The complete chloroplast genome of Castanopsis hystrix Hook. f. & Thomson ex A. DC. 1863 (Fagaceae).

Castanopsis hystrix Hook. f. & Thomson ex A. DC. 1863 (Fagaceae) is an evergreen broad-leaved tree with high economic and ecological value. In this study, the complete chloroplast genome of C. hystrix was sequenced, assembled and annotated. The plastome (plastid genome) of C. hystrix was 160,624 bp in size, consisting of a pair of inverted repeats (IRs, 25,699 bp), a large-single-copy (LSC, 90,276 bp) region, and a small-single-copy (SSC, 18,950 bp). The overall GC content of C. hystrix was 36.8%. A total of 133 genes were annotated, including 88 protein-coding genes (PCG), 37 transfer RNA genes (tRNA), and eight ribosomal RNA genes (rRNA). A maximum likelihood analysis showed that the Castanopsis species form a monophyletic clade. C. hystrix is most closely related to C. tibetana with 100% bootstrap support value. The result enriches the genomic data for the genus Castanopsis, which will contribute to future studies in phylogenetics and evolution.

Cytocompatible chitosan based multi-network hydrogels with antimicrobial, cell anti-adhesive and mechanical properties.

Hydrogel with good mechanical and biological properties has great potential and promise for biomedical applications. Here we fabricated a series of novel cytocompatible chitosan (CS) based double-network (DN) and triple-network (TN) hydrogels by physically-chemically crosslinking methods. Natural polysaccharide CS with abundant resources was chosen as the first network due to its good antimicrobial activity, biocompatibility and easy cross-linking reaction. Zwitterionic sulfopropylbetaine (PDMAPS) was chosen as the second network due its good biocompatibility, antimicrobial and antifouling properties. And nonionic poly(2-hydroxyethyl acrylate) (PHEA) was chosen as the final network due to its good biocompatibility, excellent nonfouling and mechanical properties. Cross-section SEM images showed that both CS/PHEA (DN1, the molar ratio of glutaraldehyde to structural unit of CS is 0.2/3.0) and CS/PDMAPS/PHEA (TN1, the molar ratio of glutaraldehyde to structural unit of CS is 0.2/3.0) hydrogels exhibited a smooth and uniformly dispersed porous microstructures with pore size distribution in the range of 20∼100 µm. The largest compressive stress and tensile stress of DN1 hydrogels reached 84.7 MPa and 292 kPa, respectively, and largest compressive stress and tensile stress of TN1 hydrogels could reach 81.9 MPa and 384 kPa, respectively. Moreover, the value of failure strain for TN1 gels reached 1020%. Besides excellent mechanical properties, DN1 and TN1 gels exhibited good antimicrobial, cytocompatible and antifouling properties due to introduction of antimicrobial chitosan, cell anti-adhesive PDMAPS and PHEA. The combination of the excellent mechanical and biological properties of multiple network hydrogels can provide a potential pathway to develop biomedical hydrogels as promising bioapplications in wound dressing and other biomedical applications.

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.