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The harsh climate and vast terrain of the Tibetan Plateau present significant challenges for large-scale remote sensing monitoring of grasslands, particularly in regions where the variation patterns of aboveground biomass (AGB) are influenced by high spatial complexity and pronounced environmental variability, further complicating the monitoring and estimation processes. To address this pressing issue, we propose a novel concept called the “grassland key belt,” which utilizes localized studies to reflect regional dynamics. Using AGB estimation in this key belt as a case study, we analyzed the gradient changes in AGB across different elevations. To achieve this, we used a bit-depth & residual quantization method for mining radiometric information from remote sensing images and integrated multimodal features, including remote sensing, terrain, and climate data, to accurately estimate AGB and generate an AGB distribution map. The experimental results demonstrate significant estimation accuracy, with an R2 of 0.8728 and an RMSE of 17.4799 g/m2. The study reveals an increasing trend in AGB as elevation decreases, with southern regions exhibiting higher AGB due to superior hydrothermal conditions. Notably, the southeastern grasslands display the highest AGB, consistent with previous studies, thus validating the reliability of the “grassland key belt” concept. This research advances the application of remote sensing technologies for monitoring vegetation dynamics in challenging environments, providing valuable insights for sustainable ecosystem management.

An accurate estimation of seasonal spatiotemporal dynamics of forage phosphorus (P) content in alpine grassland is crucial for effective grassland and livestock management. In this study, we integrated Sentinel-2 multispectral imagery (MSI) with computational hyperspectral features (CHSFs) and developed a novel symbolic regression algorithm based on deep reinforcement learning and genetic programming (DRL-GP) to estimate forage P content in alpine grasslands. Using 243 field observations collected during the regreening, grass-bearing, and yellowing periods in 2023 from the Shaliu River Basin, we generated 10 CHSF images (CHSFIs) with varying spectral dispersions (1–10 nm). Our results demonstrated the following: (1) The DRL-GP-based symbolic regression model identified the optimal CHSF and spectral dispersion for each growing season, significantly enhancing estimation accuracy. (2) Forage P content estimations using the combined CHSF and DRL-GP-based symbolic regression algorithm significantly outperformed traditional methods. Compared to original spectral features, the R2 improved by 99.5%, 57.4%, and 86.2% during the regreening, grass-bearing, and yellowing periods, with corresponding MSE reductions of 84.8%, 41.5%, and 75.8% and MAE decreases of 70.7%, 57.5%, and 50.4%. Across these growing seasons, the R2 increased by 322.2%, 68.2%, and 639.8% compared to MLR, 128.9%, 97.4%, and 469.2% compared to RF, and 485.1%, 65.3%, and 231.3% compared to DNN. The MSE decreased by 31%, 82.9%, and 52.4% compared to MLR, 39.9%, 42.4%, and 31.4% compared to RF, and 84.5%, 73.4%, and 81.9% compared to DNN. The MAE decreased by 32.6%, 67%, and 44.2% compared to MLR, 42.6%, 47.6%, and 37.9% compared to RF, and 60.2%, 50%, and 56.3% compared to DNN. (3) Proximity to the water system notably influenced forage P variation, with the highest increases observed within 1–2 km of water sources. These findings provide critical insights for optimizing grassland management and improving livestock productivity.

Salt stress is a significant abiotic factor affecting the growth and development of alfalfa. Malondialdehyde (MDA) serves as a critical biomarker for assessing alfalfa’s salt tolerance. Traditional methods for measuring MDA are often time-consuming and labor-intensive. Recent advances in remote sensing technology have made non-destructive estimation of metabolites feasible, positioning the accurate estimation of MDA content in alfalfa as a key focus in intelligent breeding. To address the challenge of detecting subtle changes in MDA content, this study developed a partial least squares regression (PLSR) model specifically for Medicago truncatula. This study utilized leaf reflectance hyperspectral data across the visible near-infrared–shortwave infrared (VIR-NIR-SWIR) spectrum, applying multi-order spectral transformation methods, including continuous wavelet transform (CWT), fractional differential (FD), and multi-granularity spectral segmentation (MGSS). Feature selection techniques, such as sequential forward selection (SFS), Least-Squares Boosting (LSBoost), and feature selection using neighborhood component analysis for regression (FSRNCA), were employed to enhance the efficiency of the MDA estimation. The findings revealed that the optimal PLSR model for MDA estimation was achieved by integrating CWT features across orders 1–30 with the SFS method. This model demonstrated robust estimation capabilities under varying salt stress conditions, significantly outperforming the original spectral data (R2 = 0.654, RMSE = 22.567 vs. R2 = 0.242, RMSE = 33.411). A comparative analysis of feature selection methods confirmed that SFS was the most effective for estimating MDA content in alfalfa. These results provide valuable insights and methodologies for MDA estimation and evaluating salt tolerance in alfalfa.

