Pinus thunbergii Parl. Somatic Plants' Resistance to Bursaphelenchus xylophilus Depends on Pathogen-Induced Differential Transcriptomic Responses.

Pinus thunbergii Parl. is an economically and medicinally important plant, as well as a world-renowned horticultural species of the Pinus genus. Pine wilt disease is a dangerous condition that affects P. thunbergii. However, understanding of the genetics underlying resistance to this disease is poor. Our findings reveal that P. thunbergii's resistance mechanism is based on differential transcriptome responses generated by the early presence of the pathogen Bursaphelenchus xylophilus, also known as the pine wood nematode. A transcriptome analysis (RNA-seq) was performed to examine gene expression in shoot tissues from resistant and susceptible P. thunbergii trees. RNA samples were collected from the shoots of inoculated pines throughout the infection phases by the virulent Bursaphelenchus xylophilus AMA3 strain. The photosynthesis and plant-pathogen interaction pathways were significantly enriched in the first and third days after infection. Flavonoid biosynthesis was induced in response to late infestation (7 and 14 days post-infestation). Calmodulin, RBOH, HLC protein, RPS, PR1, and genes implicated in phytohormone crosstalk (e.g., SGT1, MYC2, PP2C, and ERF1) showed significant alterations between resistant and susceptible trees. Furthermore, salicylic acid was found to aid pine wood nematodes tolerate adverse conditions and boost reproduction, which may be significant for pine wood nematode colonization within pines. These findings provide new insights into how host defenses overcame pine wood nematode infection in the early stage, which could potentially contribute to the development of novel strategies for the control of pine wilt disease.

Remote Sensing Monitoring and Assessment of Global Vegetation Status and Changes during 2016-2020.

Vegetation plays a fundamental role within terrestrial ecosystems, serving as a cornerstone of their functionality. Presently, these crucial ecosystems face a myriad of threats, including deforestation, overgrazing, wildfires, and the impact of climate change. The implementation of remote sensing for monitoring the status and dynamics of vegetation ecosystems has emerged as an indispensable tool for advancing ecological research and effective resource management. This study takes a comprehensive approach by integrating ecosystem monitoring indicators and aligning them with the objectives of SDG15. We conducted a thorough analysis by leveraging global 500 m resolution products for vegetation Leaf Area Index (LAI) and land cover classification spanning the period from 2016 to 2020. This encompassed the calculation of annual average LAI, identification of anomalies, and evaluation of change rates, thereby enabling a comprehensive assessment of the global status and transformations occurring within major vegetation ecosystems. In 2020, a discernible rise in the annual Average LAI of major vegetation ecosystems on a global scale became evident when compared to data from 2016. Notably, the ecosystems demonstrating a slight increase in area constituted the largest proportion (34.23%), while those exhibiting a significant decrease were the least prevalent (6.09%). Within various regions, such as Eastern Europe, Central Africa, and South Asia, substantial increases in both forest ecosystem area and annual Average LAI were observed. Furthermore, Eastern Europe and Central America recorded significant expansions in both grassland ecosystem area and annual average LAI. Similarly, regions experiencing notable growth in both cropland ecosystem areas and annual average LAI encompassed Southern Africa, Northern Europe, and Eastern Africa.

Integrated vegetation cover of typical steppe in China based on mixed decomposing derived from high resolution remote sensing data.

Grasslands represent the largest ecosystem in China, accurate and efficient extraction of its integrated vegetation cover (IVC) plays a crucial role in supporting policy decisions. This study presented a method for grassland monitoring via IVC derived from high-resolution satellite data. Taking the multispectral data of Gaofen-1 (GF-1) and Gaofen-6 (GF-6) with 16 m resolution as the main data source, vegetation cover of six representative regions was assessed based on mixed-pixel decomposition model. Using grassland vegetation cover and ratio of grassland area, the IVC in each site was calculated and verified against ground-measured sample data. The results showed that the IVC of grassland was closely related to vegetation habitat driven by regional hydrothermal regime. Yichang grassland, dominated with warm-temperate shrub tussock type, had the highest IVC (80.06 %) due to its favorable hydrothermal conditions. For the main grassland types in Hulunbuir and Gansu Province (temperate meadow steppe and temperate typical steppe), the IVC was 79.38 % and 58.46 %, respectively. In both Xilin-Gol and Nagqu, vegetation cover decreased gradually from east to west, and the IVC was merely 42.83 % and 42.61 %, respectively. Both regions are endowed with less hydrothermal resources to different degrees. Alxa, with a predominately temperate desert landscape, had the lowest IVC of 15.58 % where precipitation is extremely scarce. Based on the grass species of measured samples, the dominant species and biodiversity of different grassland types in Gansu Province and Hulunbuir Municipality of Inner Mongolia Autonomous Region were analyzed. The results showed that the meadow grassland has the richest biodiversity. The temperate mountain meadows in Gansu Province have a high species diversity, with a total of 90 grass species, and the lowland meadows in Hulunbuir have a total of 49 grass species. This study utilizes high-resolution data to conduct large-scale vegetation monitoring, which is a viable alternative for efficient assessment of steppe ecology.

