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Heat stress, as a negative factor, severely threatens the quality and production of bottle gourd, which prefers to grow in a warm environment. To understand which genes are involved in the resistance to heat stress in bottle gourd (Lagenaria siceraria (Mol.) Standl.), we analyzed the characteristics of two genetic bottle gourd varieties, "Mei feng"-MF (heat resistant) and "Lv long"-LL (heat sensitive). Under heat stress, MF plants exhibited a higher survival rate, lower relative electrolytic leakage, and decreased stomatal aperture compared with LL. In addition, RNA-Seq was carried out on the two varieties under normal conditions and heat stress. The results revealed a total of 1485 up-regulated and 946 down-regulated genes under normal conditions, while 602 genes were up-regulated and 1212 genes were down-regulated under heat stress. Among these genes, several differentially expressed genes (DEGs) involved in the MAPK (mitogen-activated protein kinase) signaling pathway and members of bHLH (basic helix-loop-helix) transcription factors showed significant up- or down-regulation after heat stress. Next, to validate these findings, we conducted quantitative real-time PCR (qRT-PCR) analysis, which confirmed the expression patterns of the genes detected through RNA-Seq. Collectively, the DEGs between the two contrasting cultivars identified in our study provide novel insight into excavating helpful candidate genes associated with heat tolerance in bottle gourd. [ABSTRACT FROM AUTHOR]

Herein, a green, carbon dioxide-in-water high-internal-phase emulsion (C/W HIPEs) was developed and stabilized with polyvinyl alcohol (PVA) for the formation of chitosan oligosaccharide/poly(acrylamide-co-sodium 4-styrene sulfonate) [COS/P(AM-co-SSS)] monolithic porous hydrogel. The obtained monolith was characterized via FT-IR and SEM. The SEM patterns depicted that the monoliths were interconnected, the void sizes were 78.5 µm, and the interconnected pore throats were 28 μm approximately. Mechanical measurement results indicated that the maximum compress stress of the monolith could reach 334.4 kPa at 90% strain, and it exhibited good mechanical stability. After 200 cycles of compression, it could still recover its original shape without cracking. The obtained COS-based monolith was selected to remove tetracycline (TC) for evaluating the adsorptive features of the interpenetrating pore-containing monolith. The monolithic COS/P(AM-co-SSS) hydrogel behaved with strong antibiotic adsorption capacity (1600.4 mg/g for TC). The adsorption process agreed well with the pseudo-second-order kinetic and Langmuir isothermal models. In addition, the porous monolith had a strong electrostatic force on TC according to the thermodynamic study. This work provides a green route for the development of novel monolithic hydrogels and highlights its potential application in the treatment of antibiotic-containing wastewater. [ABSTRACT FROM AUTHOR]

Porous carbon nanofibers are commonly used for adsorption processes owing to their high specific surface area and rich pore structure. However, the poor mechanical properties of polyacrylonitrile (PAN)-based porous carbon nanofibers have limited their applications. Herein, we introduced solid waste-derived oxidized coal liquefaction residue (OCLR) into PAN-based nanofibers to obtain activated reinforced porous carbon nanofibers (ARCNF) with enhanced mechanical properties and regeneration for efficient adsorption of organic dyes in wastewater. This study examined the effects of contact time, concentration, temperature, pH, and salinity on the adsorption capacity. The adsorption processes of the dyes in ARCNF are appropriately described by the pseudo-second-order kinetic model. The maximum adsorption capacity for malachite green (MG) on ARCNF is 2712.84 mg g−1 according to the fitted parameters of the Langmuir model. Adsorption thermodynamics indicated that the adsorptions of the five dyes are spontaneous and endothermic processes. In addition, ARCNF have good regenerative performance, and the adsorption capacity of MG is still higher than 76% after 5 adsorption-desorption cycles. Our prepared ARCNF can efficiently adsorb organic dyes in wastewater, reducing the pollution to the environment and providing a new idea for solid waste recycling and water treatment. [ABSTRACT FROM AUTHOR]