IntroductionThe drawbacks of using antibiotics as feed additives for blue foxes have gradually become apparent; moreover, thymol has wide-spectrum antimicrobial activity and has the potential to replace antibiotics in various animals. However, there are few reports on the effects of thymol on blue foxes.MethodsThis study aimed to investigate the effects of different concentrations of thymol on the growth performance, apparent nutrient digestibility, serum biochemical indicators, intestinal morphology, and gut microbiota of blue foxes. Twenty-four male blue foxes (120 ± 5 d) of similar weight (6.05 ± 0.16 kg) were randomly divided into 4 groups. 0, 100, 200, and 300 mg/kg thymol were added to the basal diets of groups C, L, M, and H, respectively.ResultsCompared with those in the C group, the addition of 100 mg/kg thymol to the diet significantly increased organic matter (OM) digestibility, crude protein (CP) digestibility, immunoglobulin (Ig) A, IgM, the VH of the duodenum, the CD of the jejunum, the VH of the ileum, and the VH/CD of the ileum (P < 0.05) and strongly significantly increased IgG (P < 0.01). The addition of 200 mg/kg thymol to the diet increased the VH/CD of the duodenum (P < 0.05). The addition of 300 mg/kg thymol to the diet significantly increased the VH and CD of the jejunum (P < 0.05). The addition of 200 mg/kg and 300 mg/kg thymol to the diets increased the final weight (FW) (P < 0.05). Adding 100 mg/kg thymol significantly increased the levels of interleukin-4 (IL-4) and catalase (CAT) compared with those in the other groups (P < 0.05). 16S rRNA gene detection revealed that thymol can change the abundances of Bifidobacterium, Fusobacterium, Allobaculum, Streptococcus, Megasphaera, and Lactobacillus in the gut.ConclusionThe addition of thymol to diets can increase the abundance of Bifidobacterium, Fusobacterium, and Allobaculum, which may contribute to improving the growth performance of blue foxes.

Remote sensing images (RSIs) are widely used in various fields due to their versatility, accuracy, and capacity for earth observation. Direct application of RSIs to harvest optimal results is generally difficult, especially for weak information features in the images. Thus, extracting the weak information in RSIs is reasonable to promote further applications. However, the current techniques for weak information extraction mainly focus on spectral features in hyperspectral images (HSIs), and a universal weak information extraction technology for RSI is lacking. Therefore, this study focused on mining the weak information from RSIs and proposed the deep multi-order spatial–spectral residual feature extractor (DMSRE). The DMSRE considers the global information and three-dimensional cube structures by combining low-rank representation, high-order residual quantization, and multi-granularity spectral segmentation theories. This extractor obtains spatial–spectral features from two derived sequences (deep spatial–spectral residual feature (DMSR) and deep spatial–spectral coding feature (DMSC)), and three RSI datasets (i.e., Chikusei, ZY1-02D, and Pasture datasets) were employed to validate the DMSRE method. Comparative results of the weak information extraction-based classifications (including DMSR and DMSC) and the raw image-based classifications showed the following: (i) the DMSRs can improve the classification accuracy of individual classes in fine classification applications (e.g., Asphalt class in the Chikusei dataset, from 89.12% to 95.99%); (ii) the DMSC improved the overall accuracy in rough classification applications (from 92.07% to 92.78%); and (iii) the DMSC improved the overall accuracy in RGB classification applications (from 63.25% to 63.6%), whereas DMSR improved the classification accuracy of individual classes on the RGB image (e.g., Plantain classes in the Pasture dataset, from 32.49% to 39.86%). This study demonstrates the practicality and capability of the DMSRE method to promote target recognition on RSIs and presents an alternative technique for weak information mining on RSIs, indicating the potential to extend weak information-based applications of RSIs.

IntroductionAdding antibiotics to animal basal diets can improve growth and production performance. However, the use of antibiotics poses a potential threat to public health safety.MethodsThe study was conducted to investigate the effects of different levels of mannan oligosaccharides (MOS) on the fur quality, nutrient apparent digestibility, serum immunity, antioxidant status, intestinal morphology, and gut microbiota of fur-growing raccoon dogs. Divide 24 male raccoon dogs (120 ± 5 d) of similar weight (5.01 ± 0.52 kg) into 4 groups randomly. Add 0, 0.05, 0.1, and 0.2% MOS to the basal diets of groups C, L, M, and H, respectively.ResultsCompared to the C group, the addition of 0.05% and 0.1% MOS in the diet increased the apparent digestibility of crude protein (CP), Underfur length (UL), Guard hair length (GL), immunoglobulin A (IgA), immunoglobulin G (IgG), and immunoglobulin M (IgM) levels in the serum (p

Rapid, non-destructive and automated salt tolerance evaluation is particularly important for screening salt-tolerant germplasm of alfalfa. Traditional evaluation of salt tolerance is mostly based on phenotypic traits obtained by some broken ways, which is time-consuming and difficult to meet the needs of large-scale breeding screening. Therefore, this paper proposed a non-contact and non-destructive multi-index fuzzy comprehensive evaluation model for evaluating the salt tolerance of alfalfa from Light Detection and Ranging data (LiDAR) and HyperSpectral Image data (HSI). Firstly, the structural traits related to growth status were extracted from the LiDAR data of alfalfa, and the spectral traits representing the physical and chemical characteristics were extracted from HSI data. In this paper, these phenotypic traits obtained automatically by computation were called Computing Phenotypic Traits (CPT). Subsequently, the multi-index fuzzy evaluation system of alfalfa salt tolerance was constructed by CPT, and according to the fuzzy mathematics theory, a multi-index Fuzzy Comprehensive Evaluation model with information Entropy of alfalfa salt tolerance (FCE-E) was proposed, which comprehensively evaluated the salt tolerance of alfalfa from the aspects of growth structure, physiology and biochemistry. Finally, comparative experiments showed that: (1) The multi-index FCE-E model based on the CPT was proposed in this paper, which could find more salt-sensitive information than the evaluation method based on the measured Typical Phenotypic Traits (TPT) such as fresh weight, dry weight, water content and chlorophyll. The two evaluation results had 66.67% consistent results, indicating that the multi-index FCE-E model integrates more information about alfalfa and more comprehensive evaluation. (2) On the basis of the CPT, the results of the multi-index FCE-E method were basically consistent with those of Principal Component Analysis (PCA), indicating that the multi-index FCE-E model could accurately evaluate the salt tolerance of alfalfa. Three highly salt-tolerant alfalfa varieties and two highly salt-susceptible alfalfa varieties were screened by the multi-index FCE-E method. The multi-index FCE-E method provides a new method for non-contact non-destructive evaluation of salt tolerance of alfalfa.