Assessment of Land Cover Status and Change in the World and "the Belt and Road" Region from 2016 to 2020.

The assessment of land cover and changes will help to understand the temporal and spatial pattern of land cover in the world and the Belt and Road (B&R) region, and provide reference information for global sustainable development and the Belt and Road construction. In this paper, the 1 km global land cover classification maps of 2016 and 2020 with a high accuracy of 88% are mapped using the Moderate Resolution Imaging Spectroradiometer (MODIS) time series surface reflectance products. Based on the maps, the land cover status of the world and the Belt and Road region, the land cover change from 2016 to 2020, and the mutual transformation characteristics between various types, are analyzed. The research results indicate that from 2016 to 2020, the global change rates of cropland, forest, grassland, and impervious surface are 0.25%, 0.22%, 0.08% and 3.41%, respectively. In the Belt and Road region, the change rates of cropland, forest, grassland, and impervious surface are 0.42%, 0.60%, -0.55% and 2.98% respectively. The assessment results will help to clarify the spatial pattern of land cover change in the five years from 2016 to 2020, so as to provide valuable scientific information for the global realization of sustainable development goals and the construction of the B&R.

Neuroprotective effect of engineered Clostridiumbutyricum-pMTL007-GLP-1 on Parkinson's disease mice models via promoting mitophagy.

Parkinson's disease (PD) is a common neurodegenerative disease with limited treatment and no cure, hence, broadening PD drug spectrum is of great significance. At present, engineered microorganisms are attracting increasing attention. In this study, we constructed an engineered strain of Clostridium butyricum-GLP-1, a C. butyricum (a probiotic) that consistently expresses glucagon-like peptide-1 (GLP-1, a peptide-based hormone with neurological advantage) in anticipation of its use in PD treatment. We further investigated the neuroprotective mechanism of C. butyricum-GLP-1 on PD mice models induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. The results indicated that C. butyricum-GLP-1 could improve motor dysfunction and ameliorate neuropathological changes by increasing TH expression and reducing the expression of α-syn. Moreover, we confirmed that C. butyricum-GLP-1 improved microbiome imbalance of PD mice by decreasing the relative abundance of Bifidobacterium at the genus level, improved gut integrity, and upregulated the levels of GPR41/43. Surprisingly, we found it could exert its neuroprotective effects via promoting PINK1/Parkin mediated mitophagy and attenuating oxidative stress. Together, our work showed that C. butyricum-GLP-1 improves PD by promoting mitophagy, which provides an alternative therapeutic modality for PD.

Effect of Psychological Intervention Combined with Dietary Guidance on Quality of Life and Long-Term Efficacy of Bushen Quyu Decoction in Treatment of Patients with Advanced Ovarian Cancer.

