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Leaf area index (LAI) and canopy chlorophyll content (CCC) are important indicators that describe the growth status and nitrogen deficiencies of crops. Several studies have been performed to estimate LAI and CCC using multispectral cameras onboard an unmanned airborne vehicle (UAV) system. However, the impacts of illuminations during UAV flight and problems of how to invert still need more investigation. UAV flights with a multispectral camera were performed under clear (diffuse ratio 0) and cloudy illumination conditions (diffuse ratio 1) over rapeseed, wheat and sunflower (only clear) fields. One-dimension radiative transfer model PROSAIL was run twice to generate a clear-sky model and a cloudy-sky model, respectively. The LAI and CCC of flights under a clear sky were inverted from the clear-sky model, and the flights under cloudy conditions were inverted from both clear-sky and cloudy-sky models to compare the results. Moreover, three Look-Up-Tables (LUT) were built with same input variables but different distributions of LAI. Results showed that LAI from uniform dense LUT had better correspondence with ground measurements for all crops (R2 = 0.51~0.69). The illumination condition had little impact on small to medium LAI (LAI < 5) and CCC. However, the inversion of imageries during cloudy sky conditions from the clear-sky model led to an overestimation of high LAI values.

Grasshoppers can swarm in the millions and destroy crops over wide areas, posing a major economic threat to agriculture. A wide range of insect-related viruses has recently been reported in the metagenomics of grasshoppers. Here, we identified and isolated a novel reovirus from grasshoppers, named Acrididae reovirus (ARV). The complete genome of ARV was composed of nine dsRNA segments. Phylogenetic analysis revealed that ARV formed a monophyletic lineage with unclassified insect-associated reoviruses and was sufficiently distinct from known genera of Reoviridae. ARV could replicate in its host Locusta migratoria and result in host death. Lower-dose ARV infection affected ovary development and resulted in a significant reduction in fecundity. The identification and characterization of a novel pathogenic reovirus could potentially promote the development of new biological control agents.

We presented a theoretical study for the structural, mechanical, and thermophysical properties of the precipitates in 2xxx series aluminum alloy by applying the widely used density functional theory of Perdew-Burke-Ernzerhof (PBE). The results indicated that the most thermodynamically stable structure refers to the Al3Zr phase in regardless of its different polymorphs, while the formation enthalpy of Al5Cu2Mg8Si6 is only -0.02 eV (close to zero) indicating its metastable nature. The universal anisotropy index of AU follows the trend of: Al2Cu > Al2CuMg ≈ Al3Zr_D022 ≈ Al20Cu2Mn3 > Al3Fe ≈ Al6Mn > Al3Zr_D023 ≈ Al3Zr_L12 > Al7Cu2Fe > Al3Fe2Si. The thermal expansion coefficients (TECs) were calculated based on Quasi harmonic approximation (QHA); Al2CuMg shows the highest linear thermal expansion coefficient (LTEC), followed by Al3Fe, Al2Cu, Al3Zr_L12 and others, while Al3Zr_D022 is the lowest one. The calculated data of three Al3Zr polymorphs follow the order of L12 > D023 > D022, all of them show much lower LTEC than Al substance. For multi-phase aluminum alloys, when the expansion coefficient of the precipitates is quite different from the matrix, it may cause a relatively large internal stress, or even produce cracks under actual service conditions. Therefore, it is necessary to discuss the heat misfit degree during the material design. The discrepancy between a-Al and Al2CuMg is the smallest, which may decrease the heat misfit degree between them and improve the thermal shock resistant behaviors.

Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the relative ability of DAP and ALS in predicting canopy structural variables (i.e., canopy cover and leaf area index (LAI)) has not been sufficiently investigated by previous studies. In this study, we comprehensively compared the canopy cover and LAI estimates using DAP- and ALS-based methods over 166 selected tropical forest sample plots with seven different tree species and forest types. We also explored the relationship between field-measured aboveground biomass (AGB) and the LAI estimates. The airborne LAI estimates were subsequently compared with the Sentinel-2-based LAI values that were retrieved using a one-dimensional radiative transfer model. The results demonstrated that the DAP-based method generally overestimated the two canopy variables compared to ALS-based methods but with relatively high correlations regardless of forest type and species (R2 of 0.80 for canopy cover and R2 of 0.76 for LAI). Under different forest types and species, the R2 of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively. Apparently, different correlations between AGB and LAI were found for different forest types and species where the mixed coniferous and broad-leaved forest shows the best correlation with R2 larger than 0.70 for both methods. The comparison with satellite retrievals verified that the ALS-based estimates are more consistent with Sentinel-2-based estimates than DAP-based estimates. We concluded that DAP data failed to provide analogous results to ALS data for canopy variable estimation in tropical forests.

