.

Ginkgo (Ginkgo biloba L.) is one of the earliest extant species in seed plant phylogeny. Embryo development patterns can provide fundamental evidence for the origin, evolution, and adaptation of seeds. However, the architectural and morphological dynamics during embryogenesis in Ginkgo biloba (G. biloba) remain elusive. Herein, we obtained over 2200 visual slices from three stages of embryo development using micro-computed tomography imaging with improved staining methods. Based on 3D spatio-temporal pattern analysis, we found that a shoot apical meristem with seven highly differentiated leaf primordia, including apical and axillary leaf buds, is present in mature Ginkgo embryos. 3D rendering from the front, top, and side views showed two separate transport systems of tracheids located in the hypocotyl and cotyledon, representing a unique pattern of embryogenesis. Furthermore, the morphological dynamic analysis of secretory cavities indicated their strong association with cotyledons during development. In addition, we identified genes GbLBD25a (lateral organ boundaries domain 25a), GbCESA2a (cellulose synthase 2a), GbMYB74c (myeloblastosis 74c), GbPIN2 (PIN-FORMED 2) associated with vascular development regulation, and GbWRKY1 (WRKYGOK 1), GbbHLH12a (basic helix-loop-helix 12a), GbJAZ4 (jasmonate zim-domain 4) potentially involved in the formation of secretory cavities. Moreover, we found that flavonoid accumulation in mature embryos could enhance post-germinative growth and seedling establishment in harsh environments. Our 3D spatial reconstruction technique combined with multi-omics analysis opens avenues for investigating developmental architecture and molecular mechanisms during embryogenesis and lays the foundation for evolutionary studies of embryo development and maturation.

Atomic Positional Embedding-Based Transformer Model for Predicting the Density of States of Crystalline Materials.

The rapid advancement of machine learning has revolutionized quite a few science fields, leading to a surge in the development of highly efficient and accurate materials discovery methods. Recently, predictions of multiple related properties have received attention, with a particular emphasis on spectral properties, where the electronic density of states (DOS) stands out as the fundamental data with enormous potential to advance our understanding of crystalline materials. Leveraging the power of the Transformer framework, we introduce an Atomic Positional Embedding-Based Transformer (APET), which surpasses existing state-of-the-art models for predicting ab initio DOS. APET utilizes atomic periodical positions as its positional embedding, which incorporates all of the structural information in a crystal, providing a more complete and accurate representation. Furthermore, the interpretability of APET enables us to discover the underlying physical properties of materials with greater precision and accuracy.

Effects of Mo Particles Addition on the Microstructure and Properties of 316 L Stainless Steels Fabricated by Laser Powder Bed Fusion.

Application of the 316 L stainless steel (SS) is limited by its relatively low wear resistance, insufficient strength, and poor corrosion resistance in special environments. To this end, effects of Mo particles addition on the microstructure, mechanical properties, and corrosion resistance of the laser powder bed fusion (LPBF) 316 L SS are investigated in this study. The results show that the addition of Mo particles from 0 wt.% to 10 wt.% can modify the crystal orientation and improve the strength, wear resistance, and corrosion resistance of LPBF 316 L SSs. Particularly, the LPBF 316 L SS forms a biphasic structure with a similar ratio of α-Fe to γ-Fe with 5 wt.% Mo addition. As a result, the corresponding samples possess both the excellent toughness of austenitic SSs and the high strength and corrosion resistance of ferrite SSs, which reaches a high tensile strength of about 830 MPa, together with a low friction coefficient of 0.421 µ. Since the Mo particles addition is beneficial to increase the content of Cr2O3 on the samples surface from 13.48% to 22.68%, the corrosion current density of 316 L SS decreases by two orders of magnitude from 569 nA to 6 nA, while the mechanical properties remain favorable. This study is expected to serve as a reference for the preparation of LPBF SSs with excellent integrated performance.

Cuprate superconducting materials above liquid nitrogen temperature from machine learning.

The superconductivity of cuprates remains a challenging topic in condensed matter physics, and the search for materials that superconduct electricity above liquid nitrogen temperature and even at room temperature is of great significance for future applications. Nowadays, with the advent of artificial intelligence, research approaches based on data science have achieved excellent results in material exploration. We investigated machine learning (ML) models by employing separately the element symbolic descriptor atomic feature set 1 (AFS-1) and a prior physics knowledge descriptor atomic feature set 2 (AFS-2). An analysis of the manifold in the hidden layer of the deep neural network (DNN) showed that cuprates still offer the greatest potential as superconducting candidates. By calculating the SHapley Additive exPlanations (SHAP) value, it is evident that the covalent bond length and hole doping concentration emerge as the crucial factors influencing the superconducting critical temperature (Tc). These findings align with our current understanding of the subject, emphasizing the significance of these specific physical quantities. In order to improve the robustness and practicability of our model, two types of descriptors were used to train the DNN. We also proposed the idea of cost-sensitive learning, predicted the sample in another dataset, and designed a virtual high-throughput search workflow.