OBJECTIVE: To study the effects of psychological intervention combined with dietary guidance on the quality of life and long-term efficacy of Bushen Quyu Decoction in the treatment of patients with advanced ovarian cancer. METHODS: 220 patients with advanced (stages III to IV) ovarian cancer in our hospital from May 2015 to October 2018 were selected and randomly divided into a control group and an observation group, with 110 cases in each group. The patients in the control group received basic nursing care and treatment with Bushen Quyu Decoction, and the patients in the observation group were combined with psychological intervention and dietary guidance on the basis of the treatment of the patients in the control group. The clinical efficacy, nursing satisfaction, treatment compliance, quality of life, negative emotion comparison, and long-term efficacy of the two groups were compared. Moreover, the changes of immune function indexes and the content of tumor markers were compared between the two groups. RESULTS: The total effective rate of treatment in the observation group (64.55%) was higher than that in the control group (31.82%). The nursing satisfaction of the observation group was 94.55%, the nursing satisfaction of the control group was 84.55%, and the difference was statistically significant (p < 0.01). The treatment compliance of the observation group was 98.18%, the treatment compliance of the control group was 82.73%, and the difference was statistically significant (p < 0.0001). After nursing, the Anxiety Self-Rating Scale (SAS) score and Self-Rating Depression Scale (SDS) score of the two groups of patients were decreased (∗p < 0.05), and the score of the observation group decreased more significantly (Δ p < 0.05). After nursing, the scores of the two groups of patients in social/family status, physical function, physiological function, and emotional status increased (∗p < 0.05), and the observation group was significantly higher than the control group (Δ p < 0.05). After nursing, the CD3+, CD4+, CD4+/CD8+ levels of the observation group were significantly higher than the control group (p < 0.05). The CD8+ level of the observation group was significantly lower than the control group (p < 0.05). After nursing, the levels of tumor markers in the two groups were decreased (∗p < 0.05), and the observation group was downregulated more significantly than the control group (Δ p < 0.05). The two-year cumulative survival rate of the observation group was 78.18%, and the two-year cumulative survival rate of the control group was 54.55%. The observation group was significantly higher than the control group (p < 0.05). CONCLUSIONS: Psychological intervention combined with dietary guidance can significantly improve the quality of life and mental state of patients with advanced ovarian cancer, enhance the patient's immune function, reduce the serum tumor markers carcinoembryonic antigen (CEA) and carbohydrate antigen (CA199) levels, and improve survival rate and survival time, which has important clinical significance.

Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery.

Semantic segmentation for high-resolution remote-sensing imagery (HRRSI) has become increasingly popular in machine vision in recent years. Most of the state-of-the-art methods for semantic segmentation of HRRSI usually emphasize the strong learning ability of deep convolutional neural network to model the contextual relationship in the image, which takes too much consideration on every pixel in images and subsequently causes the problem of overlearning. Annotation errors and easily confused features can also lead to the confusion problem while using the pixel-based methods. Therefore, we propose a new semantic segmentation network-the region-enhancing network (RE-Net)-to emphasize the regional information instead of pixels to solve the above problems. RE-Net introduces the regional information into the base network, to enhance the regional integrity of images and thus reduce misclassification. Specifically, the regional context learning procedure (RCLP) can learn the context relationship from the perspective of regions. The region correcting procedure (RCP) uses the pixel aggregation feature to recalibrate the pixel features in each region. In addition, another simple intra-network multi-scale attention module is introduced to select features at different scales by the size of the region. A large number of comparative experiments on four different public datasets demonstrate that the proposed RE-Net performs better than most of the state-of-the-art ones.

Radiometric Performance Evaluation of FY-4A/AGRI Based on Aqua/MODIS.

Fengyun-4A (FY-4A) is the first satellite of the Chinese second-generation geostationary orbit meteorological satellites (FY-4). The Advanced Geostationary Radiation Imager (AGRI), onboard FY-4A does not load with high-precision calibration facility in visible and near infrared (VNIR) channel. As a consequence, it is necessary to comprehensively evaluate its radiometric performance and quantitatively describe the attenuation while using its VNIR data. In this paper, the radiometric performance at VNIR channels of FY-4A/AGRI is evaluated based on Aqua/MODIS data using the deep convective cloud (DCC) target. In order to reduce the influence of view angle and spectral response difference, the bi-directional reflectance distribution function (BRDF) correction and spectral matching have been performed. The evaluation result shows the radiometric performance of FY-4A/AGRI: (1) is less stable and with obvious fluctuations; (2) has a lower radiation level because of 24.99% lower compared with Aqua/MODIS; 3) has a high attenuation with 9.11% total attenuation over 2 years and 4.0% average annual attenuation rate. After the evaluation, relative radiometric normalization between AGRI and MODIS in VNIR channel is performed and the procedure is proved effective. This paper proposed a more reliable reference for the quantitative applications of FY-4A data.

Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy.

