The characteristics and environmental significance of BVOCs released by aquatic macrophytes.

Biogenic volatile organic compounds (BVOCs) emitted by plants serve crucial biological functions and potentially impact atmospheric environment and global carbon cycling. Despite their significance, BVOC emissions from aquatic macrophytes have been relatively understudied. In this study, for the first time we identified there were 68 major BVOCs released from 34 common aquatic macrophytes, and these compounds referred to alcohols, aldehydes, alkanes, alkenes, arenes, ethers, furans, ketones, phenol. For type of BVOC emissions from different life form and phylogenetic group of aquatic macrophytes, 34 of the 68 BVOCs from emergent and submerged macrophytes are classified into alkene and alcohol compounds, over 50% BVOCs from dicotyledon and monocotyledon belong to alcohol and arene compounds. Charophyte and pteridophyte emitted significantly fewer BVOCs than dicotyledon and monocotyledon, and each of them only released 12 BVOCs. These BVOCs may be of great importance for the growth and development of macrophytes, because many BVOCs, such as azulene, (E)-ß-farnesene, and dimethyl sulfide are proved to play vital roles in plant growth, defense, and information transmission. Our results confirmed that both life form and phylogenetic group of aquatic macrophytes had significantly affected the BVOC emissions form macrophytes, and suggested that the intricate interplay of internal and external factors that shape BVOC emissions from aquatic macrophytes. Thus, further studies are urgently needed to investigate the influence factors and ecological function of BVOCs released by macrophytes within aquatic ecosystem.

Global prediction of gross primary productivity under future climate change.

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid-high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.

Metasurface-based triple-band beam splitter with large spatial separation at visible wavelengths.

The dual-function of a wavelength beam splitter and a power beam splitter is desired in both classical optics and quantum optics. We propose a triple-band large-spatial-separation beam splitter at visible wavelengths using a phase-gradient metasurface in both the x- and y-directions. Under x-polarized normal incidence, the blue light is split in the y-direction into two equal-intensity beams owing to the resonance inside a single meta-atom, the green light is split in the x-direction into another two equal-intensity beams owing to the size variation between adjacent meta-atoms, while the red light passes directly without splitting. The size of the meta-atoms was optimized based on their phase response and transmittance. The simulated working efficiencies under normal incidence are 68.1%, 85.0%, and 81.9% at the wavelengths of 420 nm, 530 nm, and 730 nm, respectively. The sensitivities of the oblique incidence and polarization angle are also discussed.

An injectable multifunctional hydrogel for eradication of bacterial biofilms and wound healing.

Wound treatment is largely influenced by pre-existing hypoxic microenvironments and biofilms, which can severely diminish the efficacy of phototherapy, suggesting the importance of multifunctional nanoplatforms for synergistic treatment of wound infections. Here, we developed a multifunctional injectable hydrogel (PSPG hydrogel) by loading photothermal sensitive sodium nitroprusside (SNP) into Pt-modified porphyrin metal organic framework (PCN) and in situ modification of gold particles to form a near-infrared (NIR) light-triggered all-in-one phototherapeutic nanoplatform. The Pt-modified nanoplatform exhibits a remarkable catalase-like behavior and promotes the continuous decomposition of endogenous H2O2 into O2, thereby enhancing the photodynamic therapy (PDT) effect under hypoxia. Under dual NIR irradiation, PSPG hydrogel can not only produce hyperthermia (η=89.21%) but also generate reactive oxygen species and trigger NO release, contributing jointly to removal of biofilms and disruption of the cell membranes of methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli (E. coli). In vivo experiments demonstrated a 99.9% reduction in bacterial burden on wounds. Additionally, PSPG hydrogel can accelerate MRSA-infected and Pseudomonas aeruginosa-infected (P. aeruginosa-infected) wound healing by promoting angiogenesis, collagen deposition, and suppressing inflammatory responses. Furthermore, in vitro and in vivo experiments revealed that PSPG hydrogel has good cytocompatibility. Overall, we proposed an antimicrobial strategy to eliminate bacteria through the synergistic effects of gas-photodynamic-photothermal killing, alleviating hypoxia in the bacterial infection microenvironment, and inhibiting biofilms, offering a new way against antimicrobial resistance and biofilm-associated infections. STATEMENT OF SIGNIFICANCE: The NIR light-triggered multifunctional injectable hydrogel nanoplatform (PSPG hydrogel) based on Pt-decorated gold nanoparticles with sodium nitroprusside (SNP)-loading porphyrin metal organic framework (PCN) as inner templates can efficiently perform photothermal conversion (η=89.21%) to trigger NO release from SNP, while continuously regulating the hypoxic microenvironment at the bacterial infection site through Pt-induced self-oxygenation, achieving efficient sterilization and removal of biofilm by synergistic PDT and PTT phototherapy. In vivo and in vitro experiments demonstrated that the PSPG hydrogel has significant anti-biofilm, antibacterial, and inflammatory regulatory functions. This study proposed an antimicrobial strategy to eliminate bacteria through the synergistic effects of gas-photodynamic-photothermal killing, alleviating hypoxia in the bacterial infection microenvironment, and inhibiting biofilms.

