Crystal Violet Selectively Detects Aß Oligomers but Not Fibrils In Vitro and in Alzheimer's Disease Brain Tissue.

Deposition of extracellular Amyloid Beta (Aß) and intracellular tau fibrils in post-mortem brains remains the only way to conclusively confirm cases of Alzheimer's Disease (AD). Substantial evidence, though, implicates small globular oligomers instead of fibrils as relevant biomarkers of, and critical contributors to, the clinical symptoms of AD. Efforts to verify and utilize amyloid oligomers as AD biomarkers in vivo have been limited by the near-exclusive dependence on conformation-selective antibodies for oligomer detection. While antibodies have yielded critical evidence for the role of both Aß and tau oligomers in AD, they are not suitable for imaging amyloid oligomers in vivo. Therefore, it would be desirable to identify a set of oligomer-selective small molecules for subsequent development into Positron Emission Tomography (PET) probes. Using a kinetics-based screening assay, we confirm that the triarylmethane dye Crystal Violet (CV) is oligomer-selective for Aß42 oligomers (AßOs) grown under near-physiological solution conditions in vitro. In postmortem brains of an AD mouse model and human AD patients, we demonstrate that A11 antibody-positive oligomers but not Thioflavin S (ThioS)-positive fibrils colocalize with CV staining, confirming in vitro results. Therefore, our kinetic screen represents a robust approach for identifying new classes of small molecules as candidates for oligomer-selective dyes (OSDs). Such OSDs, in turn, provide promising starting points for the development of PET probes for pre-mortem imaging of oligomer deposits in humans.

Probing the interactions of the HIV-1 matrix protein-derived polybasic region with lipid bilayers: insights from AFM imaging and force spectroscopy.

The human immunodeficiency virus type 1 (HIV-1) matrix protein contains a highly basic region, MA-HBR, crucial for various stages of viral replication. To elucidate the interactions between the polybasic peptide MA-HBR and lipid bilayers, we employed liquid-based atomic force microscopy (AFM) imaging and force spectroscopy on lipid bilayers of differing compositions. In 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers, AFM imaging revealed the formation of annulus-shaped protrusions upon exposure to the polybasic peptide, accompanied by distinctive mechanical responses characterized by enhanced bilayer puncture forces. Importantly, our AFM-based force spectroscopy measurements unveiled that MA-HBR induces interleaflet decoupling within the cohesive bilayer organization. This is evidenced by a force discontinuity observed within the bilayer's elastic deformation regime. In POPC/cholesterol bilayers, MA-HBR caused similar yet smaller annular protrusions, demonstrating an intriguing interplay with cholesterol-rich membranes. In contrast, in bilayers containing anionic 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine (POPS) lipids, MA-HBR induced unique annular protrusions, granular nanoparticles, and nanotubules, showcasing its distinctive effects in anionic lipid-enriched environments. Notably, our force spectroscopy data revealed that anionic POPS lipids weakened interleaflet adhesion within the bilayer, resulting in interleaflet decoupling, which potentially contributes to the specific bilayer perturbations induced by MA-HBR. Collectively, our findings highlight the remarkable variations in how the polybasic peptide, MA-HBR, interacts with lipid bilayers of differing compositions, shedding light on its role in host membrane restructuring during HIV-1 infection.

Construction of nano-silica particle clusters and their effects on the shear thickening properties of liquids.

It is of great research significance to prepare a new shear thickening fluid (STF) with a simple process, remarkable thickening effect and excellent impact resistance from the properties of the particles. Inspired by the shear thickening mechanism, nano-silica particle clusters (SPC) with different morphological structures were prepared by the reaction of amino-modified silica with polyethylene glycol diglycidyl ether (PEGDGE), and the structure models of particle clusters were designed through theoretical analysis. The structure of SPC was affected by the degree of amination modification and the molecular weight of PEGDGE, which was analyzed by DLS and TEM. The shear thickening behavior of the fluid was evaluated by steady-state rheology and dynamic-state rheology analysis. The shear thickening behavior of the fluid composed of SPC also changed greatly with the influence of the degree of amination modification and the molecular weight of PEGDGE. In addition, compared with the STF contained original silica, the STF contained SPC could produce a faster and stronger shear thickening response. Therefore, silica particle clusters are not only a promising candidate for the preparation of high-performance shear thickening fluids, but can also be better applied to industrial and scientific fields such as impact protection and shock absorption.

