ALBERT

Description: Accurate and timely access to the production area of crop seeds allows the seed market and secure seed supply to be monitored. Seed maize and common maize production fields typically share similar phenological development profiles with differences in the planting patterns, which makes it challenging to separate these fields from decametric-resolution satellite images. In this research, we proposed a method to identify seed maize production fields as early as possible in the growing season using a time series of remote sensing images in the Liangzhou district of Gansu province, China. We collected Sentinel-2 and GaoFen-1 (GF-1) images captured from March to September. The feature space for classification consists of four original bands, namely red, green, blue, and near-infrared (nir), and eight vegetation indexes. We analyzed the timeliness of seed maize identification using Sentinel-2 time series of different time spans and identified the earliest time frame for reasonable classification accuracy. Then, the earliest time series that met the requirements of regulatory accuracy were compared and analyzed. Four machine/deep learning algorithms were tested, including K-nearest neighbor (KNN), support vector classification (SVC), random forest (RF), and long short-term memory (LSTM). The results showed that using Sentinel-2 images from March to June, the RF and LSTM algorithms achieve over 88% accuracy, with the LSTM performing the best (90%). In contrast, the accuracy of KNN and SVC was between 82% and 86%. At the end of June, seed maize mapping can be carried out in the experimental area, and the precision can meet the basic requirements of monitoring for the seed industry. The classification using GF-1 images were less accurate and reliable; the accuracy was 85% using images from March to June. To achieve near real-time identification of seed maize fields early in the growing season, we adopted an automated sample generation approach for the current season using only historical samples based on clustering analysis. The classification accuracy using new samples extracted from historical mapping reached 74% by the end of the season (September) and 63% by the end of July. This research provides important insights into the classification of crop fields cultivated with the same crop but different planting patterns using remote sensing images. The approach proposed by this study enables near-real time identification of seed maize production fields within the growing season, which could effectively support large-scale monitoring of the seed supply industry.

Description: The extraction and evaluation of crop production units are important foundations for agricultural production and management in modern smallholder regions, which are very significant to the regulation and sustainable development of agriculture. Crop areas have been recognized efficiently and accurately via remote sensing (RS) and machine learning (ML), especially deep learning (DL), which are too rough for modern smallholder production. In this paper, a delimitation-grading method for actual crop production units (ACPUs) based on RS images was explored using a combination of a mask region-based convolutional neural network (Mask R-CNN), spatial analysis, comprehensive index evaluation, and cluster analysis. Da’an City, Jilin province, China, was chosen as the study region to satisfy the agro-production demands in modern smallholder areas. Firstly, the ACPUs were interpreted from perspectives such as production mode, spatial form, and actual productivity. Secondly, cultivated land plots (C-plots) were extracted by Mask R-CNN with high-resolution RS images, which were used to delineate contiguous cultivated land plots (CC-plots) on the basis of auxiliary data correction. Then, the refined delimitation-grading results of the ACPUs were obtained through comprehensive evaluation of spatial characteristics and real productivity clustering. For the conclusion, the effectiveness of the Mask R-CNN model in C-plot recognition (loss = 0.16, mean average precision (mAP) = 82.29%) and a reasonable distance threshold (20 m) for CC-plot delimiting were verified. The spatial features were evaluated with the scale-shape dimensions of nine specific indicators. Real productivities were clustered by the incorporation of two-step cluster and K-Means cluster. Furthermore, most of the ACPUs in the study area were of a reasonable scale and an appropriate shape, holding real productivities at a medium level or above. The proposed method in this paper can be adjusted according to the changes of the study area with flexibility to assist agro-supervision in many modern smallholder regions.

