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The soil dielectric constant model is essential for retrieving soil properties based on microwave remote sensing. However, the existing saline soil dielectric constant models perform poorly in simulating the dielectric constant of soil with high water content and salinity. In this study, the Wang Yueru (WYR) saline soil dielectric constant model, which was demonstrated to perform well in describing the effect of salinity and moisture on the dielectric constant, was validated based on experimental measurements of soil samples under different water content and salinity degrees. Furthermore, we adjusted the model form, refitted the empirical coefficient in the model, and finally acquired a two-stage model for simulating the soil dielectric constant. The enhanced model was validated under different soil moisture and salinity ranges using experimental measurements of soil samples. Compared to the original model, the proposed model exhibits a larger improvement in simulating the soil dielectric constant, and the RMSE of the simulated results dramatically decreased from 7.3 to 1.6, especially for soil with high salinity and water content. On this basis, a model suitable for L-band microwave was established. This model is of great significance for studying soil dielectric characteristics and retrieving soil parameters based on L-band data. Furthermore, this model can be used to retrieve soil salinity and water content using microwave remote sensing under a broadened application situation, such as in saline-alkali soils, wetlands, and salt marshes. [ABSTRACT FROM AUTHOR]

[Display omitted] • ZIF-67 was etched to produce CoCo-LDH while coated by organic polydopamine (PDA) concomitantly. A PDA modified hollow cage lamellar LDH nanostructure loaded with TPP was constructed (TPP@LDH@Co-PDA). • Polydopamine enhanced compatibility between flame retardants and the matrix, boosting mechanical strength. • An inspiration for enhancing the thermal stability and compatibility of LDH, and to solve the migration problem of phosphorous small molecules. Layered double hydroxides (LDH) have irreplaceable advantages in the field of polymer flame retardancy, but their thermal stability and compatibility with matrix still need to be improved. In this paper, the bottom-up method is adopted, and the phosphorus series flame retardant triphenyl phosphate (TPP) was first encapsulated inside ZIF-67. On this basis, ZIF-67 was etched to produce LDH while modified by polydopamine (PDA) concomitantly. An organic coated polydopamine hollow cage lamellar LDH microstructure loaded with TPP was constructed, and its structure-performance relationship was verified. When 2 wt% TPP@LDH@Co-PDA was added to the epoxy resin, the LOI value of the composite was increased to 29.4 %, the peak heat release was reduced by 43.1 %, and the smoke release was significantly reduced. The unique microstructure endows epoxy composites with good flame retardancy, improves mechanical properties, and provides a new solution to the migration problem of phosphorous based flame retardants. [ABSTRACT FROM AUTHOR]

Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors. [ABSTRACT FROM AUTHOR]

Motivation Recently, deep learning has become the mainstream methodology for drug–target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ignore the individual information of sequence elements, resulting in poor sequence feature representations. On the other hand, without prior biological knowledge, the prediction of drug–target binding regions based on attention weights of a deep neural network could be difficult to verify, which may bring adverse interference to biological researchers. Results We propose a novel Multi-Functional and Robust Drug–Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new biological sequence feature extraction block, namely BioMLP, that assists the model in extracting individual features of sequence elements. Then, we propose a new Elem-feature fusion block to refine the extracted features. After that, we construct a Mix-Decoder block that extracts drug–target interaction information and predicts their binding regions simultaneously. Last, we evaluate MFR-DTA on two benchmarks consistently with the existing methods and propose a new dataset, sc-PDB, to better measure the accuracy of binding region prediction. We also visualize some samples to demonstrate the locations of their binding sites and the predicted multi-scale interaction regions. The proposed method achieves excellent performance on these datasets, demonstrating its merits and superiority over the state-of-the-art methods. Availability and implementation https://github.com/JU-HuaY/MFR. [ABSTRACT FROM AUTHOR]

