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The Aryl Hydrocarbon Receptor (AhR) pathway significantly influences immune cell regulation, impacting the effectiveness of immunotherapy and patient outcomes in melanoma. However, the specific downstream targets and mechanisms by which AhR influences melanoma remain insufficiently understood. Melanoma samples from The Cancer Genome Atlas (TCGA) and normal skin tissues from the Genotype-Tissue Expression (GTEx) database were analyzed to identify differentially expressed genes, which were intersected with a curated list of AhR-related pathway genes. Prognostic models were subsequently developed, and feature genes were identified. Advanced methodologies, including Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis, were employed to explore the biological significance of these genes. The stability of the machine learning models and the relationship between gene expression and immune infiltrating cells were validated using three independent melanoma datasets. A mouse melanoma model was used to validate the dynamic changes of the feature genes during tumor progression. The relationship between the selected genes and drug sensitivity, as well as non-coding RNA interactions, was thoroughly investigated. Our analysis identified a robust prognostic model, with four AhR-related genes (MAP2K1, PRKACB, KLF5, and PIK3R2) emerging as key contributors to melanoma progression. GSEA revealed that these genes are involved in primary immunodeficiency. Immune cell infiltration analysis demonstrated enrichment of CD4 naïve and memory T cells, macrophages (M0 and M2), and CD8 T cells in melanoma, all of which were associated with the expression of the four feature genes. Importantly, the diagnostic power of the prognostic model and the relevance of the feature genes were validated in three additional independent melanoma datasets. In the mouse melanoma model, Map2k1 and Prkacb mRNA levels exhibited a progressive increase with tumor progression, supporting their role in melanoma advancement. This study presents a comprehensive analysis of AhR-related genes in melanoma, highlighting MAP2K1, PRKACB, KLF5, and PIK3R2 as key prognostic markers and potential therapeutic targets. The integration of bioinformatics and machine learning provides a robust framework for enhancing prognostic evaluation in melanoma patients and offers new avenues for the development of treatments, particularly for those resistant to current immunotherapies.

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

Accurate and rapid estimation of the crop yield is essential to precision agriculture. Critical to crop improvement, yield is a primary index for selecting excellent genotypes in crop breeding. Recently developed unmanned aerial vehicle (UAV) platforms and advanced algorithms can provide powerful tools for plant breeders. Genotype category information such as the maturity group information (M) can significantly influence soybean yield estimation using remote sensing data. The objective of this study was to improve soybean yield prediction by combining M with UAV-based multi-sensor data using machine learning methods. We investigated three types of maturity groups (Early, Median and Late) of soybean, and collected the UAV-based hyperspectral and red–green–blue (RGB) images at three key growth stages. Vegetation indices (VI) and texture features (Te) were extracted and combined with M to predict yield using partial least square regression (PLSR), Gaussian process regression (GPR), random forest regression (RFR) and kernel ridge regression (KRR). The results showed that (1) the method of combining M with remote sensing data could significantly improve the estimation performances of soybean yield. (2) The combinations of three variables (VI, Te and M) gave the best estimation accuracy. Meanwhile, the flowering stage was the optimal single time point for yield estimation (R2 = 0.689, RMSE = 408.099 kg/hm2), while using multiple growth stages produced the best estimation performance (R2 = 0.700, RMSE = 400.946 kg/hm2). (3) By comparing the models constructed by different algorithms for different growth stages, it showed that the models built by GPR showed the best performances. Overall, the results of this study provide insights into soybean yield estimation based on UAV remote sensing data and maturity information.

The resulting maps of land use classification obtained by pixel-based methods often have salt-and-pepper noise, which usually shows a certain degree of cluttered distribution of classification image elements within the region. This paper carries out a study on crop classification and identification based on time series Sentinel images and object-oriented methods and takes the crop recognition and classification of the National Modern Agricultural Industrial Park in Jalaid Banner, Inner Mongolia, as the research object. It uses the Google Earth Engine (GEE) cloud platform to extract time series Sentinel satellite radar and optical remote sensing images combined with simple noniterative clustering (SNIC) multiscale segmentation with random forest (RF) and support vector machine (SVM) classification algorithms to classify and identify major regional crops based on radar and spectral features. Compared with the pixel-based method, the combination of SNIC multiscale segmentation and random forest classification based on time series radar and optical remote sensing images can effectively reduce the salt-and-pepper phenomenon in classification and improve crop classification accuracy with the highest accuracy of 98.66 and a kappa coefficient of 0.9823. This study provides a reference for large-scale crop identification and classification work.