Description: Revealing the characteristics of soil moisture and temperature under typical sloping land uses in the loess hilly region is of great significance for the efficient and sustainable use of sloping land resources. In this study, the soil moisture content in the 0–160 cm soil layer and the soil temperature in the 0–100 cm soil layer under soybean sloping field, maize terraced field, jujube orchard, and grassland were continuously observed during the 2014 and 2015 growing seasons (May to October). Traditional statistical analysis and wavelet fractal dimension method were used to study the characteristics and complexity of soil moisture and temperature changes under different sloping land uses. The main findings are as follows: (1) Maize terraced field obtained high soil moisture content in the 0–160 cm soil layer, showing the outstanding effect of soil moisture conservation, especially in the drought growing season. Maize terraced field minimized the changing amplitude (Ka), variation degree (Cv), and active layer of soil moisture in the 0–160 cm soil layer and the Ka and Cv of soil temperature in the 0−100 cm soil layer. The maize terraced field had the minimum fractal dimensions of soil moisture and temperature both in normal precipitation and drought growing seasons, indicating that the maize terraced field minimized the complexity of soil moisture and temperature changes. (2) The jujube orchard obtained the minimum soil moisture content in the 0−160 cm soil layer, and greatly increased the Ka, Cv, and active layer of soil moisture both in normal precipitation and drought growing seasons. The jujube orchard obtained the maximum soil temperature in the 0–100 cm soil layer, and greatly increased the Ka and Cv of soil temperature. The jujube orchard also had the maximum fractal dimensions of soil moisture and temperature, indicating that soil moisture and temperature changes in jujube orchard were the most complex. (3) Compared to jujube orchard, soybean sloping field and grassland increased soil moisture content, reduced the Ka and Cv of soil moisture and temperature, and weakened the complexity of soil moisture and temperature changes. (4) The analysis results of the complexity of soil moisture and temperature changes under the experimental sloping land uses based on the wavelet fractal dimension method were consistent with the traditional statistical analysis results, indicating that it is feasible to evaluate the complexity of soil moisture and temperature changes under the typical sloping land uses in the loess hilly region by using wavelet fractal dimension method. In summary, terraced fields were conducive to improving soil moisture content and maintaining the stability of soil moisture and temperature. It is recommended that the project of changing sloping fields into terraced fields should be popularized in the loess hilly region to effectively utilize limited natural precipitation. In order to prevent the jujube orchard from degenerating and dying due to long-term drought and water shortage, effective water management measures need to be taken to achieve the sustainable development of dry farming jujube orchard.

Description: Electronic nose is a kind of widely-used artificial olfactory system for the detection and classification of volatile organic compounds. The high dimensionality of data collected by electronic noses can hinder the process of pattern recognition. Thus, the feature selection is an essential stage in building a robust and accurate model for gas recognition. This paper proposed an improved grey wolf optimizer (GWO) based algorithm for feature selection and applied it on electronic nose data for the first time. Two mechanisms are employed for the proposed algorithm. The first mechanism contains two novel binary transform approaches, which are used for searching feature subset from electronic nose data that maximizing the classification accuracy while minimizing the number of features. The second mechanism is based on the adaptive restart approach, which attempts to further enhance the search capability and stability of the algorithm. The proposed algorithm is compared with five efficient feature selection algorithms on three electronic nose data sets. Three classifiers and multiple assessment indicators are used to evaluate the performance of algorithm. The experimental results show that the proposed algorithm can effectively select the feature subsets that are conducive to gas recognition, which can improve the performance of the electronic nose.

Description: Sugars are important throughout a plant’s lifecycle. Monosaccharide transporters (MST) are essential sugar transporters that have been identified in many plants, but little is known about the evolution or functions of MST genes in rapeseed (Brassica napus). In this study, we identified 175 MST genes in B. napus, 87 in Brassica oleracea, and 83 in Brassica rapa. These genes were separated into the sugar transport protein (STP), polyol transporter (PLT), vacuolar glucose transporter (VGT), tonoplast monosaccharide transporter (TMT), inositol transporter (INT), plastidic glucose transporter (pGlcT), and ERD6-like subfamilies, respectively. Phylogenetic and syntenic analysis indicated that gene redundancy and gene elimination have commonly occurred in Brassica species during polyploidization. Changes in exon-intron structures during evolution likely resulted in the differences in coding regions, expression patterns, and functions seen among BnMST genes. In total, 31 differentially expressed genes (DEGs) were identified through RNA-seq among materials with high and low harvest index (HI) values, which were divided into two categories based on the qRT-PCR results, expressed more highly in source or sink organs. We finally identified four genes, including BnSTP5, BnSTP13, BnPLT5, and BnERD6-like14, which might be involved in monosaccharide uptake or unloading and further affect the HI of rapeseed. These findings provide fundamental information about MST genes in Brassica and reveal the importance of BnMST genes to high HI in B. napus.