Soil organic carbon (SOC) is an important soil property that has profound impact on soil quality and plant growth. With 140 soil samples collected from Ebinur Lake Wetland National Nature Reserve, Xinjiang Uyghur Autonomous Region of China, this research evaluated the feasibility of visible/near infrared (VIS/NIR) spectroscopy data (350-2,500 nm) and simulated EO-1 Hyperion data to estimate SOC in arid wetland regions. Three machine learning algorithms including Ant Colony Optimization-interval Partial Least Squares (ACO-iPLS), Recursive Feature Elimination-Support Vector Machine (RF-SVM), and Random Forest (RF) were employed to select spectral features and further estimate SOC. Results indicated that the feature wavelengths pertaining to SOC were mainly within the ranges of 745-910 nm and 1,911-2,254 nm. The combination of RF-SVM and first derivative pre-processing produced the highest estimation accuracy with the optimal values of Rt (correlation coefficient of testing set), RMSE t and RPD of 0.91, 0.27% and 2.41, respectively. The simulated EO-1 Hyperion data combined with Support Vector Machine (SVM) based recursive feature elimination algorithm produced the most accurate estimate of SOC content. For the testing set, Rt was 0.79, RMSE t was 0.19%, and RPD was 1.61. This practice provides an efficient, low-cost approach with potentially high accuracy to estimate SOC contents and hence supports better management and protection strategies for desert wetland ecosystems.

Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors.

The radiometric capability of on-orbit sensors should be updated on time due to changes induced by space environmental factors and instrument aging. Some sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have onboard calibrators, which enable real-time calibration. However, most Chinese remote sensing satellite sensors lack onboard calibrators. Their radiometric calibrations have been updated once a year based on a vicarious calibration procedure, which has affected the applications of the data. Therefore, a full evaluation of the sensors' radiometric capabilities is essential before quantitative applications can be made. In this study, a comprehensive procedure for evaluating the radiometric capability of several Chinese optical satellite sensors is proposed. In this procedure, long-term radiometric stability and radiometric accuracy are the two major indicators for radiometric evaluation. The radiometric temporal stability is analyzed by the tendency of long-term top-of-atmosphere (TOA) reflectance variation; the radiometric accuracy is determined by comparison with the TOA reflectance from MODIS after spectrally matching. Three Chinese sensors including the Charge-Coupled Device (CCD) camera onboard Huan Jing 1 satellite (HJ-1), as well as the Visible and Infrared Radiometer (VIRR) and Medium-Resolution Spectral Imager (MERSI) onboard the Feng Yun 3 satellite (FY-3) are evaluated in reflective bands based on this procedure. The results are reasonable, and thus can provide reliable reference for the sensors' application, and as such will promote the development of Chinese satellite data.

Mitochondrial Dysfunction and Ca2+ Overload in Injured Sertoli Cells Exposed to Bisphenol A.

Bisphenol-A (BPA) is well known as one of endocrine-disrupting chemicals and testicular toxicant. In this present study, we determined whether BPA caused cell injury through mitochondria impairment and ROS overproduction. The cellular ROS production, mitochondrial ATP synthetase activity and Ca2+ concentration were examined. We have found BPA caused the cellular mitochondria dysfunction and followed by cell death in Sertoli cells. Moreover cytoplasm Ca2+ overload was also involved. Furthermore, pretreatment with N-acetyl-L-cysteine (NAC) could alleviate the damage by causing a remarkable decrease in ROS production and mitochondrial dysfunction. Collectively, our results showed that BPA exposure induced Sertoli cell apoptosis because of excessive ROS generation and mitochondrial dysfunction. © 2016 Wiley Periodicals, Inc. Environ Toxicol 32: 823-831, 2017.

[Study on Estimation of Deserts Soil Total Phosphorus Content by Vis-NIR Spectra with Variable Selection].