In this work, corn stalk nano-cellulose and corn stalk starch were used as film-forming substrates to prepare corn stalk nano-cellulose-starch film by blending-flow casting method. The properties of nano-cellulose-starch film were determined on the basis of single factor and orthogonal test. The effects of each material of film-forming on mechanical properties, moisture permeability, light transmittance, water solubility and oxygen permeability of nano-cellulose-starch film were investigated. Finally, the optimal formula combination of film-forming liquid was determined as follows: The starch was 10.0% (W/V), nano-cellulose was 5% (W/V), sodium carboxymethyl cellulose was 1.6% (W/V) and glycerol was 2.3% (V/V). Under optimal conditions, the nano-cellulose-starch film has the best comprehensive effect. The performance indexes were measured, and the membrane thickness was (0.063±0.050) mm, tensile strength was 14.92 MPa, elongation at break was 64.75%, moisture permeability coefficient was 2.19×10−12 g·m/m2·s·Pa, light transmittance was 87.60%, hydrolysis time was 97.00 s, oxygen permeability was 2.75×10−14 cm3·cm/cm2·s·Pa.

Chlorophyll plays a critical role in assessing the photosynthetic capacity and health of grasslands. However, existing studies on the hyperspectral inversion of chlorophyll have mainly focused on field crops, leading to limited accuracy when applied to natural grasslands due to their complex canopy structures and species diversity. This study aims to address this challenge by extrapolating the measured leaf chlorophyll to the canopy level using the green vegetation coverage approach. Additionally, fractional-order derivative (FOD) methods are employed to enhance the sensitivity of hyperspectral data to chlorophyll. Several FOD spectral indices are developed to minimize interference from factors such as bare soil and hay, resulting in improved chlorophyll estimation accuracy. The study utilizes partial least squares regression (PLSR) and support vector machine regression (SVR) to construct inversion models based on full-band FOD, two-band FOD spectral indices, and their combination. Through comparative analysis, the optimal model for estimating grassland chlorophyll content is determined, yielding an R2 value of 0.808, RMSE value of 1.720, and RPD value of 2.347.

Abstract As a major epigenetic modification, DNA methylation plays an important role in coordinating plant responses to environmental changes. Methylation-sensitive amplified polymorphism (MSAP) technology was used in this study to investigate the epigenetic diversity of fifty japonica rice samples from five regions in Heilongjiang Province, China. In addition, the phenotypic indicators of japonica rice samples and the environmental conditions of the sampling sites were investigated and analysed. Based on the MSAP analysis technique, using eight pairs of selective primers, we identified a total of 551 amplified loci, of which 267 (48.5%) were classified as methylation loci. The methylation status and levels of the japonica rice genome in different regions differed significantly (p

The tree diameter at breast height (DBH) is one of the most important variables for monitoring the forest ecology. Mobile laser scanning (MLS), which has been widely applied in the forestry field, makes DBH measurement fast and convenient. However, there are many shrubs and deadwood in the neutral forest environment and the point clouds quality from MLS are easily affected by the environment which results in low single tree segmentation and DBH estimation accuracy. To improve the accuracy in a complex forest environment and low point cloud quality, we propose a relative density segmentation method for the single tree segmentation and DBH estimation method based on multi-height diameters for the DBH estimation. The relative density segmentation method calculates the relative density according to the ratio of density in two different scales, and segments the tree trunks by the higher relative density of trunk point clouds compared with their surroundings points. In the natural forest plot, the precision and recall of the proposed segmentation method reached 0.966 and 0.946, respectively; In the urban forest plot, the precision and recall reached 1 and 0.966, respectively. The proposed DBH estimation method was used to estimate the DBH of trees using multi-height diameters. The multi-height diameters combined with the outlier detection algorithm were able to improve the accuracy and robustness when the trunk point clouds have large noise. For the DBH estimation results, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 2.5 cm, 11.54%, and 3.17 cm, respectively, in the natural forest plot and 1.65 cm, 6.31%, and 1.97 cm, respectively, in the urban forest plot. The good experiment results indicate that the proposed method can achieve accurate and robust DBH extraction and provide fundamental data for supervision and sustainable development of forest resources.

Abstract Backgrounds The incidence of angiostrongyliasis is increasing in recent decades due to the expanding endemic areas all over the world. Clinicians face tremendous challenge of diagnosing angiostrongyliasis because of the lack of awareness of the disease and less effective definitive laboratory tests. Case presentation A 27-year-old man initially manifested skin itching, emesis, myalgia and quadriparesis. With progressive weakness of four limbs and elevated protein in the cerebrospinal fluid (CSF), he was diagnosed as Guillain-Barré syndrome and treated with intravenous methylprednisolone and immunoglobulin. However, the patient deteriorated with hyperpyrexia, headache and then persistent coma. The routine tests for Angiostrongylus cantonensis (A. cantonensis) with both the CSF and the serum were all negative. In contrast, the metagenomic next-generation sequencing (mNGS) was applied with the serum sample and the CSF sample in the middle phase. The central nervous system (CNS) angiostrongyliasis was diagnosed by mNGS with the mid-phase CSF, but not the mid-phase serum. At the same time, the CSF analysis revealed eosinophils ratio up to 67%. The discovery of A. cantonensis was confirmed by PCR with CSF later. Unfortunately, the patient died of severe angiostrongyliasis. During his hospitalization, mNGS was carried out repeatedly after definitive diagnosis and targeted treatment. The DNA strictly map reads number of A. cantonensis detected by mNGS was positively correlated with the CSF opening pressure and clinical manifestations. Conclusions The case of A. cantonensis infection highlights the benefit of mNGS as a target-free identification in disclosing the rare CNS angiostrongyliasis in the unusual season, while solid evidence from routine clinical testing was absent. The appropriate sample of mNGS should be chosen according to the life cycle of A. cantonensis. Besides, given the fact that the DNA reads number of A. cantonensis fluctuated with CSF opening pressure and clinical manifestations, whether mNGS could be applied as a marker of effectiveness of treatment is worth further exploration.