To avoid the widespread resistance of commercial fungicides, new broad-spectrum botanical fungicides need to be developed. In previous bioactive screening assays, extracts of Artemisia ordosica Krasch. (A. ordosica) had highly antifungal activities, but the responsible phytochemicals were unidentified. In this study, active compounds of A. ordosica extracts were identified using a bioassay-guided method, and antifungal assays were performed in vitro and in vivo. The bioactive compounds were dissolved in petroleum ether, and the best antifungal fraction contained four compounds: trans-dehydromatricaria ester (TDDE), 7, 4-demetylnringenin, capillarin, and stearic acid. Among them, TDDE exhibited the highest antifungal activity against six pathogenic fungi and five bacteria. It exhibited significant fungicidal activity against Thanatephorus cucumeris and Botrytis cinerea with EC50 values of 0.464 μg/mL and 1.4 μg/mL, respectively. The living tissue bioassay results showed that the relative protection effects (RPE) of TDDE on tomato leaves, tomato fruit, and strawberry leaves infected with B. cinerea reached 76.78%, 86.2%, and 80.89%, respectively. In pot experiments, the RPE on tomato and strawberry plants infected with B. cinerea reached 84.11% and 96.37%, respectively. Morphological and physiological examination showed that TDDE had significant inhibitory effects on mycelial growth, including increased top offshoot, contorted hyphal tips, and extravasated cytochylema. Meanwhile, bactericidal activities of TDDE were significantly higher than kanamycin and streptomycin in five bacteria, and the plant tissue experiments further demonstrated that it had an 88.31% RPE on walnut leaves infected with Xanthomonas campestris pv. jugiandis, 72.18% RPE on potato infected with Erwinia carotovora subsp. carotovora, and 82.50% RPE on kiwifruit branches infected with Pseudomonas syringae pv. actinidiae. The active compounds isolated from A. ordosica in this study show great potential value for developing broad-spectrum fungicides, and also provide an important way to identify and isolate new bioactive products from medicinal plants.

The nationwide lockdown due to the COVID-19 pandemic in 2020 reduced industrial and human activities in China. In this study, we investigate atmospheric carbon monoxide (CO) concentration changes during the lockdown from observations at the surface and from two satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Infrared Atmospheric Sounding Interferometer (IASI)). It is found that the average CO surface concentration in 2020 was close to that in 2019 before the lockdown, and became 18.7% lower as compared to 2019 during the lockdown. The spatial variation of the change in the CO surface concentration is high, with an 8–27% reduction observed for Beijing, Shanghai, Chengdu, Zhengzhou, and Guangzhou, and almost no change in Wuhan. The TROPOMI and IASI satellite observations show that the CO columns decreased by 2–13% during the lockdown in most regions in China. However in South China, there was an 8.8% increase in the CO columns observed by TROPOMI and a 36.7% increase observed by IASI, which is contrary to the 23% decrease in the surface CO concentration. The enhancement of the CO column in South China is strongly affected by the fire emissions transported from Southeast Asia. This study provides an insight into the impact of COVID-19 on CO concentrations both at the surface and in the columns in China, and it can be extended to evaluate other areas using the same approach.

Somatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs) in vitro. Previously, a lentivirus induction strategy of introducing Oct4, Sox2, Nanog and Lin28 (OSNL) into the iPSC process has been shown as a possible way to produce chicken iPSCs from chicken embryonic fibroblasts, but the induction efficiency of this method was found to be significantly limiting. In order to help resolve this efficiency obstacle, this study seeks to clarify the associated regulation mechanisms and optimizes the reprogramming strategy of chicken iPSCs. This study showed that glycolysis and the expression of glycolysis-related genes correlate with a more efficient reprogramming process. At the same time, the transcription factors Oct4, Sox2 and Nanog were found to activate the expression of glycolysis-related genes. In addition, we introduced two small-molecule inhibitors (2i-SP) as a “glycolysis activator” together with the OSNL cocktail, and found that this significantly improved the induction efficiency of the iPSC process. As such, the study identifies direct molecular connections between core pluripotency factors and glycolysis during the chicken iPSC induction process and, with its results, provides a theoretical basis and technical support for chicken somatic reprogramming.

