Search

Research on fusion modeling of high spatial and temporal resolution images typically uses MODIS products at 500 m and 250 m resolution with Landsat images at 30 m, but the effect on results of the date of reference images and the 'mixed pixels' nature of moderate-resolution imaging spectroradiometer (MODIS) images are not often considered. In this study, we evaluated those effects using the flexible spatiotemporal data fusion model (FSDAF) to generate fusion images with both high spatial resolution and frequent coverage over three cotton field plots in the San Joaquin Valley of California, USA. Landsat images of different dates (day-of-year (DOY) 174, 206, and 254, representing early, middle, and end stages of the growing season, respectively) were used as reference images in fusion with two MODIS products (MOD09GA and MOD13Q1) to produce new time-series fusion images with improved temporal sampling over that provided by Landsat alone. The impact on the accuracy of yield estimation of the different Landsat reference dates, as well as the degree of mixing of the two MODIS products, were evaluated. A mixed degree index (MDI) was constructed to evaluate the accuracy and time-series fusion results of the different cotton plots, after which the different yield estimation models were compared. The results show the following: (1) there is a strong correlation (above 0.6) between cotton yield and both the Normalized Difference Vegetation Index (NDVI) from Landsat (NDVIL30) and NDVI from the fusion of Landsat with MOD13Q1 (NDVIF250). (2) Use of a mid-season Landsat image as reference for the fusion of MODIS imagery provides a better yield estimation, 14.73% and 17.26% higher than reference images from early or late in the season, respectively. (3) The accuracy of the yield estimation model of the three plots is different and relates to the MDI of the plots and the types of surrounding crops. These results can be used as a reference for data fusion for vegetation monitoring using remote sensing at the field scale.

In recent decades, the integrity and security of the ecosystem in the Sanjiang Plain have faced severe challenges due to land reclamation. Understanding the impact of paddy field expansion on regional ecosystem services (ESs), as well as revealing the trade-offs and synergies (TOS) between these services to achieve optimal resource allocation, has become an urgent issue to address. This study employs the InVEST model to map the spatial and temporal dynamics of five key ESs, while the Optimal Parameter Geodetector (OPGD) identifies primary drivers of these changes. Correlation analysis and Geographically Weighted Regression (GWR) reveal intricate TOS among ESs at multiple scales. Additionally, the Partial Least Squares-Structural Equation Model (PLS-SEM) elucidates the direct impacts of paddy field expansion on ESs. The main findings include the following: (1) The paddy field area in the Sanjiang Plain increased from 5775 km2 to 18,773.41 km2 from 1990 to 2020, an increase of 12,998.41 km2 in 40 years. And the area of other land use types has generally decreased. (2) Overall, ESs showed a recovery trend, with carbon storage (CS) and habitat quality (HQ) initially decreasing but later improving, and consistent increases were observed in soil conservation, water yield (WY), and food production (FP). Paddy fields, drylands, forests, and wetlands were the main ES providers, with soil type, topography, and NDVI emerging as the main influencing factors. (3) Distinct correlations among ESs, where CS shows synergies with HQ and SC, while trade-offs are noted between CS and both WY and FP. These TOS demonstrate significant spatial heterogeneity and scale effects across subregions. (4) Paddy field expansion enhances regional SC, WY, and FP, but negatively affects CS and HQ. These insights offer a scientific basis for harmonizing agricultural development with ecological conservation, enriching our understanding of ES interrelationships, and guiding sustainable ecosystem management and policymaking. [ABSTRACT FROM AUTHOR]