Studies have shown that inhibiting inflammation and regulating intestinal microflora imbalance is a significant factor in controlling the development of type 2 diabetes mellitus (T2DM). This experiment studied the protective effect of polyphenol extract from germinated mung beans on diabetic C57BL/6 mice.ResultsFasting blood glucose (FBG) was decreased, glucose tolerance was increased, insulin resistance was decreased, serum lipid indexes in T2DM mice were improved, and the enzyme activities of alanine aminotransferase (ALT) and aspartate transaminase (AST) in serum were reduced. Meanwhile, the levels of interleukin 6 (IL-6), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) in serum were decreased, the concentration of interleukin 10 (IL-10) in serum was increased, inhibiting the inflammatory reaction induced by diabetes and repairing the morphology of mice liver tissue. At the same time, germinated mung bean polyphenol (GMP) can regulate the main intestinal flora, Firmicutes, Bacteroidetes, and Proteobacteria in diabetic mice and can also regulate species diversity and improve intestinal flora imbalance. Taken together, the experimental conclusion is a certain dose of polyphenol extract from germinated mung beans that can improve mouse T2DM by inhibiting inflammatory reaction and regulating intestinal microflora.

ObjectivesIn this study, the effects of γ-aminobutyric acid (GABA)-rich sprouted adzuki beans on the glycolipid metabolism and gastrointestinal health were investigated in mice with type 2 diabetes mellitus (T2DM).MethodsMice with T2DM were subjected to dietary intervention with different doses of GABA-rich sprouted adzuki beans for 6 consecutive weeks, during which growth indicators, glycolipid metabolism, and the composition and diversity of the gut microbiota changes were observed.ResultsA high dietary intake of GABA-rich sprouted adzuki beans had a preventive effect against weight gain, significantly reduced serum levels of FBG, TG, and TC. Additionally, high dietary intake of GABA-rich sprouted adzuki beans increased the abundances of Firmicutes, Bacteroidetes, Verrucomicrobia, and Akkermansia, leading to a shift in the structure of the gut microbiota toward the dominance of probiotics with regulatory effects on glycolipid metabolism.ConclusionsGABA-rich sprouted adzuki beans can effectively control the bodyweight of mice with T2DM, maintain a balanced blood glucose level, improve glycolipid metabolism and the changes in the microbiota may mediate the anti-diabetic effect of sprouted adzuki beans.

Fresh rice (Oryza sativa ssp. japonica) at the medium-milk stage is rich in soluble dietary fiber (SDF), leading to potential effects on type 2 diabetes (T2D). In this study, we analyzed each monosaccharide, the relative molecular weight, and the molecular linkages of fresh rice (grain, stem, husk, and leaf) SDF. The examined fresh rice leaf SDF consisted of 31.9% glucose, 35.7% galactose, 15.3% arabinose, 11.7% mannose, and a small amount of rhamnose and xylose. The molecular weight (Mw) was 232.5±19.1×104 u, and the radius of gyration (Rg) was 298.8±25.4 nm. The glycosidic bonds consisted mainly of Araf and Glc/Gal and included 1,3-Araf, 1,5-Araf, and 3,5-Araf glycosidic bonds. The effect of fresh rice leaf SDF on insulin resistance (IR) in HepG2 cells showed that it could significantly enhance glucose consumption (P

Type 2 diabetes mellitus (T2DM) is one of the most common metabolic diseases, and exploring strategies to prevent and treat diabetes has become extremely important. In recent decades the search for new therapeutic strategies for T2DM involving dietary interventions has attracted public attention. We established a diabetic mouse model by feeding mice a high-fat diet combined with injection of low-dose streptozotocin, intending to elucidate the effects and possible mechanisms of different dosages of γ-aminobutyric acid (GABA)-rich germinated adzuki beans on the treatment of diabetes in mice. The mice were treated for 6 weeks either with increasing doses of GABA-enriched germinated adzuki beans, with non-germinated adzuki beans, with GABA, or with the positive control drug metformin. Then, the blood glucose levels and blood lipid biochemical indicators of all the mice were measured. At the same time, serum differential metabolite interactions were explored by UPLC-Q/TOF-MS-based serum metabolomic analysis. The results showed that body weight and fasting blood glucose levels were significantly reduced (P < 0.05). We also report improved levels of total cholesterol, triglycerides, aspartate aminotransferase, alanine aminotransferase, urea, and serum creatinine. We observed a significant improvement in the homeostasis model assessment of the beta cell function and insulin resistance (HOMA-β and HOMA-IR) scores (P < 0.05) in the group of mice treated with the highest dose of GABA-enriched germinated adzuki beans. In addition, the metabolic profiles of the serum were analyzed, and 31 differential metabolites including amino acids and lipids were obtained. According to the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, this was found to be correlated with nine significantly enriched metabolic pathways involving the up-regulation of levels of L-serine, SM (d18:1/22:1(13Z)), L-histidine, creatine, and 3-indoleacetic acid. Our data suggest that the hypoglycemic effect of GABA-enriched germinated adzuki beans on diabetic mice may be related to improving tryptophan metabolism, glycerol phospholipid metabolism, sphingosline metabolism, and the glycine, serine, and threonine metabolic pathways. This study provides a reference for the application of GABA-enriched germinated foods in type 2 diabetes and could provide a cue for searching biomarkers to be adopted for T2DM diagnosis.

