Hierarchical macro-microporous ZIF-8 nanostructures as efficient nano-lipase carriers for rapid and direct electrochemical detection of nitrogenous diphenyl ether pesticides

Free biocatalyst such as lipase cannot be recovered and reused; and is extremely unstable in harsh reaction condition. Present study aimed to synthesize hierarchical ordered macro-microporous ZIF-8 structures (MAC-ZIF-8) for encapsulation of nano-Burkholderia cepacia lipase (nano BCL). The synthesized MAC-ZIF-8 has good crystallinity, thermal stability and high surface area which made it suitable for immobilization of nano-BCL. The MAC-ZIF-8 immobilized nano-BCL (nano-BCL@MAC-ZIF-8) demonstrated high enzyme activity (4.3778 U) and can be recovered and reused for consecutive hydrolysis reaction. It is found that the nano-BCL is well encapsulated with the macropores of the MAC-ZIF-8 structures and they are able to preserve their native configuration with high affinity for substrates (lower Km of 1.068 mM as compared to that of nano-BCL@ZIF-8). The nano-BCL@MAC-ZIF-8 can also be used to fabricate electrochemical biosensors for detection of nitrogenous diphenyl ether pesticides (nitrofen) in real food systems (apricots). The fabricated biosensors demonstrated wide linear range (0−114µM), low limit of detection (0.46µM) and good recovery rate. In comparison to conventional detection method, the fabricated biosensor also has added advantages in the form of easy and convenient operation, low cost, and better linear range

Improved Modeling of Gross Primary Productivity of Alpine Grasslands on the Tibetan Plateau Using the Biome-BGC Model

The ability of process-based biogeochemical models in estimating the gross primary productivity (GPP) of alpine vegetation is largely hampered by the poor representation of phenology and insufficient calibration of model parameters. The development of remote sensing technology and the eddy covariance (EC) technique has made it possible to overcome this dilemma. In this study, we have incorporated remotely sensed phenology into the Biome-BGC model and calibrated its parameters to improve the modeling of GPP of alpine grasslands on the Tibetan Plateau (TP). Specifically, we first used the remotely sensed phenology to modify the original meteorological-based phenology module in the Biome-BGC to better prescribe the phenological states within the model. Then, based on the GPP derived from EC measurements, we combined the global sensitivity analysis method and the simulated annealing optimization algorithm to effectively calibrate the ecophysiological parameters of the Biome-BGC model. Finally, we simulated the GPP of alpine grasslands on the TP from 1982 to 2015 based on the Biome-BGC model after a phenology module modification and parameter calibration. The results indicate that the improved Biome-BGC model effectively overcomes the limitations of the original Biome-BGC model and is able to reproduce the seasonal dynamics and magnitude of GPP in alpine grasslands. Meanwhile, the simulated results also reveal that the GPP of alpine grasslands on the TP has increased significantly from 1982 to 2015 and shows a large spatial heterogeneity, with a mean of 289.8 gC/m2/yr or 305.8 TgC/yr. Our study demonstrates that the incorporation of remotely sensed phenology into the Biome-BGC model and the use of EC measurements to calibrate model parameters can effectively overcome the limitations of its application in alpine grassland ecosystems, which is important for detecting trends in vegetation productivity. This approach could also be upscaled to regional and global scales

A Flexible, Wearable, Humidity‐Resistant and Self‐Powered Sensor Fabricated by Chitosan‐Critic Acid Film and its Applications in Human Motion Monitoring and Energy Harvesting

Abstract Being a resource‐abundant natural material, chitosan shows good frictional electrical properties and has become one of the most used materials for triboelectric nanogenerators (TENGs). To improve water resistance and mechanical properties, herein, a strategy is proposed to modify the chitosan film by citric acid. The results show that both humidity‐resistant and mechanical properties of the modified films can be notably enhanced due to the formation of a network structure between chitosan molecular chains. Excellent output performance of 77.5 V and 2.66 µA is achieved in the TENG based on the modified film with a mass ratio of chitosan/citric acid = 3:1. Under a relative humidity (RH) of 80%RH, it can output 70.16% of the voltage compared to that under 20%RH. Self‐powered tactile sensor based on the TENG exhibits a sensitive response to pressure and bending and humidity resistance, giving the sensor tremendous promise for a wider range of human motion monitoring and energy harvesting