In this paper, 300 samples of desert soil collected in the Ebinur Lake Wetland Nature Reserve of Xinjiang were used as the research subject, and the visible/near-infrared spectra data about the soil obtained with the ASD Field Spec 3 HR spectrometer and the data about total phosphorus in the soil obtained through chemical analysis were used as the data sources; following Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment, the combination of ant colony optimization interval partial least squares (ACO-iPLS) and genetic algorithm interval partial least squares (GA-iPLS) were employed to extract the characteristic wavelengths of the total phosphorus content in desert soil, before the partial least squares regression model for predicting the total-phosphorus content in soil was constructed; and this model was compared with the full-spectrum partial least squares model, ACO-iPLS and GA-iPLS. According to the results: through filte- ring with ACO-iPLS, the total-phosphorus characteristic wavebands in the desert soil were 500-700, 1 101-1 300, 1 501-1 700, and 1 901-2 100 nm; through further variable selection with GA-iPLS, 13 effective wavelengths with the minimum colinearity were selected, which were respectively: 1621, 546, 1259, 573, 1572, 1527, 564, 1 186, 1 988, 1541, 2024, 1 118, and 1 191 nm. According to the comparison of modeling methods, the most accurate model was the one based on the characteristic variables selected with the combination of ACO-iPLS and GA-iPLS, followed by the ones with genetic algorithm, ant colony optimization algorithm and the full spectrum method. For the total phosphorus content in soil model established with the combination of ACO-iPLS and GA-iPLS, the root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were respectively 0.122 and 0.108 mg x g(-1), and the related coefficient for cross validation (R(c)) and the related coefficient for prediction (R(p)) were 0.535 7 and 0.555 9, respectively. Therefore, it can be seen that the model constructed through Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment and by using the combination of AGO-iPLS and GA-iPLS has simple structure, high prediction accuracy and good robustness, and can be used for estimating the total phosphorus content in desert soil.

[EEMD de-noising of reflecting spectrum in soil profiles].

In the present paper, based on the multi-resolution attribute of EEMD (ensemble empirical mode decomposition) method, we presented a new de-noising method for analyzing spectrum, and applied it to process the reflecting spectrum data of 33 soil profiles in the typical oasis located in the middle reaches of the Tarim River. To explore the de-noising effect of EEMD threshold method for reflecting spectrum in soil profiles; we compared EEMD threshold method with wavelet transform method. The results showed that compared with traditional wavelet transform method, the signal to noise ratio (SNR) was improved from 14. 8366 to 34. 2757 dB, and the root mean square error (RMSE) was reduced to 7. 2406 X 10(-6) from 6. 7861 X 10(-5) and the correlation coefficient (r) increased from 0. 9825 to 0. 9998. Therefore, three de-noising effect indicators of EEMD threshold method are better than those of wavelet transform method. This proved that the EEMD threshold method can effectively eliminate the noise of soil-profile spectrum and also preserve the detailed information of the original spectra well. Thus, the analysis precision of the spectrum will be improved. In addition, by contrast with the wavelet threshold method, the EEMD threshold method is adaptive and is fairly reliable. As a new method for spectral pretreatment, the EEMD threshold method will have a good application prospect in spectra de-noising.

Quality control of tuirejieduling granules using high-performance liquid chromatography fingerprint method and simultaneous determination of four main active ingredients.

BACKGROUND: The Tuirejieduling granule is a compound preparation made from four kinds of Chinese medicines. It is effective for anti-inflammation, antivirus, defervescence and anti-bacterium; however, its quality control standards have remained unknown. OBJECTIVE: To establish a simple and accurate fingerprint method for quality control of the Tuirejieduling granule. MATERIALS AND METHODS: The methanol extract of the Tuirejieduling granule was used for the fingerprint analysis and the four selected active ingredients (epigoitrin, phillyrin, saikosaponin A and glycyrrhetinic acid) in the extract were determined. The fingerprint method was performed on an Amethyst C18-P chromatography column by gradient elution with acetonitrile and aqueous phase (containing 0.5% H3PO4 (v/v), pH 3.0). RESULTS: Under the optimal chromatographic condition, twenty peaks were chosen as fingerprint peaks of the Tuirejieduling granules extractions. The similarities of 10 batches of Tuirejieduling granule was more than 0.99. This indicates that the different batches of Tuirejieduling granules were under the consistent quality control. Good linear behaviors over the investigated concentration ranges were obtained with the values of R (2) higher than 0.99 for four studied active ingredients. The recoveries for spiked samples were in the range of 96.2-105.5%. The developed method was successfully applied to determine the contents of active constituents in different batches of Tuirejieduling granule. CONCLUSION: The HPLC fingerprint was proved to be a reliable method for the quality control of Tuirejieduling granule.