This study aimed to investigate the optimal hypoxic and monosodium glutamate (MSG) stress conditions for the enrichment of γ-Aminobutyric acid (GABA) in germinating adzuki beans and to reveal the potential underlying molecular mechanisms of GABA accumulation. Using single-factor experiments and response surface model, we investigated the effects of germination time, germination temperature, vacuum time, and MSG concentration on GABA contents, and further explored the activity and gene expression of glutamate decarboxylase (GAD) and polyamine oxidase (PAO) critical rate restriction enzymes during GABA synthesis. The optimal soaking temperature, soaking time, and pH conditions were 35°C, 16 h, and 5, respectively. Furthermore, the optimal germination conditions for optimal GABA enrichment were 48 h, 1.99 mg/ml MSG concentration, germination temperature of 31.49°C, and vacuum time of 15.83 h. Under such conditions, the predicted GABA concentration was 443.57 ± 7.18 mg/100 g, with no significant difference between the predicted and experimental data. The vacuum + MSG (FZM) treatment has a maximum contribution rate of GABA to 38.29%, which significantly increase GABA content, and the increase was associated with increased GAD and PAO activity. In addition, MSG in combination with vacuum treatment could significantly induce VaGAD4 and VaGAD6 genes in 2 days germination of adzuki beans. According to the results of the present study, vacuum + MSG treatment is an effective approach to enhancing GABA accumulation in germinating adzuki beans, which could be employed in enhancing the functional quality of germinating adzuki beans.

Sika deer velvet antler is the most important animal nutraceutic in traditional Chinese medicine. Reducing the breeding cost of sika deer by looking for a low-cost diet is the main research direction at present. The purpose of this experiment was to find an alternative diet for sika deer and reduce the cost of the diet by using spent mushroom substrate (SMS) as a concentrate supplement. The apparent digestibility for sika doe and the hematological changes of sika doe and suckling fawn were measured by replacing 10% of the concentrate supplement with SMS of Pleurotus ostreatus (SMS-MP). Compared with the control group, the digestibility of dry matter (DM), total protein (TP), globulin (GLO), and cholesterol (CHOL) of sika doe were significantly decreased (p < 0.05), and glucose (GLU), alanine (Ala), phenylalanine (Phe), and proline (Pro) of sika doe were significantly increased (p < 0.05) after the replacement of SMS-MP. Compared with the control group, the serum GLU of suckling fawn was significantly decreased (p < 0.05) and the phosphatase (ALP) was significantly increased after the replacement of SMS-MP (p < 0.05). There were no significant differences in the immune globulin and amino acid of suckling fawns between the two groups (p > 0.05). The present findings confirm the applicability of SMS-MP as a sika doe concentrate supplement. At the same time, using SMS, a waste resource, can not only reduce the breeding cost of sika doe, but also make full use of SMS to reduce environmental pollution.

This paper proposes an endmember matrix constraint unmixing method for ZY-1 02D hyperspectral imagery (HSI) super-resolution reconstruction (SRR) to overcome the low resolution of ZY-1 02D HSI. The proposed method combines spectral unmixing and adds novel smoothing constraints to traditional non-negative matrix factorization to improve details and preserve the spectral information of traditional SRR methods. The full utilization of the endmember spectral matrix and endmember abundance matrix of HSI and multispectral imagery (MSI) reconstructs the high spatial resolution and high spectral fidelity HSI. Furthermore, given the ZY-1 02D HSI infrared bands are seriously corrupted by noise, the influence of denoising on the SRR accuracy is also discussed. Experiments show that the proposed method restores spatial details and spectral information and is robust for noise, preserving more spectral information. Therefore, the proposed method is a ZY-1 02D HSI SRR method with high spatial resolution and high spectral fidelity, which improves the spatial resolution while simultaneously solving spectral mixing and provides the possibility for the data further expansion.

The survival and longevity of architectural heritage, especially structures in isolated locations such as ancient opera stages, have been gradually threatened by natural and sociocultural changes over the past few decades. Furthermore, the future of these heritage sites remains uncertain. Therefore, in this study, we aimed to develop a research method, using multidisciplinary knowledge and digital technology, to digitally protect and inherit the endangered heritage of ancient stages. Hence, in this study, we applied a qualitative longitudinal method for digital protection, digital culture inheritance, and data intelligence management. Particularly, digital culture inheritance established the knowledge visualization model, which was used to excavate cultural connotations and was displayed with virtual reality, augmented reality, etc. Furthermore, a GIS digital platform was developed to provide data management and a virtual experience in a 3D-interactive way for culture presentation, cognition, and heritage. Then, the developed method was validated over four years by using the ancient opera stages in Shanxi, China, as case studies. The results demonstrate that this method is feasible in and applicable to the heritages of ancient stages and can be applied to other architectural heritages and even cultural heritages. This method also helps to promote the public’s awareness of the protection of cultural heritage, especially the heritages of ancient stages.

At present, spent mushroom substrate (SMS) is a waste resource that is producing a pollution problem in China, and which has some use as animal feed or fertilizer, has not been assessed as a feed for deer. The purpose of this study is to expand the feed of male sika deer and reduce the feeding cost by using the waste resource of SMS. The 10% concentrated supplement was replaced with SMS and the feed intake, apparent digestibility, blood index and velvet production of male sika deer were measured. As the results showed, compared to the control group, the substitution of SMS for 10% of the concentrate supplement decreased the concentration of IgA (p < 0.01), replacing 10% concentrated supplement with SMS of Pleurotus ostreatus (SMS-MP) reduced the intake of organic matter (OMI) and improved the digestibility of ether extract (EE), while replacing 10% concentrated supplement with SMS of Flammulina velutipes (SMS-MF) had no effect on apparent nutrient digestibility, feed intake, velvet antler production, and biochemical indexes. In conclusion, SMS had no effect on serum biochemical indexes and the ratio of the feed weight of the deer supplement to the weight of velvet antler (p > 0.05). At the same time, SMS could reduce the feed consumption and improve the economy by using SMS as a waste resource.