Effects of optical and radar satellite observations within Google Earth Engine on soil organic carbon prediction models in Spain.

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and "ASCENDING" orbits outperformed those involving copolarization, a single time period and "DESCENDING" orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping.

[Vertical Pollution Characteristics and Source Analysis of Soil PAHs in Different Land Use Types].

In order to study the vertical pollution characteristics of polycyclic aromatic hydrocarbons (PAHs) in soils of different land use types in suburban areas of Nanjing, 15 types of controlled PAHs were studied in each section (0-100 cm) of soils from six different land use types, including a vegetable field, forestland, residential area, urban land, paddy field, and industrial area. The vertical distribution and composition characteristics, influencing factors, and sources of PAHs were analyzed. The results showed that:the total concentrations of Σ15PAHs in the six sampling site profiles were as follows:vegetable field (69.3-299.2 µg·kg-1), forestland (20.8-128.3 µg·kg-1), residential area (30.7-142.1 µg·kg-1), urban land (185.6-1728.7 µg·kg-1), paddy field (208.3-693.0 µg·kg-1), and industrial area (165.6-739.2 µg·kg-1). There was no pollution in the residential area or forestland and a light pollution level in the vegetable field, medium pollution level in the paddy field and industrial area, and more serious pollution in the urban land. Soil PAHs were mainly distributed in the surface or subsurface layer, except in the residential area and urban land; however, they were still detected in the deep layers, and high-molecular-weight PAHs were dominant in most depths and sampling sites. The vertical distribution and migration of PAHs in soils were affected by molecular characteristics and component concentrations of PAHs, soil physical and chemical properties, and land use types. PMF source analysis indicated that coke sources, traffic sources, and coal combustion sources from human activities were the main sources of PAHs in this study region.

[Pollution Characteristics and Risk Assessment of Polycyclic Aromatic Hydrocarbons in Agricultural Soils of Different Land Use Types in Nanjing Suburbs].

In order to clarify the pollution characteristics of PAHs in suburban agricultural soils, the content of 16 types of PAHs was measured in agricultural soils with different land use types (paddy fields, vegetable fields, and forest land) in the suburbs of Nanjing. The results showed that acenaphthene (Acy) was not detected in any soil samples. The concentration of 15 PAHs in agricultural soil in suburban Nanjing ranged from 24.49 to 925.54 µg·kg-1, with an average concentration of 259.88 µg·kg-1. In different land use types, the order of PAHs concentration in soil from high to low was:forest land>paddy fields>vegetable fields, and the high-ring PAHs content was dominant in general. The effects of different soil physicochemical properties on PAHs showed that there was a certain correlation between soil organic carbon (TOC) and clay (clay) content and PAHs, whereas pH and total nitrogen (TN) had no significant correlation with PAHs. The toxic equivalence method and CSI index method were used for ecological risk assessment, which showed that the ecological risk of PAHs in agricultural soils in suburban Nanjing was relatively small; however, the ecological risk of PAHs in forest land should be given some attention, and supervision should be strengthened. Health risk assessment using incremental lifetime cancer risk (ILCR) showed that the threat to the health of children was slightly greater than that of adults, and the CR of forest land was significantly higher than that of vegetable and paddy fields, though still within an acceptable range. Uncertain health assessments were performed in adults, showing that risk analyses of deterministic health risks underestimated the health risks of PAHs. The results of sensitivity analysis showed that the input parameter that had the greatest impact on the total variance of the total carcinogenic risk CR was the exposure frequency EF (50.7%), followed by the pollutant concentration CS (43.3%).

Elucidating the Influence of Lipid Composition on Bilayer Perturbations Induced by the N-terminal Region of the Huntingtin Protein.