Mapping soil available copper content in the mine tailings pond with combined simulated annealing deep neural network and UAV hyperspectral images.

Improper discharge of slag from mining will pollute the surrounding soil, thereby affecting the ecology and becoming an important global problem. The available copper (ACu) content in polluted soil is an important factor affecting plant growth and development. When investigating a large area of soil with ACu, manual sampling by points and inspection are mainly used, due to the heterogeneity of soil, the efficiency and accuracy are lower. The Unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor as a remote sensing technology is widely used in soil indicator monitoring because of its rapid and convenience. Meanwhile, using the relationship between soil organic matter and available copper has the potential to predict available copper. In this study, we selected the study area with tailings area in the Jianghan Plain of China and used a UAV equipped with a hyperspectral sensor to predict ACu and soil organic matter (SOM) in the soil with two datasets. Firstly, 74 soil samples were collected in the study area, and the ACu and SOM of the soil samples were determined. Second, a hyperspectral image of the study area is obtained using a UAV equipped with a hyperspectral sensor. Thirdly, we combine hyperspectral data with competitive adaptive reweighted sampling (CARS) to obtain feature bands and utilize simulated annealing deep neural network (SA-DNN) to generate estimation models. Finally, maps of the distribution of ACu and SOM in the area were generated using the model. In two datasets, the model of ACu with R2 values both are 0.89, and R2 on the model of SOM is 0.89 and 0.88. The results show that the combination of UAV hyperspectral imagery with the SA-DNN model has good performance in the prediction of organic matter and available copper, which is helpful for soil environmental monitoring.

Melt-spun bio-based PLA-co-PET copolyester fibers with tunable properties: Synergistic effects of chemical structure and drawing process.

The fabrication of bio-based copolyester fiber with adjustable crystallization, orientation structure and mechanical property still remains a great challenge. In this study, a series of copolyester fibers based on terephthalic acid (PTA), ethylene glycol (EG) and l-Lactide (L-LA) were prepared via melt copolymerization and spinning. The resultant PLA-co-PET (PETLA) fibers exhibited tunable structure and property due to the synergistic effects of chemical structure and drawing process. The chemical structure of PETLA was confirmed by NMR, FTIR and XRD, which suggested that the random degree of copolymer increased with LA content and the viscosity decreased with the increase of LA content. The crystallization behavior, melting characteristic, thermal stability and rheological property were investigated by DSC, TGA and rheometer, the results indicated that all the PETLA exhibited the crystallization capacity, melting temperature and thermal stability were slightly affected by LA segment. The synergistic effects of LA segment and spinning process on PETLA structure and property were analyzed by WAXD and SAXS. The breaking strength of PETLA fibers dropped from 5.3 cN/dtex of PET to 2.8 cN/dtex of PET85LA15, which still met the requirements of most textile applications. Therefore, our work presented a feasible approach to prepare bio-based polyester fibers with tunable property.