[Display omitted] • The relationships between network topological characteristics and the different ESVs were analyzed. • The important sources and corridors are mainly located along the sides of the Wei River and the Fen River. • Higher ESVs related to water resources have better network connectivity. • The "one central source and three forested belts" radially ecological arrangement is planned. There are numerous ecological challenges that are of considerable concern caused by urbanization and natural calamities. The spatial pattern of the landscape is of great significance in maintaining species diversity and regulating material cycles and individual ecophysiological processes as a medium for ecosystem service functions. Resolving the conflicting relationships between people and land necessitates the precise identification of essential ecological sources. Therefore, there is an urgent need to investigate the relationship between ecological sources and ecosystem service values (ESVs) and to optimize the matter and energy.connectivity network. The study optimizes ecological network simulation using a penetration threshold, population density inverse distance formula, and geodetector weight analysis, constructs ecological spatial networks and evaluates their structure using the minimum cumulative resistance (MCR) model and complex network analysis, and discusses the relationship between network topological characteristics (NTCs) and the spatial pattern of the various ESVs. The results show that the major sources and corridors are primarily along the banks of the Wei River and the Fen River. The clustering coefficient has a significant negative correlation with both water supply and water regulation service values. It is found that the sources with higher ecological service values related to water resources have better network connectivity. This result plays a major reference role for the identification of several crucial ecological components, such as sources, nodes and stepping stones, in the middle Yellow River basin (MYRB). Ultimately, we propose strengthening the ecological protection of the eastern part of the Qinling Mountains and increasing the ecological communication capacity of the forestland on both sides of the river valley to create an optimized ecological pattern. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influence of meteorological and land surface hydrothermal on LAI in mountainous areas was assessed. • A clear greening trend was observed in Yan Mountains, and the greening trend was the largest in summer. • LAI was negatively correlated with land surface temperature and soil moisture but positively correlated with precipitation and air temperature. • The hysteresis of LAI of different vegetation types to changes in hydrothermal conditions was explored. Changes in hydrothermal conditions have significant effects on vegetation, but there is still a lack of understanding of how vegetation responds to land surface (surface temperature and soil moisture) and meteorological (temperature and precipitation) conditions in mountain regions. This study examined the trends of leaf area index (LAI) in the Yan Mountains over the last four decades using Global Land Surface Satellite (GLASS) data. The results showed a persistent increase of LAI (greening) over 20 % to 80 % of the study area in growing season, spring, summer and autumn. Anthropogenic activities caused the greening trend by crop management before 2000 and afforestation after 2000. The increasing rate of LAI varied with elevation, and the most significant increase occurred in areas between 300 and 900 m, and the lowest increase occurred in areas below 300 m. Moreover, we found that LAI was negatively correlated with land surface temperature and soil moisture, but positively correlated with precipitation and air temperature. The time-lag effect was found between hydrothermal factors and LAI in the past four decades. There was a time lag of 2–3 months between LAI changes and temperature/precipitation during the early and late stages of the growing season, and a time lag of 0–1 month during the middle stage. Specifically, there was no time lag in vegetation response to surface soil moisture, and a time lag of 2–3 months in vegetation response to land surface temperature from July to October. Our findings provide insights into how vegetation adapts to land surface and climatic hydrothermal conditions in mountain regions and can be used by governments to develop policies for ecological protection. [ABSTRACT FROM AUTHOR]

[Display omitted] • Design of MOF-derived hollow hybrids covered with a polyphosphazene coating. • Cobalt-phosphorus-nitrogen flame retardant elements synergistic effect. • Mismatch structure significantly improves the dispersity of fillers in EP. • The hybrids endow EP with better flame retardancy and mechanical performance. Polyphosphazene has drawn an abundance of attention as a flame retardant due to its eco-friendliness and phosphorus-nitrogen synergistic effect. ZIF-67 and cobalt compounds derived from cobalt ions released by ZIF-67 were employed as dual templates for loading polyphosphazene. We skillfully designed a polyphosphazene hollow hybrid material via the polymerization of phosphazene accompanied by the etching of ZIF-67 and cobalt compounds by generated HCl. The hybrid material (CPPHS) was composed of cobalt-doped polyphosphazene hollow sub-micron polyhedrons (CP) and polyphosphazene hollow nanospheres (PHNS) with different dimensions and magnitudes of sizes. After adding 2.0 wt% CPPHS, the limiting oxygen index of epoxy resin increased to 27.6%, while the peak heat release rate and total smoke production decreased by 40.1% and 38.8%, respectively. The formation of a continuous and strongly extended dense char layer in the EP composites was fostered by the synergistic effect of Co-P-N flame retardant components, significantly decreasing the heat flow and combustible gas exchange rate and effectively hindering the combustion chain reaction. Furthermore, the unique dimensional mismatch structure of CPPHS enhanced its dispersion in the EP matrix, enabling the EP to have superior mechanical properties that match realistic application requirements, consequently expanding the variety of applications for fire-resistant EP composites. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a crucial biophysical parameter related closely to the land–atmosphere interface. Satellite thermal infrared measurement provides an effective method to derive LST on regional and global scales, but it is very hard to acquire simultaneously high spatiotemporal resolution LST due to its limitation in the sensor design. Recently, many LST downscaling and spatiotemporal image fusion methods have been widely proposed to solve this problem. However, most methods ignored the spatial heterogeneity of LST distribution, and there are inconsistent image textures and LST values over heterogeneous regions. Thus, this study aims to propose one framework to derive high spatiotemporal resolution LSTs in heterogeneous areas by considering the optimal selection of LST predictors, the downscaling of MODIS LST, and the spatiotemporal fusion of Landsat 8 LST. A total of eight periods of MODIS and Landsat 8 data were used to predict the 100-m resolution LST at prediction time tp in Zhangye and Beijing of China. Further, the predicted LST at tp was quantitatively contrasted with the LSTs predicted by the regression-then-fusion strategy, STARFM-based fusion, and random forest-based regression, and was validated with the actual Landsat 8 LST product at tp. Results indicated that the proposed framework performed better in characterizing LST texture than the referenced three methods, and the root mean square error (RMSE) varied from 0.85 K to 2.29 K, and relative RMSE varied from 0.18 K to 0.69 K, where the correlation coefficients were all greater than 0.84. Furthermore, the distribution error analysis indicated the proposed new framework generated the most area proportion at 0~1 K in some heterogeneous regions, especially in artificial impermeable surfaces and bare lands. This means that this framework can provide a set of LST dataset with reasonable accuracy and a high spatiotemporal resolution over heterogeneous areas. [ABSTRACT FROM AUTHOR]