Understanding the membrane interactions of the N-terminal 17 residues of the huntingtin protein (HttN) is essential for unraveling its role in cellular processes and its impact on huntingtin misfolding. In this study, we used atomic force microscopy (AFM) to examine the effects of lipid specificity in mediating bilayer perturbations induced by HttN. Across various lipid environments, the peptide consistently induced bilayer disruptions in the form of holes. Notably, our results unveiled that cholesterol enhanced bilayer perturbation induced by HttN, while phosphatidylethanolamine (PE) lipids suppressed hole formation. Furthermore, anionic phosphatidylglycerol (PG) and cardiolipin lipids, along with cholesterol at high concentrations, promoted the formation of double-bilayer patches. This unique structure suggests that the synergy among HttN, anionic lipids, and cholesterol can enhance bilayer fusion, potentially by facilitating lipid intermixing between adjacent bilayers. Additionally, our AFM-based force spectroscopy revealed that HttN enhanced the mechanical stability of lipid bilayers, as evidenced by an elevated bilayer puncture force. These findings illuminate the complex interplay between HttN and lipid membranes and provide useful insights into the role of lipid composition in modulating membrane interactions with the huntingtin protein.

Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images-Case Study of the Small Hilly Areas in Jurong City.

With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries.

Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging.

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models-random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)-were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.

Design and experimental study of a fiber Bragg grating strain sensor with enhanced sensitivity.

A novel high-sensitivity fiber Bragg grating (FBG) strain sensor is reported, to the best of our knowledge. The sensitivity of the sensor is improved by fixing the FBG on an elastic substrate with a sensitization function. The sensitization principle of the designed sensor is introduced, and the mathematical model of the sensor is established. In the static and dynamic experiments of the sensor, the effect of adhesive between the sensor and the measured structure on the sensitivity of the FBG strain sensor is experimentally investigated. The experimental results show that the adhesive with high shear strength is beneficial to the realization of a high-sensitivity sensor. The sensor fixed with planting bar glue can achieve a sensitivity of 9.42 pm/µÎµ, a repeatability error of 4.79%, and a hysteresis error of 3.36%, which is consistent with theoretical and simulation results. The designed high-sensitivity strain sensor has a simple structure, small size, and convenient installation, so it has a good application prospect in micro-strain monitoring.

[Predicting soil property in hilly regions by using landscape and multiscale micro-landform features]. / 基于景观及微地形特征的丘陵区土壤属性预测.

To assess the high-resolution digital soil mapping method for small watersheds in hilly areas, we explored the role of landscape classification and multiscale micro-landform features in predicting soil pH, soil clay content (SCC), and cation exchange capacity (CEC). Geomorphons (GM) terrain classification method was used to create landform units. The traditional digital elevation model (DEM) derivatives and remote sensing variables were employed for different combinations with landscape and micro-landform classification variables, with further compa-rison and analysis being conducted. In addition, three machine learning techniques, including support vector machine (SVM), partial least squares regression (PLSR), and random forest (RF), were used to build prediction models. The best method was then selected, and then combined with regression kriging by modeling spatial structure of the model residuals. The results showed that the application of landscape and multiscale micro-landform classification variables effectively improved the prediction accuracy of pH, SCC, and CEC by 18.8%, 8.2% and 8.7%, respectively. The map of landscape classification that contained vegetation coverage information had greater model contribution than land use data. The GM classification map with 5 m resolution was more suitable for high-precision DSM than those with lower resolution. The composite model of RF performed the best in predicting SCC, while the pH and CEC were not suitable for adding the residual regression kriging on the basis of RF model. Finally, the combination of landscape and multiscale micro-landform classification variables, DEM derivatives and remote sensing variables had the highest prediction accuracy for all the three soil properties. This result indicated that multivariable contained more effective soil information than single data source for rolling areas. The landscape variables composed of GM and surface classified data explained about 40% of the spatial variation of tested soil attributes in hilly area. Therefore, multi-resolution GM and landscape classified variables could be included into the construction of prediction model in research of soil mapping.

The N-terminal helices of amphiphysin and endophilin have different capabilities of membrane remodeling.