It is crucial but challenging to detect intermediate or end products promptly. Traditional chemical detection methods are time-consuming and cannot detect mineral phase content. Thermal infrared hyperspectral (TIH) technology is an effective means of real-time imaging and can precisely capture the emissivity characteristics of objects. This study introduces TIH to estimate the content of potassium salts, with a model based on Competitive Adaptive Reweighted Sampling (CARS) and Partial Least Squares Regression (PLSR). The model takes the emissivity spectrum of potassium salt into account and accurately predicts the content of Mixing Potassium (MP), a mineral mixture produced in Lop Nur, Xinjiang. The main mineral content in MP was measured by Mineral Liberation Analyzer (MLA), mainly including picromerite, potassium chloride, magnesium sulfate, and less sodium chloride. 129 configured MP samples were divided into calibration (97 samples) and prediction (32 samples) sets. The CARS-PLSR method achieved good prediction results for MP mineral content (picromerite: correlation coefficient of correction set ( R p 2 ) = 0.943, predicted root mean square error (RMSEP) = 2.72%, relative predictive deviation (RPD) = 4.24; potassium chloride: R p 2 = 0.948, RMSEP = 2.86%, RPD = 4.42). Experimental results convey that TIH technology can effectively identify the emissivity characteristics of MP minerals, facilitating quantitative detection of MP mineral content. [ABSTRACT FROM AUTHOR]

To meet the demand for the separation of specific substances, the construction of porous composite aerogels with a high specific surface area and a strong adsorption capacity is still a challenge. Herein, a sodium alginate/bentonite composite aerogel was efficiently prepared through supercritical fluid drying. The aerogel's volume shrank less during supercritical drying, maintaining its original three-dimensional mesh structure. The resulting aerogel had a large specific surface area (445 m2/g), a low density (0.059 g/cm3), and a large pore volume (3.617 cm3/g). Due to the fixation and intercalation effects, bentonite was uniformly dispersed in the sodium alginate matrixes. The adsorption of lysozyme by the composite aerogel was evaluated, and the results showed that the optimal adsorption pH was 8 when the pH of the phosphoric acid buffer solution was between pH = 5 and 8.5. The time for adsorption to reach equilibrium was 8 h. The adsorption capacity increased with the increase in bentonite content, and when the initial concentration of lysozyme was from 0.2 to 1.2 g/L, the adsorption capacity first increased and then stabilized, and the maximum adsorption amount was 697 mg/g. The adsorption behavior was simulated in the isothermal region, and the linear correlation coefficient of Langmuir isothermal adsorption fitting was found to be 0.997. Thus, this composite aerogel with strong adsorption capacity can be used as a good alternative to enzymatic adsorbents or immobilized materials. [ABSTRACT FROM AUTHOR]

Soil total arsenic (TAs) contamination caused by human activities—such as mining, smelting, and agriculture—is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470–900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on Spearman's rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (R2 = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (R2 = 0.78, RMSE = 0.42), random forest (RF) (R2 = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (R2 = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with Spearman's rank correlation with machine learning models can be used to estimate soil TAs content. [ABSTRACT FROM AUTHOR]

Emissivity information derived from thermal infrared (TIR) hyperspectral imagery has the advantages of both high spatial and spectral resolutions, which facilitate the detection and identification of the subtle spectral features of ground targets. Despite the emergence of several different TIR hyperspectral imagers, there are still no universal spectral emissivity measurement standards for TIR hyperspectral imagers in the field. In this paper, we address the problems encountered when measuring emissivity spectra in the field and propose a practical data acquisition and processing framework for a Fourier transform (FT) TIR hyperspectral imager—the Hyper-Cam LW—to obtain high-quality emissivity spectra in the field. This framework consists of three main parts. (1) The performance of the Hyper-Cam LW sensor was evaluated in terms of the radiometric calibration and measurement noise, and a data acquisition procedure was carried out to obtain the useful TIR hyperspectral imagery in the field. (2) The data quality of the original TIR hyperspectral imagery was improved through preprocessing operations, including band selection, denoising, and background radiance correction. A spatial denoising method was also introduced to preserve the atmospheric radiance features in the spectra. (3) Three representative temperature-emissivity separation (TES) algorithms were evaluated and compared based on the Hyper-Cam LW TIR hyperspectral imagery, and the optimal TES algorithm was adopted to determine the final spectral emissivity. These algorithms are the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm, the improved Advanced Spaceborne Thermal Emission and Reflection Radiometer temperature and emissivity separation (ASTER-TES) algorithm, and the Fast Line-of-sight Atmospheric Analysis of Hypercubes-IR (FLAASH-IR) algorithm. The emissivity results from these different methods were compared to the reference spectra measured by a Model 102F spectrometer. The experimental results indicated that the retrieved emissivity spectra from the ISSTES algorithm were more accurate than the spectra retrieved by the other methods on the same Hyper-Cam LW field data and had close consistency with the reference spectra obtained from the Model 102F spectrometer. The root-mean-square error (RMSE) between the retrieved emissivity and the standard spectra was 0.0086, and the spectral angle error was 0.0093. [ABSTRACT FROM AUTHOR]

Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal flexibility, which open a new way to accurate agricultural monitoring. To extract crop types from the airborne hyperspectral images, we propose a fine classification method based on multi-feature fusion and deep learning. In this research, the morphological profiles, GLCM texture and endmember abundance features are leveraged to exploit the spatial information of the hyperspectral imagery. Then, the multiple spatial information is fused with the original spectral information to generate classification result by using the deep neural network with conditional random field (DNN+CRF) model. Specifically, the deep neural network (DNN) is a deep recognition model which can extract depth features and mine the potential information of data. As a discriminant model, conditional random field (CRF) considers both spatial and contextual information to reduce the misclassification noises while keeping the object boundaries. Moreover, three multiple feature fusion approaches, namely feature stacking, decision fusion and probability fusion, are taken into account. In the experiments, two airborne hyperspectral remote sensing datasets (Honghu dataset and Xiong'an dataset) are used. The experimental results show that the classification performance of the proposed method is satisfactory, where the salt and pepper noise is decreased, and the boundary of the ground object is preserved. [ABSTRACT FROM AUTHOR]

Although hyperspectral anomaly detection is commonly conducted in the visible, near-infrared, and shortwave infrared spectral regions, there has been less research on hyperspectral anomaly detection in the longwave infrared (LWIR) hyperspectral region. The radiance of thermal infrared hyperspectral imagery is determined by the temperature and emissivity. To avoid the detection uncertainty caused by the single factor of temperature, emissivity can be introduced to detect anomalies. However, in the emissivity domain, the spectral contrast and signal-to-noise ratio (SNR) are low, which makes it difficult to separate the anomalies from the background. In this paper, an anomaly detection method combining emissivity and a segmented low-rank prior (EaSLRP) is proposed for use with thermal infrared hyperspectral imagery. The EaSLRP method is divided into three parts—(1) temperature/emissivity retrieval, (2) extraction of the thermal infrared hyperspectral background information, and (3) Mahalanobis distance detection. A homogeneous region generation method is also proposed to solve the problem of the complex global background leading to inaccurate background estimation. The GoDec method is used for matrix decomposition and background information extraction and to remove some of the noise. The proposed Mahalanobis distance detector then uses the background component and original image for anomaly detection, while highlighting the spectral difference between the anomalies and background. This method can also suppress the influence of noise, to some extent. The experimental results obtained with airborne Fourier transform thermal infrared spectrometer hyperspectral images demonstrate that the EaSLRP method is effective when compared with the Reed–Xiaoli detector (RXD), the segmented RX detector (SegRX), the low-rank and sparse representation-based detector (LRASR), the low-rank and sparse matrix decomposition (LRaSMD)-based Mahalanobis distance method (LSMAD), and the locally enhanced low-rank prior method (LELRP-AD). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) refers to all carbon-containing organic matter in soil and is one of the most important indicators of soil fertility. The hyperspectral inversion analysis of SOM traditionally relies on laboratory chemical testing methods, which have the disadvantages of being inefficient and time-consuming. In this study, 69 soil samples were collected from the Honghu farmland area and a mining area in northwest China. After pretreatment, 10 spectral indicators were obtained. Ridge regression, kernel ridge regression, Bayesian ridge regression, and AdaBoost algorithms were then used to construct the SOM hyperspectral inversion model based on the characteristic bands, and the accuracy of the models was compared. The results showed that the AdaBoost algorithm based on a grid search had the best accuracy in the different regions. For the mining area in northwest China, R p 2 = 0.91, R M S E p = 0.22, and M A E p = 0.2. For the Honghu farmland area, R p 2 = 0.86, R M S E p = 0.72, and M A E p = 0.56. The detection of SOM content using hyperspectral technology has the characteristics of a high detection precision and high speed, which will be of great significance for the rapid development of precision agriculture. [ABSTRACT FROM AUTHOR]