Small-scale placer mining in Colombia takes place in rural areas and involves excavations resulting in large footprints of bare soil and water ponds. Such excavated areas comprise a mosaic of challenging terrains for cloud and cloud-shadow detection of Sentinel-2 (S2A and S2B) data used to identify, map, and monitor these highly dynamic activities. This paper uses an efficient two-step machine-learning approach using freely available tools to detect clouds and shadows in the context of mapping small-scale mining areas, one which places an emphasis on the reduction of misclassification of mining sites as clouds or shadows. The first step is comprised of a supervised support-vector-machine classification identifying clouds, cloud shadows, and clear pixels. The second step is a geometry-based improvement of cloud-shadow detection where solar-cloud-shadow-sensor geometry is used to exclude commission errors in cloud shadows. The geometry-based approach makes use of sun angles and sensor view angles available in Sentinel-2 metadata to identify potential directions of cloud shadow for each cloud projection. The approach does not require supplementary data on cloud-top or bottom heights nor cloud-top ruggedness. It assumes that the location of dense clouds is mainly impacted by meteorological conditions and that cloud-top and cloud-base heights vary in a predefined manner. The methodology has been tested over an intensively excavated and well-studied pilot site and shows 50% more detection of clouds and shadows than Sen2Cor. Furthermore, it has reached a Specificity of 1 in the correct detection of mining sites and water ponds, proving itself to be a reliable approach for further related studies on the mapping of small-scale mining in the area. Although the methodology was tailored to the context of small-scale mining in the region of Antioquia, it is a scalable approach and can be adapted to other areas and conditions.

Although lncRNAs have been identified as playing critical roles in the development of germ cells, their potential involvement in the development of PGCs in chickens remains poorly understood. Differentially expressed lncRNAs (DELs) from previous RNA-seq of embryonic stem cells (ESCs), PGCs, and spermatogonial stem cells (SSCs) were analyzed by K-means clustering, from which a key candidate, lncRNA (lncRNA PGC regulator, LncPGCR) was obtained. We confirmed that LncPGCR plays a positive role in the development of PGCs by increasing the expression of the PGC marker gene (Cvh and C-kit), while downregulating the pluripotency-associated gene (Nanog) in vitro and in vivo. The activation and expression of LncPGCR are regulated by histone acetylation, and transcription factor TCF7L2. Mechanistically, a rescue assay was performed to further confirm that LncPGCR contributed to the development of PGCs by regulating the gga-miR-6577-5p/Btrc signaling pathway. Adsorption of gga-miR-6577-5p activated the WNT signaling cascade by relieving the gga-miR-6577-5p-dependent inhibition of Btrc expression. Taken together, our study discovered the growth-expedited role of LncPGCR in PGCs development, showing the potential LncPGCR/miR-6577-5p/Btrc pathway. The results and findings provide a novel insight into the development of PGCs.

High-quality leaf area index (LAI) products retrieved from satellite observations are urgently needed for crop growth monitoring and yield estimation, land-surface process simulation and global change studies. In recent years, sequential assimilation methods have been increasingly used to retrieve LAI from time series remote-sensing data. However, the inherent characteristics of these sequential assimilation methods result in temporal discontinuities in the retrieved LAI profiles. In this study, a sequential assimilation method with incremental analysis update (IAU) was developed to jointly update model states and parameters and to retrieve temporally continuous LAI profiles from time series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. Based on the existing multi-year Global Land Surface Satellite (GLASS) LAI product, a dynamic model was constructed to evolve LAI anomalies over time. The sequential assimilation method with an IAU technique takes advantage of the Kalman filter (KF) technique to update model parameters, uses the ensemble Kalman filter (EnKF) technique to update LAI anomalies recursively from time series MODIS reflectance data and then calculates the temporally continuous LAI values by combining the LAI climatology data. The method was tested over eight Committee on Earth Observing Satellites-Benchmark Land Multisite Analysis and Intercomparison of Products (CEOS-BELMANIP) sites with different vegetation types. The results indicate that the sequential method with IAU can precisely reconstruct the seasonal variation patterns of LAI and that the LAI profiles derived from the sequential method with IAU are smooth and continuous.

Currently, multiple leaf area index (LAI) products retrieved from remote sensing data are widely used in crop growth monitoring, land-surface process simulation and studies of climate change. However, most LAI products are only retrieved from individual satellite observations, which may result in spatial-temporal discontinuities and low accuracy in these products. In this paper, a new method was developed to simultaneously retrieve multiscale LAI data from satellite observations with different spatial resolutions based on an ensemble multiscale filter (EnMsF). The LAI average values corresponding to the date of satellite observations were calculated from the multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and were used as a priori knowledge for LAI in order to construct an initial ensemble multiscale tree (EnMsT). Satellite observations obtained at different spatial resolutions were then applied to update the LAI values at each node of the EnMsT using a two-sweep filtering procedure. Next, the retrieved LAI values at the finest scale were used as a priori knowledge for LAI for the new round of construction and updating of the EnMsT, until the sum of the difference of LAI values at each node of the EnMsT between two adjacent updates is less than a given threshold. The method was tested using Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) surface reflectance data and MODIS surface reflectance data from five sites that have different vegetation types. The results demonstrate that the retrieved LAI values for each spatial resolution were in good agreement with the aggregated LAI reference map values for the corresponding spatial resolution. The retrieved LAI values at the coarsest scale provided better accuracy with the aggregated LAI reference map values (root mean square error (RMSE) = 0.45) compared with that obtained from the MODIS LAI values (RMSE = 1.30).