Amphiphysin and endophilin are two members of the N-BAR protein family. We have reported membrane interactions of the helix 0 of endophilin (H0-Endo). Here we investigate membrane modulations caused by the helix 0 of amphiphysin (H0-Amph). Electron paramagnetic resonance (EPR) spectroscopy was used to explore membrane properties. H0-Amph was found to reduce lipid mobility, make the membrane interior more polar, and decrease lipid chain orientational order. The EPR data also showed that for anionic membranes, H0-Endo acted as a more potent modulator. For instance, at peptide-to-lipid (P/L) ratio of 1/20, the peak-to-peak splitting was increased by 0.27 G and 1.89 G by H0-Amph and H0-Endo, respectively. Similarly, H0-Endo caused a larger change in the bilayer polarity than H0-Amph (30% versus 12% at P/L = 1/20). At P/L = 1/50, the chain orientational order was decreased by 26% and 66% by H0-Amph and H0-Endo, respectively. The different capabilities were explained by considering hydrophobicity score distributions. We employed atomic force microscopy to investigate membrane structural changes. Both peptides caused the formation of micron-sized holes. Interestingly, only H0-Amph induced membrane fusion as evidenced by the formation of high-rise regions. Lastly, experiments of giant unilamellar vesicles showed that H0-Amph and H0-Endo generated thin tubules and miniscule vesicles, respectively. Together, our studies showed that both helices are effective in altering membrane properties; the observed changes might be important for membrane curvature induction. Importantly, comparisons between the two peptides revealed that the degree of membrane remodeling is dependent on the sequence of the N-terminal helix of the N-BAR protein family.

Significant loss of soil inorganic carbon at the continental scale.

Widespread soil acidification due to atmospheric acid deposition and agricultural fertilization may greatly accelerate soil carbonate dissolution and CO2 release. However, to date, few studies have addressed these processes. Here, we use meta-analysis and nationwide-survey datasets to investigate changes in soil inorganic carbon (SIC) stocks in China. We observe an overall decrease in SIC stocks in topsoil (0-30 cm) (11.33 g C m-2 yr-1) from the 1980s to the 2010s. Total SIC stocks have decreased by ∼8.99 ± 2.24% (1.37 ± 0.37 Pg C). The average SIC losses across China (0.046 Pg C yr-1) and in cropland (0.016 Pg C yr-1) account for ∼17.6%-24.0% of the terrestrial C sink and 57.1% of the soil organic carbon sink in cropland, respectively. Nitrogen deposition and climate change have profound influences on SIC cycling. We estimate that ∼19.12%-19.47% of SIC stocks will be further lost by 2100. The consumption of SIC may offset a large portion of global efforts aimed at ecosystem carbon sequestration, which emphasizes the importance of achieving a better understanding of the indirect coupling mechanisms of nitrogen and carbon cycling and of effective countermeasures to minimize SIC loss.

[Pollution Characteristics and Risk Assessment of Polycyclic Aromatic Hydrocarbons in a Suburban Farmland Soil].

In order to assess the pollution of polycyclic aromatic hydrocarbons(PAHs) in a suburban farmland soil, 29 sampling sites were collected around Nanjing according to the grid method, and the contents of 15 different PAHs were determined. Acenaphthene(Ace) was not detected in any of the samples. The total content of PAHs in farmland soil ranged from 24.49 to 750.04 µg·kg-1, with an average of 226.64 µg·kg-1. The spatial distribution of high-ring PAHs, the main PAHs in the farmland soil, was similar to that of total PAHs. There was no significant correlation between PAHs and soil organic matter(SOM), pH, cation exchange capacity(CEC), and total nitrogen(TN), whereas bulk density and low ring PAHs were significantly correlated. The results of source apportionment show that the main source of PAHs in the farmland soil is a mixture of combustion and petroleum. The contamination severity index(CSI) index shows that the PAHs does not pose an ecological risk. The results of the health risk assessment show that there is no potential carcinogenic risk to children or adults, and the main sequence of exposure is skin contact>ingestion>inhalation.

Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images.