Description: First-principles calculations were performed to investigate the effects of boron/nitrogen dopant on the geometry, electronic structure and magnetic properties of the penta-graphene system. It was found that the electronic band gap of penta-graphene could be tuned and varied between 1.88 and 2.12 eV depending on the type and location of the substitution. Moreover, the introduction of dopant could cause spin polarization and lead to the emergence of local magnetic moments. The main origin of the magnetic moment was analyzed and discussed by the examination of the spin-polarized charge density. Furthermore, the direction of charge transfer between the dopant and host atoms could be attributed to the competition between the charge polarization and the atomic electronegativity. Two charge-transfer mechanisms worked together to determine which atoms obtained electrons. These results provide the possibility of modifying penta-graphene by doping, making it suitable for future applications in the field of optoelectronic and magnetic devices.

Description: In this paper, a graphene/ITO nanorod metamaterial/U-bent-annealing (Gr/ITO-NM/U-bent-A)-based U-bent optical fiber local surface plasmon resonance (LSPR) sensor is presented and demonstrated for DNA detection. The proposed sensor, compared with other conventional sensors, exhibits higher sensitivity, lower cost, as well as better biological affinity and oxidize resistance. Besides, it has a structure of an original Indium Tin Oxides (ITO) nanocolumn array coated with graphene, allowing the sensor to exert significant bulk plasmon resonance effect. Moreover, for its discontinuous structure, a larger specific surface area is created to accommodate more biomolecules, thus maximizing the biological properties. The fabricated sensors exhibit great performance (690.7 nm/RIU) in alcohol solution testing. Furthermore, it also exhibits an excellent linear response (R2 = 0.998) to the target DNA with respective concentrations from 0.1 to 100 nM suggesting the promising medical applications of such sensors.

Description: Background: Polyphyllin VII (PP7), a steroidal saponin from Paris polyphylla, has been found to exert strong anticancer activity. Little is known about the anti-inflammatory property of PP7. In this study, the anti-inflammatory activity and its underlying mechanisms of PP7 were evaluated in lipopolysaccharide (LPS)-stimulated RAW264.7 cells and in multiple animal models. Methods: The content of nitric oxide (NO) was determined by spectrophotometry. The levels of prostaglandin E2 (PGE2) and cytokines were measured by enzyme-linked immunosorbent assay (ELISA) assay. The mRNA expression of pro-inflammatory genes was determined by qPCR. The total and phosphorylated protein levels were examined by Western blotting. The in vivo anti-inflammatory activities were evaluated by using mouse and zebrafish models. Results: PP7 reduced the production of NO and PGE2 and the protein and mRNA expressions of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) and enzymes (inducible NO synthase [iNOS], cyclooxygenase-2 [COX-2], and Matrix metalloproteinase-9 [MMP-9]) in LPS-induced RAW264.7 cells by suppressing the NF-κB and MAPKs pathways. Notably, PP7 markedly inhibited xylene-induced ear edema and cotton pellet-induced granuloma formation in mice and suppressed LPS and CuSO4-induced inflammation and toxicity in zebrafish embryos. Conclusion: This study demonstrates that PP7 exerts strong anti-inflammatory activities in multiple in vitro and in vivo models and suggests that PP7 is a potential novel therapeutic agent for inflammatory diseases.

Description: Protein-based nanoparticles, as an alternative to conventional polymer-based nanoparticles, offer great advantages in biomedical applications owing to their functional and biocompatible characteristics. However, the route of fabrication towards protein-based nanoparticles faces substantial challenges, including limitations in size control and unavoidable usage of toxic crosslinkers or organic solvents, which may raise safety concerns related to products and their degradation components. In the present study, a photo-induced crosslinking approach was developed to prepare stable, size-controlled protein-only nanoparticles. The facile one-step reaction irradiated by visible light enables the formation of monodispersed bovine serum albumin nanoparticles (BSA NPs) within several minutes through a tyrosine photo-redox reaction, requiring no cross-linking agents. The size of the BSA NPs could be precisely manipulated (from 20 to 100 nm) by controlling the duration time of illumination. The resultant BSA NPs exhibited spherical morphology, and the α-helix structure in BSA was preserved. Further study demonstrated that the 35 nm doxorubicin (DOX)-loaded BSA NPs achieved a drug loading content of 6.3%, encapsulation efficiency of 70.7%, and a controlled release profile with responsivity to both pH and reducing conditions. Importantly, the in vitro drug delivery experiment demonstrated efficient cellular internalizations of the DOX-loaded BSA NPs and inhibitory activities on MCF-7 and HeLa cells. This method shows the promise of being a platform for the green synthesis of protein-only nanoparticles for biomedical applications.