The large volume and windward area of the heavy-duty semi-rigid airship (HSA) result in a large turning radius when the HSA passes through every mission point. In this study, a multi-mission-point route planning method for HSA based on the genetic algorithm and greedy strategy is proposed to direct the HSA maneuver through every mission point along the optimal route. Firstly, according to the minimum flight speed and the maximum turning slope angle of the HSA during turning, the minimum turning radius of the HSA near each mission point is determined. Secondly, the genetic algorithm is used to determine the optimal flight sequence of the HSA from the take-off point through all the mission points to the landing point. Thirdly, based on the optimal flight sequence, the shortest route between every two adjacent mission points is obtained by using the route planning method based on the greedy strategy. By determining the optimal flight sequence and the shortest route, the optimal route for the HSA to pass through all mission points can be obtained. The experimental results show that the method proposed in this study can generate the optimal route with various conditions of the mission points using simulation studies. This method reduces the total voyage distance of the optimal route by 18.60% on average and improves the flight efficiency of the HSA.

The purpose of this experiment is to expand the feed of growing sika deer and to explore the effects on growing sika deer of the spent mushroom substrate of Pleurotus ostreatus (SMS-MP). Twelve immature female growing sika deer were randomly assigned to four groups. The ratios of SMS-MP to replace concentration supplements were 0%, 10%, 20%, and 30%, respectively, and the growth performance, feed intake and apparent digestibility, serum biochemical indexes, blood physiological indexes, serum immune globulin and plasma amino acid of growing sika deer were measured. The results of the current study confirmed the applicability of SMS-MP as a feed ingredient in growing sika deer diets. There was no significant change in growth performance and hematology of growing sika deer when the concentrate supplement was replaced with 10–20% SMS-MP. However, replacing 30% of concentrate supplements with SMS-MP in the growing sika deer diet resulted in significantly decreased Hb and HCT levels. It can be concluded that, as a waste resource, adding a small amount of SMS-MP has no significant effect on the growth of sika deer, and at the same time can reduce the consumption of concentrate supplements, thereby improving the economic benefits of sika deer breeding.

Estimating the carbon (C), nitrogen (N), and phosphorus (P) contents of a large-span grassland transect is essential for evaluating ecosystem functioning and monitoring biogeochemical cycles. However, the field measurements are scattered, such that they cannot indicate the continuous gradient change in the grassland transect. Although remote sensing methods have been applied for the estimation of nutrient elements at the local scale in recent years, few studies have considered the effective estimation of C, N, and P contents over large-span grassland transects with complex environment including a variety of grassland types (i.e., meadow, typical grassland, and desert grassland). In this paper, an information enhancement algorithm (involving spectral enhancement, regional enhancement, and feature enhancement) is used to extract the weak information related to C, N, and P. First, the spectral simulation algorithm is used to enhance the spectral information of Sentinel-2 imagery. Then, the enhanced spectra and meteorological data are fused to express regional characteristics and the fractional differential (FD) algorithm is used to extract sensitive spectral features related to C, N, and P, in order to construct a partial least-squares regression (PLSR) model. Finally, the C, N, and P contents are estimated over a West–East grassland transect in Inner Mongolia, China. The results demonstrate that: (i) the contents of C, N, and P in large-span transects can be effectively estimated through use of the information enhancement method involving spectral enhancement, regional feature enhancement, and information enhancement, for which the estimation accuracies (R2) were 0.88, 0.78, and 0.85, respectively. Compared with the estimation results of raw Sentinel-2 imagery, the RMSE was reduced by 3.42 g/m2, 0.14 g/m2, and 13.73 mg/m2, respectively; and (ii) the continuous change trend and spatial distribution characteristics of C, N, and P contents in the west–east transect of the Inner Mongolia Plateau were obtained, which showed decreasing trends in C, N, and P contents from east to west and the characteristics of meadow > typical grassland > desert grassland. Thus, the information enhancement algorithm can help to improve estimates of C, N, and P contents when considering large-span grassland transects.

Monitoring grassland vegetation growth is of vital importance to scientific grazing and grassland management. People expect to be able to use a portable device, like a mobile phone, to monitor grassland vegetation growth at any time. In this paper, we propose a handheld grassland vegetation monitoring system to achieve the goal of monitoring grassland vegetation growth. The system includes two parts: the hardware unit is a hand-held multispectral imaging tool named ASQ-Discover based on a smartphone, which has six bands (wavelengths)—including three visible bands (450 nm, 550 nm, 650 nm), a red-edge band (750 nm), and two near-infrared bands (850 nm, 960 nm). The imagery data of each band has a size of 5120 × 3840 pixels with 8-bit depth. The software unit improves image quality through vignetting removal, radiometric calibration, and misalignment correction and estimates and analyzes spectral traits of grassland vegetation (Fresh Grass Ratio (FGR), NDVI, NDRE, BNDVI, GNDVI, OSAVI and TGI) that are indicators of vegetation growth in grassland. We introduce the hardware and software unit in detail, and we also experiment in five pastures located in Haiyan County, Qinghai Province. Our experimental results show that the handheld grassland vegetation growth monitoring system has the potential to revolutionize the grassland monitoring that operators can conduct when using a hand-held tool to achieve the tasks of grassland vegetation growth monitoring.