Biological tissues possess high stretchability and nonlinear mechanical response owing to their wavy and crimped microstructure. The metamaterials exhibit unusual physical and mechanical properties, dominated by the configuration of artificial periodic microstructures. However, the development of bio-inspired designs and three-dimensional (3D) soft metamaterials that can achieve a variety of unusual mechanical properties remains challenging. Here, we reported a class of 3D horseshoe-shaped soft network metamaterials with tunable nonlinear mechanical response and tension-torsion coupling effect. Based on the 3D horseshoe-shaped microstructure and octahedron lattice arrangement, the designed 3D soft network metamaterials can exhibit J-shaped stress-strain curve and large angle of torsion with large levels of deformation under tension. The mechanical properties of the 3D soft network metamaterials in different characteristic directions are investigated, where a negative Poisson's ratio appears. Furthermore, the extended design that the 3D horseshoe-shaped microstructures are replaced by the semi-elliptic and quarter-arc-straight joint microstructures can tune a wide range of desired J-shaped stress-strain curves. Thus, the multi-unusual mechanical properties provide potential uses in multi-functional metamaterials and flexible bio-integrated devices. [ABSTRACT FROM AUTHOR]

The hydrogenation of carbon dioxide (CO2) to produce light olefins is one of the most promising ways to utilize CO2 in power plant flue gas. However, the low concentration of CO2 (~10%) and the existence of water steam in the flue gas pose great challenges for the catalyst design. To address these problems, we introduced a Mg promoter and hydrophobic component into the Fe-based catalyst to improve the CO2 adsorption capacity and weaken the negative effects of water. The yield of light olefins on an optimized multifunctional Fe-based catalyst increased by 37% in low-concentration CO2 hydrogenation with water steam. A variety of characterizations proved that the Mg promoter played critical roles in regulating the adsorption capacity of CO2, increasing the surface electron density of Fe species, and promoting the formation of iron carbide active sites. The hydrophobic component mainly contributed to constraining the oxidation of iron carbides via water steam. It benefited from the rational design of the catalyst, showing how our multifunctional Fe-based catalyst has great potential for practical application in CO2 utilization. [ABSTRACT FROM AUTHOR]