Soil organic carbon (SOC) and soil carbon-to-nitrogen ratio (C:N) are the main indicators of soil quality and health and play an important role in maintaining soil quality. Together with Landsat, the improved spatial and temporal resolution Sentinel sensors provide the potential to investigate soil information on various scales. We analyzed and compared the potential of satellite sensors (Landsat-8, Sentinel-2 and Sentinel-3) with various spatial and temporal resolutions to predict SOC content and C:N ratio in Switzerland. Modeling was carried out at four spatial resolutions (800 m, 400 m, 100 m and 20 m) using three machine learning techniques: support vector machine (SVM), boosted regression tree (BRT) and random forest (RF). Soil prediction models were generated in these three machine learners in which 150 soil samples and different combinations of environmental data (topography, climate and satellite imagery) were used as inputs. The prediction results were evaluated by cross-validation. Our results revealed that the model type, modeling resolution and sensor selection greatly influenced outputs. By comparing satellite-based SOC models, the models built by Landsat-8 and Sentinel-2 performed the best and the worst, respectively. C:N ratio prediction models based on Landsat-8 and Sentinel-2 showed better results than Sentinel-3. However, the prediction models built by Sentinel-3 had competitive or better accuracy at coarse resolutions. The BRT models constructed by all available predictors at a resolution of 100 m obtained the best prediction accuracy of SOC content and C:N ratio; their relative improvements (in terms of R2) compared to models without remote sensing data input were 29.1% and 58.4%, respectively. The results of variable importance revealed that remote sensing variables were the best predictors for our soil prediction models. The predicted maps indicated that the higher SOC content was mainly distributed in the Alps, while the C:N ratio shared a similar distribution pattern with land use and had higher values in forest areas. This study provides useful indicators for a more effective modeling of soil properties on various scales based on satellite imagery.

Protofibril-Fibril Interactions Inhibit Amyloid Fibril Assembly by Obstructing Secondary Nucleation.

Amyloid-ß peptides (Aß) assemble into both rigid amyloid fibrils and metastable oligomers termed AßO or protofibrils. In Alzheimer's disease, Aß fibrils constitute the core of senile plaques, but Aß protofibrils may represent the main toxic species. Aß protofibrils accumulate at the exterior of senile plaques, yet the protofibril-fibril interplay is not well understood. Applying chemical kinetics and atomic force microscopy to the assembly of Aß and lysozyme, protofibrils are observed to bind to the lateral surfaces of amyloid fibrils. When utilizing Aß variants with different critical oligomer concentrations, the interaction inhibits the autocatalytic proliferation of amyloid fibrils by secondary nucleation on the fibril surface. Thus, metastable oligomers antagonize their replacement by amyloid fibrils both by competing for monomers and blocking secondary nucleation sites. The protofibril-fibril interaction governs their temporal evolution and potential to exert specific toxic activities.

Chromosome-level draft genome of a diploid plum (Prunus salicina).

BACKGROUND: Plums are one of the most economically important Rosaceae fruit crops and comprise dozens of species distributed across the world. Until now, only limited genomic information has been available for the genetic studies and breeding programs of plums. Prunus salicina, an important diploid plum species, plays a predominant role in modern commercial plum production. Here we selected P. salicina for whole-genome sequencing and present a chromosome-level genome assembly through the combination of Pacific Biosciences sequencing, Illumina sequencing, and Hi-C technology. FINDINGS: The assembly had a total size of 284.2 Mb, with contig N50 of 1.78 Mb and scaffold N50 of 32.32 Mb. A total of 96.56% of the assembled sequences were anchored onto 8 pseudochromosomes, and 24,448 protein-coding genes were identified. Phylogenetic analysis showed that P. salicina had a close relationship with Prunus mume and Prunus armeniaca, with P. salicina diverging from their common ancestor ∼9.05 million years ago. During P. salicina evolution 146 gene families were expanded, and some cell wall-related GO terms were significantly enriched. It was noteworthy that members of the DUF579 family, a new class involved in xylan biosynthesis, were significantly expanded in P. salicina, which provided new insight into the xylan metabolism in plums. CONCLUSIONS: We constructed the first high-quality chromosome-level plum genome using Pacific Biosciences, Illumina, and Hi-C technologies. This work provides a valuable resource for facilitating plum breeding programs and studying the genetic diversity mechanisms of plums and Prunus species.

Genome-wide identification and expression analysis of the MYB transcription factor in Japanese plum (Prunus salicina).