Microplastics and co-pollutant with ciprofloxacin affect interactions between free-floating macrophytes.

Microplastic and antibiotic contamination are considered an increasing environmental problem in aquatic systems, while little is known about the impact of microplastics and co-pollutant with antibiotics on freshwater vascular plants, particularly the effects of interactions between macrophytes. Here, we performed a mesocosm experiment to evaluate the impact of polyethylene-microplastics and their co-pollutants with ciprofloxacin on the growth and physiological characteristics of Spirodela polyrhiza and Lemna minor and the interactions between these two macrophytes. Our results showed that microplastics alone cannot significantly influence fresh weight and specific leaf area of the two test free-floating macrophytes, but the effects on photosynthetic pigments, malondialdehyde, catalase and soluble sugar contents were species-specific. Ciprofloxacin can significant adverse effects on the growth and physiological traits of the two test macrophytes and microplastic mitigated the toxicity of ciprofloxacin on the two free-floating plants to a certain extent. In addition, our studies showed that microplastics and co-pollutants can influence relative yield and competitiveness of S. polyrhiza and L. minor by directly or indirectly influencing their physiology and growth. Therefore our findings suggest that species-specific sensibility to microplastic and its co-pollutant among free-floating macrophytes may influence macrophyte population dynamics and thereby community structure and ecosystem functioning. And microplastics altered other contaminant behaviours and toxicity, and may directly or indirectly influence macrophytes interactions and community structure. The present study is the first experimental study exploring the effects of microplastics alone and with their co-pollutants on interactions between free-floating macrophytes, which can provide basic theoretical guidance for improving the stability of freshwater ecosystems.

Digital mapping of soil pH and carbonates at the European scale using environmental variables and machine learning.

Soil pH and carbonates (CaCO3) are important indicators of soil chemistry and fertility, and the prediction of their spatial distribution is critical for the agronomic and environmental management. Digital soil mapping (DSM) techniques are widely accepted for the geospatial analysis of the soil properties. They are rapid and cost-efficient approaches that can provide quantitative prediction. However, the digital mapping of soil pH and CaCO3 are not well studied, especially at a continental scale. In this research, we mapped the soil pH and CaCO3 at the European scale using multisource environmental variables and machine learning approaches. Moderate Resolution Imaging Spectroradiometer (MODIS) products, terrain attributes, and climatic variables were considered. Meanwhile, nine machine learning algorithms, namely, three linear and six nonlinear models, were used for the spatial prediction of soil pH and CaCO3. The land use and cover area frame statistical survey (LUCAS) 2015 topsoil dataset provided by the European Soil Data Centre was utilised. The performances of different models were compared and analysed in terms of coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD). Specifically, nonlinear machine learning models outperformed the linear ones, and extremely randomized trees (ERT) gave the most satisfactory result for soil pH (R2 = 0.70, RMSE = 0.75, and RPD = 1.84) and CaCO3 (R2 = 0.53, RMSE = 93.49 g/kg, and RPD = 1.46). The results revealed that MODIS products and climatic variables were important in predicting soil pH and CaCO3. Moreover, spatial distribution of soil pH and CaCO3 in Europe were mapped at 250 m resolution, and the areas with high CaCO3 content always showed high soil pH value.

Mechanical, Thermal Stability, and Flame Retarding Properties of Phosphorus-Modified PET Blended with DOPO-POSS.