Multiscale microscopy to decipher plant cell structure and dynamics.

New imaging methodologies with high contrast and molecular specificity allow researchers to analyze dynamic processes in plant cells at multiple scales, from single protein and RNA molecules to organelles and cells, to whole organs and tissues. These techniques produce informative images and quantitative data on molecular dynamics to address questions that cannot be answered by conventional biochemical assays. Here, we review selected microscopy techniques, focusing on their basic principles and applications in plant science, discussing the pros and cons of each technique, and introducing methods for quantitative analysis. This review thus provides guidance for plant scientists in selecting the most appropriate techniques to decipher structures and dynamic processes at different levels, from protein dynamics to morphogenesis.

Environmental Cues Contribute to Dynamic Plasma Membrane Organization of Nanodomains Containing Flotillin-1 and Hypersensitive Induced Reaction-1 Proteins in Arabidopsis thaliana.

Plasma membranes are heterogeneous and contain multiple functional nanodomains. Although several signaling proteins have been shown to function by moving into or out of nanodomains, little is known regarding the effects of environmental cues on nanodomain organization. In this study, we investigated the heterogeneity and organization of distinct nanodomains, including those containing Arabidopsis thaliana flotillin-1 (AtFlot1) and hypersensitive induced reaction-1 proteins (AtHIR1), in response to biotic and abiotic stress. Variable-angle total internal reflection fluorescence microscopy coupled with single-particle tracking (SPT) revealed that AtFlot1 and AtHIR1 exhibit different lateral dynamics and inhabit different types of nanodomains. Furthermore, via SPT and fluorescence correlation spectroscopy, we observed lower density and intensity of AtFlot1 fluorescence in the plasma membrane after biotic stress. In contrast, the density and intensity of signal indicating AtHIR1 markedly increased in response to biotic stress. In response to abiotic stress, the density and intensity of both AtFlot1 and AtHIR1 signals decreased significantly. Importantly, SPT coupled with fluorescence recovery after photobleaching revealed that biotic and abiotic stress can regulate the dynamics of AtFlot1; however, only the abiotic stress can regulate AtHIR1 dynamics. Taken together, these findings suggest that a plethora of highly distinct nanodomains coexist in the plasma membrane (PM) and that different nanodomains may perform distinct functions in response to biotic and abiotic stresses. These phenomena may be explained by the spatial clustering of plasma membrane proteins with their associated signaling components within dedicated PM nanodomains.

Physics-Based Feature Makes Machine Learning Cognizing Crystal Properties Simple.

Machine learning (ML) accelerates the rational design and discovery of materials, where the feature plays a critical role in the ML model training. We propose a low-cost electron probability waves (EPW) descriptor based on electronic structures, which is extracted from high-symmetry points in the Brillouin zone. In the task of distinguishing ferromagnetic or antiferromagnetic material, it achieves an accuracy (ACC) at 0.92 and an area under the receiver operating characteristic curve (AUC) at 0.83 by 10-fold cross-validation. Furthermore, EPW excels at classifying metal/semiconductors and judging the direct/indirect bandgap of semiconductors. The distribution of electron clouds is an essential criterion for the origin of ferromagnetism, and EPW acts as an emulation of the electronic structure, which is the key to the achievements. Our EPW-based ML model obtains ACC and AUC equivalent to crystal graph features-based deep learning models for tasks with physical recognitions in electronic states.

Plant multiscale networks: charting plant connectivity by multi-level analysis and imaging techniques.

In multicellular and even single-celled organisms, individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation. Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes. Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project. In plant science, network analysis has similarly been applied to study the connectivity of plant components at the molecular, subcellular, cellular, organic, and organism levels. Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype. In this review, we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities. We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants. Finally, we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.

Dynamic spatial reorganization of BSK1 complexes in the plasma membrane underpins signal-specific activation for growth and immunity.

Growth and immunity are opposing processes that compete for cellular resources, and proper resource allocation is crucial for plant survival. BSK1 plays a key role in the regulation of both growth and immunity by associating with BRI1 and FLS2, respectively. However, it remains unclear how two antagonistic signals co-opt BSK1 to induce signal-specific activation. Here we show that the dynamic spatial reorganization of BSK1 within the plasma membrane underlies the mechanism of signal-specific activation for growth or immunity. Resting BSK1 localizes to membrane rafts as complexes. Unlike BSK1-associated FLS2 and BRI1, flg22 or exogenous brassinosteroid (BR) treatment did not decrease BSK1 levels at the plasma membrane (PM) but rather induced BSK1 multimerization and dissociation from FLS2/BSK1 or BRI1/BSK1, respectively. Moreover, flg22-activated BSK1 translocated from membrane rafts to non-membrane-raft regions, whereas BR-activated BSK1 remained in membrane rafts. When applied together with flg22, BR suppressed various flg22-induced BSK1 activities such as BSK1 dissociation from FLS2/BSK1, BSK1 interaction with MAPKKK5, and BSK translocation together with MAPKKK5. Taken together, this study provides a unique insight into how the precise control of BSK1 spatiotemporal organization regulates the signaling specificity to balance plant growth and immunity.