Cell-based therapeutic strategies afford major potential advantages in the repair of injured tendons. Generation of induced pluripotent stem cells (iPSCs) expands cell sources for “regenerative” therapy. However, its application in tendon repair is still limited and the effects remain unclear. In this study, equine tenocyte-derived iPSCs (teno-iPSCs) were generated by expressing four Yamanaka factors. Compared to parental tenocytes and bone marrow derived mesenchymal stem cells (BMSCs), the transcriptional activities of lineage-specific genes, including Mkx, Col1A2, Col14, DCN, ELN, FMOD, and TNC, were highly repressed in the resulting teno-iPSCs. Exposure to cyclic uniaxial mechanical loading increased the expression of Scx, Egr1, Col1A2, DCN, and TNC in teno-iPSCs and the expression of Scx, Egr1, DCN, and TNC in BMSCs. Reintroduction of tenogenic transcription factor Mohawk (Mkx) upregulated the expression of DCN in teno-iPSCs and the expression of Scx, Col14, and FMOD in BMSCs. Mechanical loading combined with ectopic expression of equine Mkx further enhanced the expression of Egr1, Col1A2, DCN, and TNC in teno-iPSCs and the expression of Scx, Egr1, and TNC in BMSCs. These data suggest that the repressed lineage-specific genes in the teno-iPSCs can be re-activated by mechanical loading and ectopic expression of Mkx. Our findings offer new insights into the application of iPSCs for basic and clinic research in tendon repair. Keywords: Tenogenic differentiation, Induced pluripotent stem cell (iPSC), Bone marrow mesenchymal stem cell (BMSC), Mechanical loading, Mohawk

Background. Chronic migraine with medication overuse headache (CM-MOH) is the most common type of chronic migraine, and it increases risk of stroke and white matter lesions. These pathologic changes could induce cognitive decline. However, the alteration of cognitive function in CM-MOH patients is not established. Therefore, we took this study to reveal the cognitive performances in CM-MOH. Methods. This cross-sectional study was conducted between December 2015 and January 2017. Patients were divided into CM-MOH, CMwoMOH (chronic migraine without medication overuse), and MO (migraine without aura) groups. Cognitive function was assessed in all cases during interictal periods using Addenbrooke’s Cognitive Examination Test (ACE-R), Trail Making Test A/B (TMT A/B), and Digit Symbol Test (DST). Detailed headache characteristics and evaluation of anxiety, depression, and living and sleep quality were collected. Results. 116 patients were included in this study. There were 21 CM-MOHs, 20 CMwoMOHs, 35 MOs, and 40 controls. Age and education were the independent risk factors of cognitive decline (P0.05). Meanwhile, CM-MOH got significantly higher scores than MO in anxiety and depression, with poorer performances in sleep and life quality (P

An accurate assessment of the grassland aboveground biomass (AGB) is important for analyzing terrestrial ecosystem structures and functions, estimating grassland primary productivity, and monitoring climate change and carbon/nitrogen circulation on a global scale. Multispectral satellites with wide-width advantages, such as Sentinel-2, have become the inevitable choice for the large-scale monitoring of grassland biomass on regional and global scales. However, the spectral resolution of multispectral satellites is generally low, which limits the inversion accuracy of grassland AGB and restricts further application in large-scale grassland monitoring. For this reason, a satellite-scale simulated spectra method was proposed to enhance the spectral information of the Sentinel-2 data, and a simulated spectrum (SS) was constructed using this algorithm. Then, the raw spectrum (RS) of Sentinel-2 and the SS were used as data sources to calculate the vegetation indices (RS-VIs and SS-VIs, which represent vegetation indices calculated using RS and SS data, respectively), and the multi-granularity spectral segmentation algorithm (MGSS) was employed to extract spectral segmentation features (RS-SF and SS-SF, which represent segmentation features extracted by RS and SS data, respectively). Following this, these spectral features (RS-SF, SS-SF, RS-VIs, and SS-VIs) were used to estimate AGB by partial least-squares regression (PLSR) and multiple stepwise regression (MSR) models. Finally, the spatial distribution law and the reasons for the latitude zone of the Inner Mongolia Plateau were analyzed, based on precipitation, the average temperature, topography, etc. The conclusions are as follows. Firstly, the SS has more spectral information and its sensitivity to biomass is higher than the RS of Sentinel-2 in some bands, and the correlation between the SS-VIs and biomass is higher than that of the RS-VIs. Secondly, among the spectral features, the most accurate AGB estimation was obtained by SS-SF, which gave R2 = 0.95. The root mean square error (RMSE) was 10.86 g/m2 and the estimate accuracy (EA) was 82.84% in the MSR model. Additionally, RMSE = 10.89 g/m2 and EA = 82.78% in the PLSR model. Compared with the traditional estimation methods using RS and VI, R2 was increased by at least 0.2, RMSE was reduced by at least 14.08 g/m2, and EA was increased by 22.26%. Therefore, the simulated spectra method can help improve the estimation accuracy of AGB, and a new idea about regional and global large-scale biomass acquisition is provided.

To achieve the ability of associating continuous-time laser frames is of vital importance but challenging for hand-held or backpack simultaneous localization and mapping (SLAM). In this study, the complex associating and mapping problem is investigated and modeled as a multilayer optimization problem to realize low drift localization and point cloud map reconstruction without the assistance of the GNSS/INS navigation systems. 3D point clouds are aligned among consecutive frames, submaps, and closed-loop frames using the normal distributions transform (NDT) algorithm and the iterative closest point (ICP) algorithm. The ground points are extracted automatically, while the non-ground points are automatically segmented to different point clusters with some noise point clusters omitted before 3D point clouds are aligned. Through the three levels of interframe association, submap matching and closed-loop optimization, the continuous-time laser frames can be accurately associated to guarantee the consistency of 3D point cloud map. Finally, the proposed method was evaluated in different scenarios, the experimental results showed that the proposed method could not only achieve accurate mapping even in the complex scenes, but also successfully handle sparse laser frames well, which is critical for the scanners such as the new Velodyne VLP-16 scanner’s performance.