Photocatalytic activity enhanced via surface hybridization

Abstract Surface hybrid inorganic semiconductors photocatalysts with π conjugated structure have recently been widely used in the field of photocatalytic environmental remediation and energy conversion. Surface hybridization is considered to be an efficient way to significantly enhance the photocatalytic activity by several times, completely inhibit photocorrosion of inorganic photocatalysts and expand the spectral response range of photocatalysts. This review provides a comprehensive overview of the surface hybridization definition, the principle of enhancing photocatalytic performance and the control factors of surface hybridization to promote photoactivity. Summarize the preparation and structural identification method of the surface hybrid photocatalyst. Special emphasis is placed on the various photocatalytic system construction of surface hybridization. The development of surface hybrid photocatalysts for pollutant degradation and energy conversion are further presented. Finally, the challenges and future development prospects of surface hybridization in photocatalysis are discussed. We hope this critical review can provide a clear picture of the state‐of‐the‐art achievements and facilitate the applications of surface hybrid photocatalysts in environmental remediation and energy conversion

Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index

Automatic detection of buildings from very high resolution (VHR) satellite images is a current research hotspot in remote sensing and computer vision. However, many irrelevant objects with similar spectral characteristics to buildings will cause a large amount of interference to the detection of buildings, thus making the accurate detection of buildings still a challenging task, especially for images captured in complex environments. Therefore, it is crucial to develop a method that can effectively eliminate these interferences and accurately detect buildings from complex image scenes. To this end, a new building detection method based on the morphological building index (MBI) is proposed in this study. First, the local feature points are detected from the VHR remote sensing imagery and they are optimized by the saliency index proposed in this study. Second, a voting matrix is calculated based on these optimized local feature points to extract built-up areas. Finally, buildings are detected from the extracted built-up areas using the MBI algorithm. Experiments confirm that our proposed method can effectively and accurately detect buildings in VHR remote sensing images captured in complex environments

Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System

Chlorophyll-a(chl-a) has been used as an important indicator of water quality. Great efforts have been invested to develop remote-sensing-based chl-a retrieval models. However, due to the spatial difference in chl-a concentration, a single model usually cannot accurately predict the whole range of chl-a concentration. To test the performance of precedent chl-a models, we carried out an experiment along the upper and middle reaches of the Kaidu River and around some small ponds in the Bayanbulak Wetland. We measured water surface reflectance in the field and analyzed the chl-a concentration in the laboratory. Initially, we performed a sensitivity analysis of the spectrum band to chl-a concentration with the aim of identifying the most suitable bands for various chl-a models. We found that the water samples could be divided into two groups with a threshold of 4.50 mg/m3. Then, we tested the performance of 11 precedent chl-a retrieval models and 7 spectral index-based regression models from this study for all the sample datasets and the two separate datasets with relatively high and low chl-a concentrations. Through a complete comparison of the performance of these models, we selected the D3B model for water bodies with high chl-a concentration and OC2 model (ocean color 2) for low chl-a concentration waters, resulting in the hierarchical and piecewise retrieval algorithm OC2-D3B. The chl-a concentration of 4.50 mg/m3 corresponded to the D3B value of −0.051; therefore, we used −0.051 as the threshold value of the OC2-D3B model. The result of the OC2-D3B model showed a better performance than the other algorithms. Finally, we mapped the spatial distribution and seasonal pattern of chl-a concentration in Bayanbulak Wetland using Sentinel-2 images from 2016 to 2019. The results indicated that the chl-a concentration in the riparian ponds was generally in the range of 8–10 mg/m3, which was higher than that in rivers with a range of 2–4 mg/m3. The highest chl-a concentration usually appears in summer, followed by spring and autumn, and the lowest in winter. The correlation between meteorological data and chl-a concentration showed that temperature is the dominant factor for chl-a concentration changes. Our analytical framework could provide a better way to accurately map the spatial distribution of chl-a concentration in complex river systems

A Hybrid Model for Carbon Price Forecasting Based on Improved Feature Extraction and Non-Linear Integration

Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on improved feature extraction and non-linear integration, which is built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FuzzyEn), improved random forest using particle swarm optimisation (PSORF), extreme learning machine (ELM), long short-term memory (LSTM), non-linear integration based on multiple linear regression (MLR) and random forest (MLRRF), and error correction with the autoregressive integrated moving average model (ARIMA), named CEEMDAN-FuzzyEn-PSORF-ELM-LSTM-MLRRF-ARIMA. Firstly, CEEMDAN is combined with FuzzyEn in the feature selection process to improve extraction efficiency and reliability. Secondly, at the critical prediction stage, PSORF, ELM, and LSTM are selected to predict high, medium, and low complexity sequences, respectively. Thirdly, the reconstructed sequences are assembled by applying MLRRF, which can effectively improve the prediction accuracy and generalisation ability. Finally, error correction is conducted using ARIMA to obtain the final forecasting results, and the Diebold–Mariano test (DM test) is introduced for a comprehensive evaluation of the models. With respect to carbon prices in the pilot regions of Shenzhen and Hubei, the results indicate that the proposed model has higher prediction accuracy and robustness. The main contributions of this paper are the improved feature extraction and the innovative combination of multiple linear regression and random forests into a non-linear integrated framework for carbon price forecasting. However, further optimisation is still a work in progress