Because of the nontoxic solvents contained in CO2-in-water emulsions, porous polymer composites templated from these emulsions are conducive for bio-applications. Herein, bio-active rod-like calcium-organic framworks (Ca-BDC MOFs, BDC= 1,4-benzenedicarboxylate anion) particles co-stabilized CO2-in-water high internal phase emulsion (C/W HIPE) in the presence of polyvinyl alcohol (PVA) is first presented. After curing of the continuous phase, followed by releasing CO2, integral 3D macro-porous Ca-BDC monolith and Ca-BDC/Poly(2-hydroxyethyl methacrylate-co-acrylamide) HIPEs monolithic composites [Ca-BDC/P(AM-co-HEMA)HIPEs] with open-cell macro-porous structures were successfully prepared. The pore structure of these porous composite can be tuned by means of tailoring the Ca-BDC dosage, carbon dioxide pressure, and continuous phase volume fractions in corresponding C/W HIPEs. Results of bio-compatibility tests show that these Ca-BDC/P(AM-co-HEMA)HIPEs monoliths have non-cytotoxicity on HepG2 cells; also, the E. coli can grow either on the surfaces or inside these monoliths. Furthermore, immobilization of β-amylase on these porous composite presents that β-amylase can be well-anchored into the porous polymer composites, its catalytic activity can be maintained even after 10 cycles. This work combined bio-active MOFs Ca-BDC, bio-compatible open-cell macroporous polymer PAM-co-HEMA and green C/W HIPEs to present a novel and facile way to prepare interconnected macro-porous MOFs/polymer composites. Compared with the existing other well-known materials such as hydrogels, these porous composites possess well-defined tunable pore structures and superior bio-activity, thereby have promising applications in bio-tissue engineering, food, and pharmaceutical. [ABSTRACT FROM AUTHOR]

The HY-1C satellite, as part of China's optical satellite constellation for global ocean monitoring, monitors the ocean and coastal environment by the three broad visible bands of the Coastal Zone Imager (CZI) instrument. However, as a result of the sensor instrument noise, the atmospheric environment during imaging, and the shooting angle, the satellite images often show uneven illumination and inconsistent color between neighboring images. In this paper, according to the characteristics of the HY-1C CZI instrument, we propose a color consistency processing framework for coastal zone images of Antarctica. First of all, the high-frequency and low-frequency information of the image is separated by a statistical filter with simple clustering. The uneven lighting is then replaced by artificial lighting, which is globally uniform. Finally, the color difference between images is corrected by a color transfer method. In order to evaluate the color consistency results quantitatively, a new quantitative evaluation method is proposed. The experimental results for the coastal zone images of Antarctica show that the new processing framework can effectively eliminate the unevenness in the lighting and color. The mosaic results show a good performance in consistent lighting and tones, and the lack of visible mosaic lines proves the effectiveness of the proposed method. The quantitative evaluation analysis confirms the superiority of the proposed method over the Wallis method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Local climate zones (LCZ) have become a generic criterion for climate analysis among global cities, as they can describe not only the urban climate but also the morphology inside the city. LCZ mapping based on the remote sensing classification method is a fundamental task, and the protocol proposed by the World Urban Database and Access Portal Tools (WUDAPT) project, which consists of random forest classification and filter-based spatial smoothing, is the most common approach. However, the classification and spatial smoothing lack a unified framework, which causes the appearance of small, isolated areas in the LCZ maps. In this paper, a spatial-contextual information-based self-training classification framework (SCSF) is proposed to solve this LCZ classification problem. In SCSF, conditional random field (CRF) is used to integrate the classification and spatial smoothing processing into one model and a self-training method is adopted, considering that the lack of sufficient expert-labeled training samples is always a big issue, especially for the complex LCZ scheme. Moreover, in the unary potentials of CRF modeling, pseudo-label selection using a self-training process is used to train the classifier, which fuses the regional spatial information through segmentation and the local neighborhood information through moving windows to provide a more reliable probabilistic classification map. In the pairwise potential function, SCSF can effectively improve the classification accuracy by integrating the spatial-contextual information through CRF. The experimental results prove that the proposed framework is efficient when compared to the traditional mapping product of WUDAPT in LCZ classification. [ABSTRACT FROM AUTHOR]