Tilting sampling is a novel sampling mode for achieving a higher resolution of hyperspectral imagery. However, most studies on the tilting image have only focused on a single band, which loses the features of hyperspectral imagery. This study focuses on the restoration of tilting hyperspectral imagery and the practicality of its results. First, we reduced the huge data of tilting hyperspectral imagery by the p-value sparse matrix band selection method (pSMBS). Then, we restored the reduced imagery by optimal reciprocal cell combined modulation transfer function (MTF) method. Next, we built the relationship between the restored tilting image and the original normal image. We employed the least square method to solve the calibration equation for each band. Finally, the calibrated tilting image and original normal image were both classified by the unsupervised classification method (K-means) to confirm the practicality of calibrated tilting images in remote sensing applications. The results of classification demonstrate the optimal reciprocal cell combined MTF method can effectively restore the tilting image and the calibrated tiling image can be used in remote sensing applications. The restored and calibrated tilting image has a higher resolution and better spectral fidelity.

The complexity of the single linear hyperspectral pushbroom imaging based on a high altitude airship (HAA) without a three-axis stabilized platform is much more than that based on the spaceborne and airborne. Due to the effects of air pressure, temperature and airflow, the large pitch and roll angles tend to appear frequently that create pushbroom images highly characterized with severe geometric distortions. Thus, the in-flight calibration procedure is not appropriate to apply to the single linear pushbroom sensors on HAA having no three-axis stabilized platform. In order to address this problem, a new ground-based boresight calibration method is proposed. Firstly, a coordinate’s transformation model is developed for direct georeferencing (DG) of the linear imaging sensor, and then the linear error equation is derived from it by using the Taylor expansion formula. Secondly, the boresight misalignments are worked out by using iterative least squares method with few ground control points (GCPs) and ground-based side-scanning experiments. The proposed method is demonstrated by three sets of experiments: (i) the stability and reliability of the method is verified through simulation-based experiments; (ii) the boresight calibration is performed using ground-based experiments; and (iii) the validation is done by applying on the orthorectification of the real hyperspectral pushbroom images from a HAA Earth observation payload system developed by our research team—“LanTianHao”. The test results show that the proposed boresight calibration approach significantly improves the quality of georeferencing by reducing the geometric distortions caused by boresight misalignments to the minimum level.

Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted “useful” data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.

This paper describes an airborne high resolution four-camera multispectral system which mainly consists of four identical monochrome cameras equipped with four interchangeable bandpass filters. For this multispectral system, an automatic multispectral data composing method was proposed. The homography registration model was chosen, and the scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) were used to generate matching points. For the difficult registration problem between visible band images and near-infrared band images in cases lacking manmade objects, we presented an effective method based on the structural characteristics of the system. Experiments show that our method can acquire high quality multispectral images and the band-to-band alignment error of the composed multiple spectral images is less than 2.5 pixels.

The material of disc cutters is important to full-face tunnel boring machines (TBM). In recent years, disc cutters were optimized and tested by many scholars all around the world. H13(4Cr5MoSiV1) steel is widely used due to its excellent properties, especially in TBM disc cutters. In this paper, H13 steel with optimized composition was prepared and heat treatment. The high temperature compression of H13 steel was conducted at the temperatures ranging from 100 °C to 700 °C, with strain rate at 0.01 s−1. The stress-strain curves, Rockwell hardness and microstructure of H13 steel after compression were obtained and analyzed. The results showed that the compression strength and hardness decreased as the temperature increased; and the compression strength, hardness and ductility decreased rapidly between 600 °C and 700 °C, HR700 (the hardness of H13 steel at 700 °C) only reached 33.23 HRC. It is not recommended for TBM disc cutters to work in an environment over 600 °C.

The spatial resolution of a hyperspectral image is often coarse as the limitations on the imaging hardware. A novel super-resolution reconstruction algorithm for hyperspectral imagery (HSI) via adaptive projection onto convex sets and image blur metric (APOCS-BM) is proposed in this paper to solve these problems. Firstly, a no-reference image blur metric assessment method based on Gabor wavelet transform is utilized to obtain the blur metric of the low-resolution (LR) image. Then, the bound used in the APOCS is automatically calculated via LR image blur metric. Finally, the high-resolution (HR) image is reconstructed by the APOCS method. With the contribution of APOCS and image blur metric, the fixed bound problem in POCS is solved, and the image blur information is utilized during the reconstruction of HR image, which effectively enhances the spatial-spectral information and improves the reconstruction accuracy. The experimental results for the PaviaU, PaviaC and Jinyin Tan datasets indicate that the proposed method not only enhances the spatial resolution, but also preserves HSI spectral information well.

This study was designed to investigate whether telomerase was involved in the neuroprotective effect of curcumin and Cur1. Alzheimer's disease is a consequence of an imbalance between the generation and clearance of amyloid-beta peptide in the brain. In this study, we used Aβ1-42 (10 µg/ml) to establish a damaged cell model, and curcumin and Cur1 were used in treatment groups. We measured cell survival and cell growth, intracellular oxidative stress and hTERT expression. After RNA interference, the effects of curcumin and Cur1 on cells were verified. Exposure to Aβ1-42 resulted in significant oxidative stress and cell toxicity, and the expression of hTERT was significantly decreased. Curcumin and Cur1 both protected SK-N-SH cells from Aβ1-42 and up-regulated the expression of hTERT. Furthermore, Cur1 demonstrated stronger protective effects than curcumin. However, when telomerase was inhibited by TERT siRNA, the neuroprotection by curcumin and Cur1 were ceased. Our study indicated that the neuroprotective effects of curcumin and Cur1 depend on telomerase, and thus telomerase may be a target for therapeutic effects of curcumin and Cur1.