MYB proteins constitute one of the largest transcription factor families in plants, members of which are involved in various plant physiological and biochemical processes. Japanese plum (Prunus salicina) is one of the important stone fruit crops worldwide. To date, no comprehensive study of the MYB family in Japanese plum has been reported. In this study, we performed genome-wide analysis of MYB genes in Japanese plum including the phylogeny, gene structures, protein motifs, chromosomal locations, collinearity and expression patterns analysis. A total of 96 Japanese plum R2R3-MYB (PsMYB) genes were characterized and distributed on 8 chromosomes at various densities. Collinearity analysis indicated that the segmental duplication events played a crucial role in the expansion of PsMYB genes, and the interspecies synteny analysis revealed the orthologous gene pairs between Japanese plum and other four selected Rosaceae species. The 96 PsMYB genes could be classified into 27 subgroups based on phylogenetic topology, as supported by the conserved gene structures and motif compositions. Further comparative phylogenetic analysis revealed the functional divergence of MYB gene family during evolution, and three subgroups which included only Rasaceae MYB genes were identified. Expression analysis revealed the distinct expression profiles of the PsMYB genes, and further functional predictions found some of them might be associated with the plum fruit quality traits. Our researches provide a global insight into the organization, phylogeny, evolution and expression patterns of the PsMYB genes, and contribute to the greater understanding of their functional roles in Japanese plum.

High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms.

Soil organic carbon (SOC) and soil total nitrogen (STN) are important indicators of soil health and play a key role in the global carbon and nitrogen cycles. High-resolution radar Sentinel-1 and multispectral Sentinel-2 images have the potential to investigate soil spatial distribution information over a large area, although Sentinel-1 and Sentinel-2 data have rarely been combined to map either SOC or STN content. In this study, we applied machine learning techniques to map both SOC and STN content in the southern part of Central Europe using digital elevation model (DEM) derivatives, multi-temporal Sentinel-1 and Sentinel-2 data, and evaluated the potential of different remote sensing sensors (Sentinel-1 and Sentinel-2) to predict SOC and STN content. Four machine-learners including random forest (RF), boosted regression trees (BRT), support vector machine (SVM) and Bagged CART were used to construct predictive models of SOC and STN contents based on 179 soil samples and different combinations of environmental covariates. The performance of these models was evaluated based on a 10-fold cross-validation method by three statistical indicators. Overall, the BRT model performed better than RF, SVM and Bagged CART, and these models yielded similar spatial distribution patterns of SOC and STN. Our results showed that multi-source sensor methods provided more accurate predictions of SOC and STN contents than individual sensors. The application of radar Sentinel-1 and multispectral Sentinel-2 images proved useful for predicting SOC and STN. A combination of Sentinel-1/2-derived predictors and DEM derivatives yielded the highest prediction accuracy. The prediction accuracy changed with and without the Sentinel-1/2-derived predictors, with the R2 for estimating both SOC and STN content using the BRT model increasing by 12.8% and 18.8%, respectively. Topographic variables were the main explanatory variables for SOC and STN predictions, where elevation was assigned as the variable with the most importance by the models. The results of this study illustrate the potential of free high-resolution radar Sentinel-1 and multispectral Sentinel-2 data as input when developing SOC and STN prediction models.

The helix 0 of endophilin modifies membrane material properties and induces local curvature.

The amphipathic helix 0 of endophilin (i.e., H0-Endo) is important to membrane binding, but its function of curvature generation remains controversial. We used electron paramagnetic resonance (EPR) spectroscopy to study effects of H0-Endo on membrane material properties. We found that H0-Endo reduced lipid chain mobility and increased bilayer polarity, i.e., making the bilayer interior more polar. Lipid-dependent examination revealed that anionic lipids augmented the effect of H0-Endo, while cholesterol had a minimal impact. Our EPR spectroscopy of magnetically aligned bicelles showed that as the peptide-to-lipid ratio increased, the lipid chain orientational order decreased gradually, followed by a sudden loss. We discuss an interfacial-bound model of the amphipathic H0-Endo to account for all EPR data. We used atomic force microscopy and fluorescence microscopy to explore membrane morphological changes. We found that H0-Endo caused the formation of micron-sized holes in mica-supported planar bilayers. Hole formation is likely caused by two competing forces - the adhesion force exerted by the substrate represses bilayer budging, whereas the line tension originating from peptide clustering has a tendency of destabilizing bilayer organization. In the absence of substrate influences, membrane curvature induction was manifested by generating small vesicles surrounding giant unilamellar vesicles. Our results of membrane perforation and vesiculation suggest that the functionality of H0-Endo is more than just coordinating membrane binding of endophilin.