The formation of black-odor water in urban rivers has a long history. It not only seriously affects the image of the city, but also easily breeds germs and damages the urban habitat. The prevention and treatment of urban black-odor water have long been important topics nationwide. "Action Plan for Prevention and Control of Water Pollution" issued by the State Council shows Chinese government's high attention to this issue. However, treatment and monitoring are inextricably linked. There are few studies on the large-scale monitoring of black-odor water, especially the cases of using unmanned aerial vehicle (UAV) to efficiently and accurately monitor the spatial distribution of urban river pollution. Therefore, in order to get rid of the limitations of traditional ground sampling to evaluate the point source pollution of rivers, the UAV-borne hyperspectral imagery was applied in this paper. It is hoped to grasp the pollution status of the entire river as soon as possible from the surface. However, the retrieval of multiple water quality parameters will lead to cumulative errors, so the Nemerow comprehensive pollution index (NCPI) is introduced to characterize the pollution level of urban water. In the paper, the retrieval results of six regression models including gradient boosting decision tree regression (GBDTR) were compared, trying to find a regression model for the retrieval NCPI in the current scenario. In the first study area, the retrieval accuracy of the training dataset (adjusted_R2 = 0.978), and test dataset (adjusted_R2 = 0.974) was higher than that of the other regression models. Although the retrieval effect of random forest is similar to that of GBDTR in both training accuracy and image inversion, it is more computationally expensive. Finally, the spatial distribution graphs of NCPI and its technical feasibility in monitoring pollution sources were investigated, in combination with field observations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Because of the nontoxic solvents contained in CO 2 -in-water emulsions, porous polymer composites templated from these emulsions are conducive for bio-applications. Herein, bio-active rod-like calcium-organic framworks (Ca-BDC MOFs, BDC= 1,4-benzenedicarboxylate anion) particles co-stabilized CO 2 -in-water high internal phase emulsion (C/W HIPE) in the presence of polyvinyl alcohol (PVA) is first presented. After curing of the continuous phase, followed by releasing CO 2 , integral 3D macro-porous Ca-BDC monolith and Ca-BDC/Poly(2-hydroxyethyl methacrylate- co -acrylamide) HIPEs monolithic composites [Ca-BDC/P(AM- co -HEMA)HIPEs] with open-cell macro-porous structures were successfully prepared. The pore structure of these porous composite can be tuned by means of tailoring the Ca-BDC dosage, carbon dioxide pressure, and continuous phase volume fractions in corresponding C/W HIPEs. Results of bio-compatibility tests show that these Ca-BDC/P(AM- co -HEMA)HIPEs monoliths have non-cytotoxicity on HepG2 cells; also, the E. coli can grow either on the surfaces or inside these monoliths. Furthermore, immobilization of β -amylase on these porous composite presents that β -amylase can be well-anchored into the porous polymer composites, its catalytic activity can be maintained even after 10 cycles. This work combined bio-active MOFs Ca-BDC, bio-compatible open-cell macroporous polymer PAM- co -HEMA and green C/W HIPEs to present a novel and facile way to prepare interconnected macro-porous MOFs/polymer composites. Compared with the existing other well-known materials such as hydrogels, these porous composites possess well-defined tunable pore structures and superior bio-activity, thereby have promising applications in bio-tissue engineering, food, and pharmaceutical.