Nanoporous Calcium Silicate and PLGA Biocomposite for Bone Repair

Nanoporous calcium silicate (n-CS) with high surface area was synthesized using the mixed surfactants of EO20PO70EO20 (polyethylene oxide)20(polypropylene oxide)70(polyethylene oxide)20, P123) and hexadecyltrimethyl ammonium bromide (CTAB) as templates, and its composite with poly(lactic acid-co-glycolic acid) (PLGA) were fabricated. The results showed that the n-CS/PLGA composite (n-CPC) with 20 wt% n-CS could induce a dense and continuous layer of apatite on its surface after soaking in simulated body fluid (SBF) for 1 week, suggesting the excellent in vitro bioactivity. The n-CPC could promote cell attachment on its surfaces. In addition, the proliferation ratio of MG63 cells on n-CPC was significantly higher than PLGA; the results demonstrated that n-CPC had excellent cytocompatibility. We prepared n-CPC scaffolds that contained open and interconnected macropores ranging in size from 200 to 500 μm. The n-CPC scaffolds were implanted in femur bone defect of rabbits, and the in vivo biocompatibility and osteogenicity of the scaffolds were investigated. The results indicated that n-CPC scaffolds exhibited good biocompatibility, degradability, and osteogenesis in vivo. Collectively, these results suggested that the incorporation of n-CS in PLGA produced biocomposites with improved bioactivity and biocompatibility

YTH Domain Proteins Play an Essential Role in Rice Growth and Stress Response

As the most prevalent epi-transcriptional modification, m6A modifications play essential roles in regulating RNA fate. The molecular functions of YTH521-B homology (YTH) domain proteins, the most known READER proteins of m6A modifications, have been well-studied in animals. Although plants contain more YTH domain proteins than other eukaryotes, little is known about their biological importance. In dicot species Arabidopsis thaliana, the YTHDFA clade members ECT2/3/4 and CPSF30-L are well-studied and important for cell proliferation, plant organogenesis, and nitrate transport. More emphasis is needed on the biological functions of plant YTH proteins, especially monocot YTHs. Here we presented a detailed phylogenetic relationship of eukaryotic YTH proteins and clustered plant YTHDFC clade into three subclades. To determine the importance of monocot YTH proteins, YTH knockout mutants and RNAi-induced knockdown plants were constructed and used for phenotyping, transcriptomic analysis, and stress treatments. Knocking out or knocking down OsYTHs led to the downregulation of multicellular organismal regulation genes and resulted in growth defects. In addition, loss-of-function ythdfa mutants led to better salinity tolerance whereas ythdfc mutants were more sensitive to abiotic stress. Overall, our study establishes the functional relevance of rice YTH genes in plant growth regulation and stress response

Remote Sensing Retrieval of Turbidity in Alpine Rivers based on high Spatial Resolution Satellites

Turbidity, relating to underwater light attenuation, is an important optical parameter for water quality evaluation. Satellite estimation of turbidity in alpine rivers is challenging for common ocean color retrieval models due to the differences in optical properties of the water bodies. In this study, we present a simple two-band semi-analytical turbidity (2BSAT) retrieval model for estimating turbidity in five alpine rivers with varying turbidity from 1.01 to 284 NTU. The model was calibrated and validated, respectively, while using one calibration dataset that was obtained from the Three Parallel Rivers basin and two independent validation datasets that were obtained from the Kaidu River basin and the Yarlung Zangbo River basin. The results show that the model has excellent performance in deriving turbidity in alpine rivers. We verified the consistency of the simulated reflectance and satellite-based reflectance and calibrated the 2BSAT model for the specified bands of high spatial resolution satellites in order to achieve the goal of remote sensing monitoring. It is concluded that the model can be used for the quantitative monitoring of turbidity in alpine rivers using satellite images. Based on the model, we used the Sentinel-2 images from one year to identify the seasonal patterns of turbidity of five alpine rivers and the Landsat series images from 1989 to 2018 to analyze the turbidity variation trends of these rivers. The results indicate that the turbidity of these alpine rivers usually presents the highest level in summer, followed by spring and autumn, and the lowest in winter. Meanwhile, the variation trends of turbidity over the past 30 years present distinctly different characteristics in the five rivers