As an essential task in protein structure and function prediction, protein fold recognition has attracted increasing attention. The majority of the existing machine learning-based protein fold recognition approaches strongly rely on handcrafted features, which depict the characteristics of different protein folds; however, effective feature extraction methods still represent the bottleneck for further performance improvement of protein fold recognition. As a powerful feature extractor, deep convolutional neural network (DCNN) can automatically extract discriminative features for fold recognition without human intervention, which has demonstrated an impressive performance on protein fold recognition. Despite the encouraging progress, DCNN often acts as a black box, and as such, it is challenging for users to understand what really happens in DCNN and why it works well for protein fold recognition. In this study, we explore the intrinsic mechanism of DCNN and explain why it works for protein fold recognition using a visual explanation technique. More specifically, we first trained a VGGNet-based DCNN model, termed VGGNet-FE, which can extract fold-specific features from the predicted protein residue–residue contact map for protein fold recognition. Subsequently, based on the trained VGGNet-FE, we implemented a new contact-assisted predictor, termed VGGfold, for protein fold recognition; we then visualized what features were extracted by each of the convolutional layers in VGGNet-FE using a deconvolution technique. Furthermore, we visualized the high-level semantic information, termed fold-discriminative region, of a predicted contact map from the localization map obtained from the last convolutional layer of VGGNet-FE. It is visually confirmed that VGGNet-FE could effectively extract distinct fold-discriminative regions for different types of protein folds, thereby accounting for the improved performance of VGGfold for protein fold recognition. In summary, this study is of great significance for both understanding the working principle of DCNNs in protein fold recognition and exploring the relationship between the predicted protein contact map and protein tertiary structure. This proposed visualization method is flexible and applicable to address other DCNN-based bioinformatics and computational biology questions. The online web server of VGGfold is freely available at http://csbio.njust.edu.cn/bioinf/vggfold/. [ABSTRACT FROM AUTHOR]

The selection of predictor variables is a crucial issue in building a digital mapping model of potentially toxic elements (PTEs) in soil. Traditionally, the predictor variables for mapping models of soil PTEs have been chosen from sets of spatial parameters or spectral parameters derived from geographical environmental data. However, the enrichment of soil PTEs exhibits significant variations in both spatial and temporal dimensions, with the temporal dimension often being overlooked in the selection of predictor variables for digital mapping models. This limitation hampers the robustness and generalizability of the models. Therefore, multi-source geographical data were used in this study to determine three temporal indices for characterizing the enrichment process of soil PTEs in temporal dimensions, and additionally to construct the temporal-spatial-spectral (TSS) covariate combinations. The random forest (RF) algorithm was used to map soil PTEs at a regional scale. Results showed that: (1) When using spatial parameters or spectral parameters as predictor variables and measured Pb content as the dependent variable, the values of the model performance indicator RPIQ (ratio of performance to inter-quartile range) were 2.66 and 2.27, respectively. After incorporating the temporal parameters into the model input, values of RPIQ for the RF model reached 3.55 (using spatial-temporal covariates) and 3.21 (using spectral-temporal covariates), representing performance improvements of 33.46% and 41.41%, respectively. (2) The RF model constructed with the temporal-spatial-spectral covariates achieved satisfactory mapping accuracy (R 2 = 0.85; RMSE = 0.80 mg kg−1; RPIQ = 4.09). (3) The soil Pb content in the western and northeastern regions was relatively high, while the remaining areas exhibited lower Pb levels, mainly due to industrial activities. (4) The mapping results of Pb obtained in this study were superior to other mapping methods, such as ordinary kriging, artificial neural networks, and multivariate linear regression methods. The soil PTE mapping technique employed in this study that combined TSS covariates with the RF provided an effective methodological approach for preventing soil pollution, controlling environmental risk, and improving soil management. [Display omitted] • A remote sensing-based strategy for mapping regional PTE was developed. • A mapping model was constructed using TSS covariates and RF. • Three temporal indices were designed to characterize soil PTEs. • Satisfactory mapping results for soil PTEs were achieved with RF/TSS (RPIQ = 4.09). [ABSTRACT FROM AUTHOR]

The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958). [ABSTRACT FROM AUTHOR]