In vivo single-particle tracking of the aquaporin AtPIP2;1 in stomata reveals cell type-specific dynamics.

Aquaporins such as the plasma membrane intrinsic proteins (PIPs) allow water to move through cell membranes and are vital for stomatal movement in plants. Despite their importance, the dynamic changes in aquaporins during water efflux and influx have not been directly observed in real time in vivo. Here, to determine which factors regulate these changes during the bidirectional translocation of water, we examined aquaporin dynamics during the stomatal immune response to the bacterial flagellin-derived peptide flg22. The Arabidopsis (Arabidopsis thaliana) aquaporin mutant pip2;1 showed defects in the flg22-induced stomatal response. Variable-angle total internal reflection fluorescence microscopy revealed that the movement dynamics and dwell times of AQ6]GFP-AtPIP2;1 in guard cells and subsidiary cells exhibited cell type-specific dependencies on flg22. The cytoskeleton, rather than the cell wall, was the major factor regulating AtPIP2;1 dynamics, although both the cytoskeleton and cell wall might form bounded domains that restrict the diffusion of AtPIP2;1 in guard cells and subsidiary cells. Finally, our analysis revealed the different roles of cortical actin and microtubules in regulating AtPIP2;1 dynamics in guard cells, as well as subsidiary cells, under various conditions. Our observations shed light on the heterogeneous mechanisms that regulate membrane protein dynamics in plants in response to pathogens.

The RALF1-FERONIA interaction modulates endocytosis to mediate control of root growth in Arabidopsis.

The interaction between the receptor-like kinase (RLK) FERONIA (FER) and the secreted peptide RAPID ALKALINIZATION FACTOR1 (RALF1) is vital for development and stress responses in Arabidopsis Ligand-induced membrane dynamics affect the function of several RLKs, but the effects of the RALF1-FER interaction on the dynamics of FER and the ensuing effects on its functionality are poorly understood. Here, we show that RALF1 modulated the dynamics and partitioning of FER-GFP at the plasma membrane (PM). Moreover, FER was internalized by both clathrin-mediated endocytosis (CME) and clathrin-independent endocytosis (CIE) under steady-state conditions. After RALF1 treatment, FER-GFP internalization was primarily enhanced via the CME pathway, raising FER-GFP levels in the vacuole. RALF1 treatment also modulated trafficking of other PM proteins, such as PIN2-GFP and BRI1-GFP, increasing their vacuolar levels by enhancing their internalization. Importantly, blocking CME attenuated RALF1-mediated root growth inhibition independently of RALF1-induced early signaling, suggesting that the RALF1 can also exert its effects via the CME pathway. These findings reveal that the RALF1-FER interaction modulates plant growth and development, and this might also involve endocytosis of PM proteins.

Organization and dynamics of functional plant membrane microdomains.

Plasma membranes are heterogeneous and laterally compartmentalized into distinct microdomains. These membrane microdomains consist of special lipids and proteins and are thought to act as signaling platforms. In plants, membrane microdomains have been detected by super-resolution microscopy, and there is evidence that they play roles in several biological processes. Here, we review current knowledge about the lipid and protein components of membrane microdomains. Furthermore, we summarize the dynamics of membrane microdomains in response to different stimuli. We also explore the biological functions associated with membrane microdomains as signal integration hubs. Finally, we outline challenges and questions for further studies.

Phosphorylation-Mediated Dynamics of Nitrate Transceptor NRT1.1 Regulate Auxin Flux and Nitrate Signaling in Lateral Root Growth.

The dual-affinity nitrate transceptor NITRATE TRANSPORTER1.1 (NRT1.1) has two modes of transport and signaling, governed by Thr-101 (T101) phosphorylation. NRT1.1 regulates lateral root (LR) development by modulating nitrate-dependent basipetal auxin export and nitrate-mediated signal transduction. Here, using the Arabidopsis (Arabidopsis thaliana) NRT1.1T101D phosphomimetic and NRT1.1T101A nonphosphorylatable mutants, we found that the phosphorylation state of NRT1.1 plays a key role in NRT1.1 function during LR development. Single-particle tracking revealed that phosphorylation affected NRT1.1 spatiotemporal dynamics. The phosphomimetic NRT1.1T101D form showed fast lateral mobility and membrane partitioning that facilitated auxin flux under low-nitrate conditions. By contrast, nonphosphorylatable NRT1.1T101A showed low lateral mobility and oligomerized at the plasma membrane (PM), where it induced endocytosis via the clathrin-mediated endocytosis and microdomain-mediated endocytosis pathways under high-nitrate conditions. These behaviors promoted LR development by suppressing NRT1.1-controlled auxin transport on the PM and stimulating Ca2+-ARABIDOPSIS NITRATE REGULATED1 signaling from the endosome.