The breeding of high-yield varieties is a core objective of soybean breeding programs, and phenotypic trait-based selection offers an effective pathway to achieve this goal. The aim of this study was to identify the key phenotypic traits of high-yield soybean varieties and to utilize these traits for screening high-yield soybean varieties. In this study, the UAV (unmanned aerial vehicle)- and field-based phenotypic data were collected from 1923 and 1015 soybean breeding plots at the Xuzhou experimental site in 2022 and 2023, respectively. First, the soybean varieties were grouped by using a self-organizing map and K-means clustering to investigate the relationships between various traits and soybean yield and to identify the key ones for selecting high-yield soybean varieties. It was shown that the duration of canopy coverage remaining above 90% (Tcc90) was a critical phenotypic trait for selecting high-yield varieties. Moreover, high-yield soybean varieties typically exhibited several key phenotypic traits such as rapid development of canopy coverage (Tcc90r, the time when canopy coverage first reached 90%), prolonged duration of high canopy coverage (Tcc90), a delayed decline in canopy coverage (Tcc90d, the time when canopy coverage began to decline below 90%), and moderate-to-high plant height (PH) and hundred-grain weight (HGW). Based on these findings, a method for screening high-yield soybean varieties was proposed, through which 87% and 72% of high-yield varieties (top 5%) in 2022 and 2023, respectively, were successfully selected. Additionally, about 9% (in 2022) and 10% (in 2023) of the low-yielding (bottom 60%) were misclassified as high-yielding. This study demonstrates the benefit of high-throughput phenotyping for soybean yield-related traits and variety screening and provides helpful insights into identifying high-yield soybean varieties in breeding programs.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by social communication deficits and restricted, repetitive behaviors. Growing evidence implicates neuroinflammation-induced blood-brain barrier (BBB) dysfunction as a key pathogenic mechanism in ASD, although the underlying molecular pathways remain poorly understood. This study aimed to identify critical genes linking BBB function and neuroinflammatory activation, with the ultimate goal of evaluating potential therapeutic targets. Through integrative analysis combining differential gene expression profiling with three machine learning algorithms - Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and RandomForest combined with eXtreme Gradient Boosting (XGBoost) - we identified four hub genes, with JUN emerging as a core regulator. JUN demonstrated strong associations with both BBB integrity and microglial activation in ASD pathogenesis. Using a maternal immune activation (MIA) mouse model of ASD, we observed significant downregulation of cortical tight junction proteins ZO-1 and occludin, confirmed through immunofluorescence and qPCR analysis. Bioinformatics analysis revealed a close correlation between JUN and IL-6/MMP-9 signaling in ASD-associated microglial activation. These findings were validated in vivo, with immunofluorescence and qPCR demonstrating elevated IL-6 and MMP-9 expression in ASD mice. Pharmacological intervention using ventricular JNK inhibitor administration effectively downregulated JUN and MMP-9 expression. In vitro studies using IL-6-stimulated BV-2 microglial cells replicated these findings, showing JNK inhibitor-mediated suppression of JUN and MMP-9 upregulation. These results collectively identify the IL-6/JUN/MMP-9 pathway as a specific mediator of barrier dysfunction in ASD, representing a promising target for personalized therapeutic interventions.