In this study, an antidroplet flame retardant system based on FRPET (phosphorus-containing copolyester) is constructed with DOPO-POSS (polyhedral oligomeric silsesquioxane containing DOPO) as an additive flame retardant. It is demonstrated that DOPO-POSS has good dispersibility at a lower amount. When the amount of DOPO-POSS is 9 wt %, the residual char of DOPO-POSS/FRPET at 700 °C increases to 23.56 from 18.16% of FRPET, and the maximum thermal weight loss rate also reduces. What is more is that the limiting oxygen index increases to 33 from 26% of FRPET. The flame burning time is shortened to 4.95 from 20.8 s, the phenomenon of self-extinguishing of the fire occurs, and the vertical combustion level is increased from V-2 to V-0. Compared with FRPET, the peak of the heat release rate decreases by 66.0%, the total heat release decreases by 32.4%, the flame retardancy index (FRI) reaches an excellent value, and the condensed-phase products significantly improve. The Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy-energy dispersive X-ray spectrometry (SEM-EDX), thermogravimetric-FTIR (TG-FTIR), and pyrolysis-gas chromatograph/mass spectrometry (Py-GC/MS) results indicate that DOPO-POSS contributes to the formation of char layers and decomposes to generate free radicals with a quenching effect. In a word, DOPO-POSS is an effective radical trapper and charring agent for PET and exerts a flame retardancy effect in gaseous and condensed phases simultaneously.

Van-mediated self-aggregating photothermal agents combined with multifunctional magnetic nickel oxide nanoparticles for precise elimination of bacterial infections.

Building a novel and efficient photothermal antibacterial nanoplatform is a promising strategy for precise bacterial elimination. Herein, a nanocomposite NiO NPs@AuNPs@Van (NAV) for selective MRSA removal was constructed by electrostatic self-assembly of highly photothermal magnetic NiO NPs and vancomycin (Van)-modified gold nanoparticles (AuNPs). In the presence of MRSA and under NIR irradiation, Van-mediated AuNPs can self-aggregate on MRSA surface, generating photothermal effect in situ and killing 99.6% MRSA in conjunction with magnetic NiO NPs. Additionally, the photothermal efficiency can be improved by magnetic enrichment due to the excellent magnetism of NAV, thereby enhancing the bactericidal effect at a lower experimental dose. In vitro antibacterial experiments and full-thickness skin wound healing test demonstrated that this combination therapy could effectively accelerate wound healing in MRSA-infected mice, increase collagen coverage, reduce IL-6 and TNF-α content, and upregulate VEGF expression. Biological safety experiments confirmed that NAV has good biocompatibility in vivo and in vitro. Overall, this work reveals a new type of nanocomposite with enhanced photothermal antibacterial activity as a potential nano-antibacterial agent for treating bacteria-infected wounds.

Ni Nanocrystals Supported on Graphene Oxide: Antibacterial Agents for Synergistic Treatment of Bacterial Infections.

The effects of antibiotics on bacterial infections are gradually weakened, leading to the wide development of nanoparticle-based antibacterial agents with unique physical and chemical properties and antibacterial mechanisms different from antibiotics. In this study, we fabricated the uniform and stable graphene oxide (GO)/Ni colloidal nanocrystal cluster (NCNC) nanocomposite by electrostatic self-assembly and investigated its synergistic antibacterial activity against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) in vitro. The GO/NCNC nanocomposite was shown to possess higher inhibition efficiency than a pure NCNC or GO suspension, with 99.5 and 100% inhibition against S. aureus and E. coli at a 125 µg/mL concentration, respectively. Antibacterial mechanism analysis revealed that (i) NCNCs decorated on GO can further enhance the antibacterial properties of GO by binding and capturing bacteria, (ii) the leaching of Ni2+ was detected during the interaction of GO/NCNCs and bacteria, resulting in a decrease in the number of bacteria, and (iii) the GO/NCNC nanocomposite can synergistically destroy the bacterial membrane through physical action and induce the reactive oxygen species generation, so as to further damage the cell membrane and affect ATPase, leakage of intercellular contents, and ultimately bacterial growth inhibition. Meanwhile, cell culture experiments demonstrated no adverse effect of GO/NCNCs on cell growth. These preliminary results indicate the high antibacterial efficiency of the GO/NCNC nanocomposite, suggesting the possibility to develop it into an effective antibacterial agent in the future against bacterial infections.