[Display omitted] • A simple method for synthesizing nano-composite flame retardants with hollow prismatic structures. • POSS can be used as a structure-directing agent to maintain the nanostructure. • Cobalt-silicon synergy for enhanced flame retardancy and smoke suppression. • Nano-composite flame retardants achieve good dispersion in epoxy resin. Polyhedral oligomeric silsesquioxanes (POSS) containing transition metals have excellent flame retardant properties. However, the flame retardant obtained by direct coordination of transition metal cobalt (Co) with POSS tends to agglomerate, resulting in unsatisfactory flame retardant efficiency. Herein, a synthetic route was designed to use a water-soluble cobalt-polyvinylpyrrolidone (Co-PVP) complex as the template and cobalt source to first grow cobalt-metal–organic frameworks (Co-MOFs) on its surface, and then an aqueous solution of Na-POSS was used as both a structure-directing agent and an etching agent to load POSS and remove the template synchronously. Finally, a hollow prism with POSS wrapped around the Co-MOF (designated Co-MOF@POSS) was obtained. The hollow structure not only showed an optimized dispersion in the matrix but also exposed more active sites, catalyzing the formation of the carbon layer, and the silica ceramic layer formed by POSS further enhanced the strength of the carbon layer. An excellent flame retardant effect was obtained by adding Co-MOF@POSS to epoxy at 2 wt%. The heat release and smoke release of the epoxy composites were significantly reduced by the synergistic effect of cobalt and silicon, and the addition of Co-MOF@POSS improved the limiting oxygen index to 27.5% and decreased the peak of heat release rate, total heat release, and total smoke production by 34.6%, 16.6% and 23.8%, respectively. This work provides new ideas for the preparation of MOF and POSS-based flame retardants. [ABSTRACT FROM AUTHOR]

The infrared and visible image fusion task aims to generate a single image that preserves complementary features and reduces redundant information from different modalities. Although convolutional neural networks (CNNs) can effectively extract local features and obtain better fusion performance, the size of the receptive field limits its feature extraction ability. Thus, the Transformer architecture has gradually become mainstream to extract global features. However, current Transformer-based fusion methods ignore the enhancement of details, which is important to image fusion tasks and other downstream vision tasks. To this end, a new super feature attention mechanism and the wavelet-guided pooling operation are applied to the fusion network to form a novel fusion network, termed SFPFusion. Specifically, super feature attention is able to establish long-range dependencies of images and to fully extract global features. The extracted global features are processed by wavelet-guided pooling to fully extract multi-scale base information and to enhance the detail features. With the powerful representation ability, only simple fusion strategies are utilized to achieve better fusion performance. The superiority of our method compared with other state-of-the-art methods is demonstrated in qualitative and quantitative experiments on multiple image fusion benchmarks. [ABSTRACT FROM AUTHOR]

Accurate information on grassland above-ground biomass (AGB) is critical to better understanding the carbon cycle and conserve grassland resources. As a climate-sensitive key ecological function area, it is important to accurately estimate the grassland AGB of the Tibetan Plateau. Sentinel-2 (S2) images have advantages in reducing mixed pixels and the scale effect for remote sensing, while the data volume is correspondingly larger. In order to improve the estimation accuracy while reducing the data volume required for AGB estimation and improving the computational efficiency, this study used the Recursive Feature Elimination (RFE) algorithm to find the optimal feature set and compared the performance of the Cubist, Gradient Boosting Regression Tree (GBRT), random forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms for estimating AGB. In this study, ten S2 bands, ten S2-derived vegetation indexes, 218 pieces of AGB field survey data, four types of meteorological data and three types of topographic data were used as the alternative input features for the AGB estimation model. The impurity and permutation importance were used as the feature importance calculation method input to the RFE, and the Cubist, GBRT, RF and XGBoost algorithms were used to construct the AGB estimation models. The results showed that the RF algorithm based on the monthly average temperature (T), elevation, Normalized Difference Phenology Index (NDPI), Normalized Difference Infrared Index (NDII) and Palmer Drought Severity Index (PDSI) performed best ( R 2 = 0.8838, RMSE = 35.05 g/m 2 , LCCC = 2.44, RPPD = 0.91). The above findings suggest that the RF model based on the features related to temperature, altitude, humidity and leaf water content is beneficial to estimate the grassland AGB on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Conventional subspace learning approaches based on image gradient orientations only employ first-order gradient information, which may ignore second-order or higher-order gradient information. Moreover, recent researches on the human vision system (HVS) have uncovered that the neural image is a landscape or a surface whose geometric properties can be captured through second-order gradient information. The second-order image gradient orientations (SOIGO) can mitigate the adverse effect of noise in face images. To reduce the redundancy of SOIGO, we propose compact SOIGO (CSOIGO) by applying linear complex principal component analysis (PCA) in SOIGO. To be more specific, the SOIGO of training data are firstly obtained. Then, linear complex PCA is applied to obtain features of reduced dimensionality. Combined with collaborative-representation-based classification (CRC) algorithm, the classification performance of CSOIGO is further enhanced. CSOIGO is evaluated under real-world disguise, synthesized occlusion, and mixed variations. Under the real disguise scenario, CSOIGO makes 2.67% and 1.09% improvement regarding accuracy when one and two neutral face images per subject are used as training samples, respectively. For the mixed variations, CSOIGO achieves a 0.86% improvement in terms of accuracy. These results indicate that the proposed method is superior to its competing approaches with few training samples, and even outperforms some prevailing deep-neural-network-based approaches. [ABSTRACT FROM AUTHOR]