Farmland shelterbelts are aimed at farmland protection and productivity improvement, environmental protection and ecological balance, as well as land use planning and management. Farmland shelterbelts play a vital role in determining the structural integrity and overall effectiveness of farmland, and assessing the dynamic changes within these protective forests accurately and swiftly is essential to maintaining their protective functions as well as for policy formulation and effectiveness evaluation in relevant departments. Traditional methods for extracting farmland shelterbelt information have faced significant challenges due to the large workload required and the inconsistencies in the accuracy of existing methods. For example, the existing vegetation index extraction methods often have significant errors, which remain unresolved. Therefore, developing a more efficient extraction method with greater accuracy is imperative. This study focused on Youyi Farm in Heilongjiang Province, China, utilizing satellite data with spatial resolutions ranging from 0.8 m (GF-7) to 30 m (Landsat). By taking into account the growth cycles of farmland shelterbelts and variations in crop types, the optimal temporal window for extraction is identified based on phenological analysis. The study introduced a new index—the Re-Modified Anthocyanin Reflectance Index (RMARI)—which is an improvement on existing vegetation indexes, such as the NDVI and the improved original ARI. Both the accuracy and extraction results showed significant improvements, and the feasibility of the RMARI was confirmed. The study proposed four extraction schemes for farmland shelterbelts: (1) spectral feature extraction, (2) extraction using vegetation indexes, (3) random forest extraction, and (4) RF combined with characteristic index bands. The extraction process was implemented on the GEE platform, and results from different spatial resolutions were compared. Results showed that (1) the bare soil period in May is the optimal time period for extracting farmland shelterbelts; (2) the RF method combined with characteristic index bands produces the best extraction results, effectively distinguishing shelterbelts from other land features; (3) the RMARI reduces background noise more effectively than the NDVI and ARI, resulting in more comprehensive extraction outcomes; and (4) among the satellite images analyzed—GF-7, Planet, Sentinel-2, and Landsat OLI 8—GF-7 achieves the highest extraction accuracy (with a Kappa coefficient of 0.95 and an OA of 0.97), providing the most detailed textural information. However, comprehensive analysis suggests that Sentinel-2 is more suitable for large-scale farmland shelterbelt information extraction. This study provides new approaches and technical support for periodic dynamic forestry surveys, providing valuable reference points for agricultural ecological research. [ABSTRACT FROM AUTHOR]

The auxin/indole-3-acetic acid (Aux/IAA) gene family plays a critical role in auxin-mediated responses and fruit development. However, studies on its role in watermelon are limited. In this study, 29 ClIAA gene members were identified in the watermelon genome and classified into eleven groups. Of note, ClIAA16, which was found to be up-regulated during fruit ripening, was targeted using CRISPR/Cas9 gene editing. Knockout mutants of ClIAA16 exhibited a 3–4 day delay in ripening compared to the wild type, highlighting the regulatory importance of ClIAA16. Our findings shed light on the importance of ClIAA genes in watermelon fruit ripening and pave the way for further functional studies. [ABSTRACT FROM AUTHOR]

Northeast China plays a pivotal role in producing commodity grains. The precipitation and temperature distribution during the growth season is impacted by geographical and climate factors, rendering the region vulnerable to drought. However, relying on a single index does not reflect the severity and extent of drought in different regions. This research utilised the random forest (RF) model for screening remote sensing indices. Relative soil moisture (RSM) was employed to compare seven commonly used indices: the temperature vegetation dryness index (TVDI), vegetation supply water index (VSWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), multi-band drought index (MBDI), and normalised difference drought index (NDDI). The effectiveness of these indices for monitoring drought during different developmental stages of spring maize was evaluated. Trend rates were employed to investigate the temporal changes in drought patterns of spring maize from 2003 to 2020, and the Sen + Mann–Kendall test was used to analyse spatial variations. The results showed the following: (1) The seven remote sensing indices could accurately track drought during critical growth stages with the TVDI demonstrating higher applicability than the other six indices. (2) The application periods of two TVDIs with different parameters differed for the drought monitoring of spring maize in different developmental periods. The consistency and accuracy of the normalised difference vegetation index (NDVI)-based TVDI (TVDIN) were 5.77% and 34.62% higher than those of the enhanced vegetation index (EVI)-based TVDI (TVDIE), respectively, in the early stage. In contrast, the TVDIE exhibited 13.46% higher consistency than the TVDIN in the middle stage, and the accuracy was the same. During the later stage, the TVDIE showed significantly higher consistency and accuracy than the TVDIN with consistency increases of 9.61% and 38.64%, respectively. (3) The drought trend in northeast China increased from 2003 to 2020, exhibiting severe spring drought and a weakening of the drought in summer. The southern, southwestern, and northwestern parts of northeast China showed an upward drought trend; the drought-affected areas accounted for 37.91% of the study area. This paper identified the most suitable remote sensing indices for monitoring drought in different developmental stages of spring maize. The results provide a comprehensive understanding of the spatial–temporal patterns of drought during the past 18 years. These findings can be used to develop a dynamic agricultural drought monitoring model to ensure food security. [ABSTRACT FROM AUTHOR]