The liver is an immunologically tolerant organ and a common site of distant metastasis for various cancers. The expression levels of glucose-regulated protein 78 (GRP78) have been associated with tumor malignancy. Secretory GRP78 (sGRP78) released from tumor cells contributes to the establishment of an immunosuppressive tumor microenvironment by regulating cytokine production in macrophages and dendritic cells (DCs). However, the role of sGRP78 on tumor cell colonization and metastasis in the liver remains unclear. Herein, we found that GRP78 was expressed at higher levels in the liver compared to other tissues and organs. We performed intravital imaging using a sGRP78-overexpressing breast cancer cell line (E0771) and found that sGRP78 interacted with dendritic cells (DCs) and F4/80 macrophages in the liver. Importantly, sGRP78 overexpression inhibited DC activation and induced M2-like polarization in F4/80 macrophages. Moreover, sGRP78 overexpression enhanced TGF-β production in the liver. In conclusion, sGRP78 promotes tumor cell colonization in the liver by remodeling the tumor microenvironment and promoting immune tolerance. The ability of sGRP78-targeting strategies to prevent or treat liver metastasis should be further examined.

Background Hepatocellular carcinoma (HCC) poses considerable difficulties regarding the prognosis and the assessment of treatment efficacy. Additionally, while it is recognized that post-translational modification (PTM) plays a crucial role in modulating HCC progression, their specific prognostic implications in HCC have not been thoroughly investigated. Methods 21 types of PTM (acetylation, succinylation, malonylation, crotonylation, β-hydroxybutyrylation, lactylation, palmitoylation, myristoylation, SUMOylation, NEDDylation, ISGylation, ATG8ylation, FAT10ylation, UFMylation, methylation, glycosylation, biotinylation, S-nitrosylation, phosphorylation, ubiquitination, deubiquitination) were generated consensus cluster. Then, WGCNA was utilized to identify module genes. Finally, a machine learning approach was employed to create PTM.score. Results This analysis revealed two distinct subtypes of PTMs, each characterized by unique molecular signatures. By integrating different categories of genes, including prognosis-related DEGs, module genes, and PTM-related genes, 15 hub genes were identified, and a PTM.score was developed. PTM.score was rigorously validated across independent external cohorts (TCGA-LIHC, LIRI-JP, GSE10143, GSE14520, GSE27150, GSE36376, and GSE76427) and an in-house cohort, demonstrating its reliability and potential applicability. In addition, patients categorized with a low PTM.score displayed a TME that was more actively engaged, which corresponded with a poor prognosis. Furthermore, these patients demonstrated a high level of responsiveness to immunotherapy interventions. Furthermore, an examination using scRNA-seq and spatial transcriptomics indicated that patients with low PTM.score exhibited heightened cell proliferation and malignancy. Conclusion This novel PTM-related prognostic signature could effectively assess the prognosis and therapeutic responses of HCC patients, providing new perspectives for individualized treatment for the patient population.

The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing is limited by the penetration of sunlight into the canopy depth, and thus directly estimating winter wheat stem biomass via canopy spectra remains a difficult task. There is a stable linear relationship between the stem dry biomass (SDB) and leaf dry biomass (LDB) of winter wheat during the entire growth stage. Therefore, this study comprehensively considered remote sensing and crop phenology, as well as biomass allocation laws, to establish a novel two-component (LDB, SDB) and two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) to estimate SDB across the growth stages of winter wheat. The core of the Tc/Tp-SDB model employed phenological variables (e.g., effective accumulative temperature, EAT) to correct the SDB estimations determined from the LDB. In particular, LDB was estimated using spectral vegetation indices (e.g., red-edge chlorophyll index, CIred edge). The results revealed that the coefficient values (β0 and β1) of ordinary least squares regression (OLSR) of SDB with LDB had a strong relationship with phenological variables. These coefficient (β0 and β1) relationships were used to correct the OLSR model parameters based on the calculated phenological variables. The EAT and CIred edge were determined as the optimal parameters for predicting SDB with the novel Tc/Tp-SDB model, with r, RMSE, MAE, and distance between indices of simulation and observation (DISO) values of 0.85, 1.28 t/ha, 0.95 t/ha, and 0.31, respectively. The estimation error of SDB showed an increasing trend from the jointing to flowering stages. Moreover, the proposed model showed good potential for estimating SDB from UAV hyperspectral imagery. This study demonstrates the ability of the Tc/Tp-SDB model to accurately estimate SDB across different growing seasons and growth stages of winter wheat.

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data.