SUMMARY: During chlorophyll degradation, large amounts of the isoprenoid alcohol phytol are released. The pathway of phytol catabolism has been studied in humans, because chlorophyll is part of the human diet, but little is known for plants. In humans, phytanoyl‐CoA derived from phytol is degraded via α‐oxidation by phytanoyl‐CoA hydroxylase (PAHX) and 2‐hydroxy‐phytanoyl‐CoA lyase (HPCL). Arabidopsis contains two sequences homologous to the human proteins AtPAHX and AtHPCL. Insertional mutants of Arabidopsis (pahx, hpcl) were grown under N deprivation to stimulate chlorophyll breakdown or supplemented with phytol to increase the endogenous amount of phytol. During N deprivation, chlorophyll, phytol, phytenal, upstream metabolites of phytol breakdown, and tocopherol and fatty acid phytyl esters, alternative phytol‐derived lipids, accumulated in pahx and hpcl mutants, in line with the scenario that the mutations interfere with phytol degradation. AtHPCL was localized to the peroxisomes. Expression analysis of the AtHPCL sequence in the yeast Δpxp1 or Δmpo1 mutants followed by supplementation with 2‐hydroxy‐palmitic acid and enzyme assays of peroxisomal proteins from Col‐0 and hpcl plants with 2‐hydroxy‐stearoyl‐CoA revealed that AtHPCL harbors 2‐hydroxy‐acyl‐CoA lyase activity. The α‐dioxygenases αDOX1 and αDOX2 are involved in α‐oxidation of fatty acids and could be involved in an alternative pathway of phytol degradation. However, phytol‐related lipids in the αdox1, αdox2, or αdox1 αdox2 mutants were not altered compared with Col‐0, indicating that αDOX1 and αDOX2 are not involved in phytol degradation. These results demonstrate that phytol degradation in Arabidopsis involves α‐oxidation by AtPAHX and AtHPCL, but that it is independent of αDOX1/αDOX2. Significance Statement: The degradation and turnover of phytol, the hydrophobic anchor of chlorophyll, in plants is poorly understood. We show here that phytol degradation in Arabidopsis proceeds via α‐oxidation catalyzed by phytanoyl‐CoA hydroxylase (PAHX) and 2‐hydroxy‐phytanoyl‐CoA lyase (HPCL), but is independent of the α‐dioxygenases αDOX1 and αDOX2. Therefore, the pathway of phytol degradation is related to the respective human pathway. [ABSTRACT FROM AUTHOR]

• Seasonal timing strongly regulates the response of N 2 O emissions to extreme droughts. • Early drought affected N 2 O emission with high interannual variability. • Middle drought consistently suppressed N 2 O emissions. • Late drought had little effect on N 2 O fluxes. Terrestrial ecosystems are important sources of nitrous oxide (N 2 O), a powerful greenhouse gas which can be strongly impacted by increasing droughts in association with climate change. However, detailed information on whether and how drought timing regulates N 2 O fluxes is still lacking. Here, we conducted a 3-year field experiment on a semiarid grassland in which extreme drought was imposed in either early-, mid-, or late-growing seasons repeatedly from 2014 to 2016. We found that early drought affected N 2 O emission with high interannual variability (increased, decreased and unchanged N 2 O emission in 2014, 2015, and 2016, respectively), coincident with changes in inorganic nitrogen (SIN), dissolve organic carbon (DOC), microbial biomass carbon (MBC), and soil functional genes (bacterial amoA , nirK , nirS , and nosZ). However, middle drought consistently suppressed N 2 O emissions due to simultaneous decreases in MBC, DOC and the abundances of archaeal amoA , nirK , and narG genes , causing the largest reduction in N 2 O emissions across the three years. In contrast, late drought had little effect on N 2 O fluxes, even though DOC and SIN decreased and the abundance of nirK , nirS , and nosZ increased. As a result, soil organic C and mineral N availability and functional gene abundances were not always robust factors for predicting N 2 O emissions under droughts across all treatments, except for abundance of AOA and nosZ. Our results highlight the vital role of seasonal timing in regulating the response of N 2 O emissions to extreme droughts. [ABSTRACT FROM AUTHOR]