Objective: Activating KRAS mutations are the most common drivers in the development of non-small cell lung cancer (NSCLC). However, unsuccess of treatment by direct inhibition of KRAS has been proven. Deregulation of PI3K signaling plays an important role in tumorigenesis and drug resistance in NSCLC. The activity of PI3Kα-selective inhibition against KRAS-mutated NSCLC remains largely unknown., Methods: Cell proliferation was detected by sulforhodamine B assay. Cell cycle distribution and apoptosis were measured by flow cytometry. Cell signaling was assessed by Western blot and immunohistochemistry. RNA interference was used to down-regulate the expression of cyclin D1. Human NSCLC xenografts were employed to detect therapeutic efficacy in vivo ., Results: CYH33 possessed variable activity against a panel of KRAS-mutated NSCLC cell lines. Although CYH33 blocked AKT phosphorylation in all tested cells, Rb phosphorylation decreased in CYH33-sensitive, but not in CYH33-resistant cells, which was consistent with G1 phase arrest in sensitive cells. Combined treatment with the CDK4/6 inhibitor, PD0332991, and CYH33 displayed synergistic activity against the proliferation of both CYH33-sensitive and CYH33-resistant cells, which was accompanied by enhanced G1-phase arrest. Moreover, down-regulation of cyclin D1 sensitized NSCLC cells to CYH33. Reciprocally, CYH33 abrogated the PD0332991-induced up-regulation of cyclin D1 and phosphorylation of AKT in A549 cells. Co-treatment with these two drugs demonstrated synergistic activity against A549 and H23 xenografts, with enhanced inhibition of Rb phosphorylation., Conclusions: Simultaneous inhibition of PI3Kα and CDK4/6 displayed synergistic activity against KRAS-mutated NSCLC. These data provide a mechanistic rationale for the combination of a PI3Kα inhibitor and a CDK4/6 inhibitor for the treatment of KRAS-mutated NSCLC.

Topoisomerase I-associated DNA single-strand breaks selectively trapped by camptothecins are lethal after being converted to double-strand breaks by replication fork collisions. BLM (Bloom's syndrome protein), a RecQ DNA helicase, and topoisomerase IIIalpha (Top3alpha) appear essential for the resolution of stalled replication forks (Holliday junctions). We investigated the involvement of BLM in the signaling response to Top1-mediated replication DNA damage. In BLM-complemented cells, BLM colocalized with promyelocytic leukemia protein (PML) nuclear bodies and Top3alpha. Fibroblasts without BLM showed an increased sensitivity to camptothecin, enhanced formation of Top1-DNA complexes, and delayed histone H2AX phosphorylation (gamma-H2AX). Camptothecin also induced nuclear relocalization of BLM, Top3alpha, and PML protein and replication-dependent phosphorylation of BLM on threonine 99 (T99p-BLM). T99p-BLM was also observed following replication stress induced by hydroxyurea. Ataxia telangiectasia mutated (ATM) protein and AT- and Rad9-related protein kinases, but not DNA-dependent protein kinase, appeared to play a redundant role in phosphorylating BLM. Following camptothecin treatment, T99p-BLM colocalized with gamma-H2AX but not with Top3alpha or PML. Thus, BLM appears to dissociate from Top3alpha and PML following its phosphorylation and facilitates H2AX phosphorylation in response to replication double-strand breaks induced by Top1. A defect in gamma-H2AX signaling in response to unrepaired replication-mediated double-strand breaks might, at least in part, explain the camptothecin-sensitivity of BLM-deficient cells.