NIR-activated multi-hit therapeutic Ag2S quantum dot-based hydrogel for healing of bacteria-infected wounds.

Hydrogel dressings are highly biocompatible and can maintain a moist wound environment, suggesting constructing an efficient multi-modal antibacterial hydrogel platform is a promising strategy for treating bacterial wound infections. In this work, a composite Ag2S quantum dot/mSiO2 NPs hydrogel (NP hydrogel) with antibacterial ability was constructed by incorporating Ag2S quantum dots (QDs) modified by mesoporous silica (mSiO2) into the network structure of 3-(trimethoxylmethosilyl) propyl methacrylate based on free radical polymerization. The NP hydrogel showed outstanding controllable photothermal and photodynamic characteristics under 808 nm near infrared (NIR) light irradiation, with a photothermal conversion efficiency of 57.3%. Additionally, the release of Ag+ could be controlled by the inherent volume change of the NP hydrogel made of N-isopropylacrylamide (NIPAAm) and acrylamide (AAm) during NIR laser exposure, with the embedded Ag2S QDs working as a reservoir to release Ag+ continuously from the hydrogel matrix to achieve bactericidal activity. The synergetic effects between hyperthermia, radical oxygen species, and Ag+ released under NIR radiation endowed the NP hydrogel with prominent antibacterial properties against Escherichia coli (E. coli) and methicillin-resistant Staphylococcus aureus (MRSA), with an inhibition rate of 99.7% and 99.8%, respectively. In vivo wound healing experiments indicated that the NP hydrogel could enhance bacterial clearance, increase collagen coverage area and up-regulate VEGF expression, exhibiting high biocompatibility. Overall, this study proposed an efficient and highly biocompatible multi-modal therapeutic nanohydrogel, opening up a new way for developing broad-spectrum antibacterial wound dressings to treat bacterial wound infections. STATEMENT OF SIGNIFICANCE: Bacterial wound infection is still one of the most difficult medical problems. In this work, a stimulating NIR-responsive hydrogel encapsulating functional Ag2S QDs was prepared, which showed high photothermal conversion efficiency (57.3%) and outstanding antibacterial ability under 808 nm NIR laser, killing 99.7% and 99.8% of E. coli and MRSA in 4 min, respectively. During NIR light irradiation, the release rate of Ag+ could be regulated by the intrinsic volume transition of the hydrogel, leading to remarkable antibacterial properties in vitro and in vivo under the combined action of hyperthermia, radical oxygen species and Ag+ released. This study proposed a novel multi-modal therapeutic nanohydrogel, opening up a new way for developing broad-spectrum antibacterial wound dressings to treat bacterial wound infections.

Functionalization of PET with Phosphazene Grafted Graphene Oxide for Synthesis, Flammability, and Mechanism.

Significant improvement in the fire resistance of polyethylene terephthalate (PET) while ensuring its mechanical properties is a tremendous challenge. A novel flame retardant (GO-HCCP, graphene oxide-hexachlorocyclotriphosphazene) was synthesized by nucleophilic substitution of the graphene oxide (GO) and hexachlorocyclotriphosphazene (HCCP) and then applied in PET by an in situ polymerization technique. The scanning electron microscope (SEM) showed a better dispersion of GO-HCCP than GO in the PET matrix. The char yield at 700 °C increased by 32.5% with the addition of GO-HCCP. Moreover, the peak heat release rate (pHRR), peak smoke produce rate (pSPR)and carbon monoxide production (COP)values significantly decreased by 26.0%, 16.7% and 37.5%, respectively, which indicates the outstanding fire and smoke suppression of GO-HCCP. In addition, the composites exhibited higher elastic modulus and tensile strength without compromising the toughness of PET matrix. These significantly reduced fire hazards properties are mainly attributed to the catalytic carbonation of HCCP and the barrier effect of GO. Thus, PET composites with good flame-retardant and mechanical properties were prepared, which provides a new strategy for further flame retardant PET preparation.

Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value.

Chlorophyll content is an important indicator of plant photosynthesis, which directly affects the growth and yield of crops. Using hyperspectral imaging technology to quickly and non-destructively estimate the soil plant analysis development (SPAD) value of pepper leaf and its distribution inversion is of great significance for agricultural monitoring and precise fertilization during pepper growth. In this study, 150 samples of pepper leaves with different leaf positions were selected, and the hyperspectral image data and SPAD value were collected for the sampled leaves. The correlation coefficient, stability competitive adaptive reweighted sampling (sCARS), and iteratively retaining informative variables (IRIV) methods were used to screen characteristic bands. These were combined with partial least-squares regression (PLSR), extreme gradient boosting (XGBoost), random forest regression (RFR), and gradient boosting decision tree (GBDT) to build regression models. The developed model was then used to build the inversion map of pepper leaf chlorophyll distribution. The research results show that: (1) The IRIV-XGBoost model demonstrates the most comprehensive performance in the modeling and inversion stages, and its Rcv2, RMSEcv, and MAEcv are 0.81, 2.76, and 2.30, respectively; (2) The IRIV-XGBoost model was used to calculate the SPAD value of each pixel of pepper leaves, and to subsequently invert the chlorophyll distribution map of pepper leaves at different leaf positions, which can provide support for the intuitive monitoring of crop growth and lay the foundation for the development of hyperspectral field dynamic monitoring sensors.

One-step preparation of red-emitting carbon dots for visual and quantitative detection of copper ions.

A one-step solvothermal method for the preparation of carbon dots with red fluorescence (R-CDs) was put forward, in which sodium citrate and formamide were chosen as precursors, while formamide was adopted as the solvent. The fluorescence emission peak of the as-prepared R-CDs remained the same (600 nm) when the excitation wavelength increased from 490 nm to 560 nm, and the fluorescence quantum yield is 35.3%. Furthermore, the fluorescence intensity of the as-prepared R-CDs could be selectively quenched by copper ions, and the mechanism of Cu2+ quenching R-CDs is the combination of static and dynamic quenching. As a result, the R-CDs were applied for the construction of a fluorescent sensor without any modification for the quantitative and visual detection of copper ions, which is a typical contaminant in water. The limit of detection for the fluorescent sensor was as low as 5 nmol/L, and it can be used to fast and directly confirm whether the content of copper ions in drinking water meets the criteria of the United States Environmental Protection Agency and the World Health Organization.

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing.

With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content.

Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model.

Soil organic matter (SOM) refers to all carbon-containing organic matter in soil and is oneof the most important indicators of soil fertility. The hyperspectral inversion analysis of SOMtraditionally relies on laboratory chemical testing methods, which have the disadvantages of beinginefficient and time-consuming. In this study, 69 soil samples were collected from the Honghufarmland area and a mining area in northwest China. After pretreatment, 10 spectral indicators wereobtained. Ridge regression, kernel ridge regression, Bayesian ridge regression, and AdaBoostalgorithms were then used to construct the SOM hyperspectral inversion model based on thecharacteristic bands, and the accuracy of the models was compared. The results showed that theAdaBoost algorithm based on a grid search had the best accuracy in the different regions. For themining area in northwest China [...].

Inland Lakes Mapping for Monitoring Water Quality Using a Detail/Smoothing-Balanced Conditional Random Field Based on Landsat-8/Levels Data.

The sustainable development of water resources is always emphasized in China, and a set of perfect standards for the division of inland water environment quality have been established to monitor water quality. However, most of the 24 indicators that determine the water quality level in the standards are non-optically active parameters. The weak optical characteristics make it difficult to find significant correlations between the single parameters and the remote sensing imagery. In addition, traditional on-site testing methods have been unable to meet the increasingly extensive water-quality monitoring requirements. Based on the above questions, it's meaningful that the supervised classification process of a detail-preserving smoothing classifier based on conditional random field (CRF) and Landsat-8 data was proposed in the two study areas around Wuhan and Huangshi in Hubei Province. The random forest classifier was selected to model the association potential of the CRF. The results (the first study area: OA = 89.50%, Kappa = 0.841; the second study area: OA = 90.35%, Kappa = 0.868) showed that the water-quality monitoring based on CRF model is feasible, and this approach can provide a reference for water-quality mapping of inland lakes. In the future, it may only require a small amount of on-site sampling to achieve the identification of the water quality levels of inland lakes across a large area of China.

Estimation of Arsenic Content in Soil Based on Laboratory and Field Reflectance Spectroscopy.

: In this study, in order to solve the difficulty of the inversion of soil arsenic (As) content using laboratory and field reflectance spectroscopy, we examined the transferability of the prediction method. Sixty-three soil samples from the Daye city area of the Jianghan Plain region of China were taken and studied in this research. The characteristic wavelengths of soil As content were then extracted from the full bands based on iteratively retaining informative variables (IRIV) coupled with Spearman's rank correlation analysis (SCA). Firstly, the IRIV algorithm was used to roughly select the original spectral data. Gaussian filtering (GF), first derivative (FD) filtering, and gaussian filtering again (GFA) pretreatments were then used to improve the correlation between the spectra and soil As content. A subset with absolute correlation values greater than 0.6 was then retained as the optimal subset after each pretreatment. Finally, partial least squares regression (PLSR), Bayesian ridge regression (BRR), ridge regression (RR), kernel ridge regression (KRR), support vector machine regression (SVMR), eXtreme gradient boosting (XGBoost) regression, and random forest regression (RFR) models were used to estimate the soil As values using the different characteristic variables. The results showed that, compared with the traditional method based on IRIV, using the characteristic bands selected by the IRIV-SCA method can effectively improve the prediction accuracy of the models. For the laboratory spectra experiment stage, the six most representative characteristic bands were selected. The performance of IRIV-SCA-SVMR was found to be the best, with the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) in the validation set being 0.97, 0.22, and 0.11, respectively. For the field spectra experiment stage, the 12 most representative characteristic bands were selected. The performance of IRIV-SCA-XGBoost was found to be the best, with the R2, RMSE, and MAE in the validation set being 0.83, 0.35, and 0.29, respectively. The accuracy and stability of the inversion of soil As content are significantly improved by the use of the proposed method, and the method could be used to provide accurate data for decision support for the treatment and recovery of As pollution over a large area.

Glutathione-mimetic D609 alleviates memory deficits and reduces amyloid-ß deposition in an AßPP/PS1 transgenic mouse model.

Excessive extracellular deposition of amyloid-ß-peptide (Aß) in the brain is a pathological hallmark of Alzheimer's disease (AD). Oxidative stress is associated with the onset and progression of AD and contributes to Aß generation. Tricyclodecan-9-yl-xanthogenate (D609) is a glutathione (GSH)-mimetic compound. Although the antioxidant properties of D609 have been well-studied, its potential therapeutic significance on AD remains unclear. In the present study, we used a mouse model of AD to investigate the effects and the mechanism of action of D609 on AD. We found that D609 treatment significantly improved the spatial learning and alleviated the memory decline in the mice harboring amyloid precursor protein (APP) and presenilin-1 (PS1) double mutations (AßPP/PS1 mice). D609 treatment also increased GSH level, GSH and oxidative glutathione ratio, and superoxide dismutase activity, whereas decreased malondialdehyde and protein carbonyl levels, suggesting that D609 alleviated oxidative stress in AßPP/PS1 mice. In addition, D609 reduced ß-secretase 1 level and decreased amyloidogenic processing of AßPP, consequently reducing Aß deposition in the mice. Thus, our findings suggest that D609 might produce beneficial effects on the prevention and treatment of AD.