Search Results (60)

Monocationic arene ruthenium complexes (RuL₁–RuL₄) incorporating phenothiazinyl-hydrazinyl-thiazole ligands (L₁–L₄) have been synthesized, characterized and evaluated as anticancer agents. Their cytotoxicity, antiproliferative activity and alteration of apoptotic gene expression were studied on three cancer cell lines, a double positive breast cancer cell line MCF-7 and two triple negative breast cancer cell lines Hs578T and MDA-MB-231. All arene ruthenium complexes were able to reduce the viability of the breast cancer cell lines, with the highest cytotoxicities being recorded for the [(p-cymene)RuL₃Cl]⁺ (RuL₃) complex on the MCF-7 (IC₅₀ = 0.019 µM) and Hs578T cell lines (IC₅₀ = 0.095 µM). In the double positive MCF-7 breast cancer cells, the complexes [(p-cymene)RuL₁Cl]⁺ (RuL₁) and [(p-cymene)RuL₂Cl]⁺ (RuL₂) significantly upregulated pro-apoptotic genes including BAK, FAS, NAIP, CASP8, TNF, XIAP and BAD, while downregulating TNFSF10. In the triple negative breast cancer cell line Hs578T, RuL₁ reduced TNFSF-10 and significantly upregulated BAK, CASP8, XIAP, FADD and BAD, while complex RuL₂ also increased BAK and CASP8 expression, but had limited effects on other genes. The triple negative MDA-MB-231 cancer cells treated with RuL₁ upregulated NOD1 and downregulated p53, while RuL₂ significantly downregulated p53, XIAP and TNFSF10, with minor changes in other genes. The significant alterations in the expression of key apoptotic genes suggest that such complexes have the potential to target cancer cells. Full article

Analyzing irrigation patterns to promote efficient water use in urban areas is challenging. Analysis of irrigation by remote sensing (AIRS) combines multispectral aerial imagery, evapotranspiration data, and ground-truth measurements to overcome these challenges. We demonstrate AIRS on eight neighborhoods in Weber County, Utah, using 0.6 m National Agriculture Imagery Program (NAIP) and 0.07 m drone imagery, reference evapotranspiration (ET), and water use records. We calculate the difference between the actual and hypothetical water required for each parcel and compare water use over three time periods (2018, 2021, and 2023). We find that the quantity of overwatering, as well as the number of customers overwatering, is decreasing over time. AIRS provides repeatable estimates of irrigated area and irrigation demand that allow water utilities to track water user habits and landscape changes over time and, when controlling for other variables, see if water conservation efforts are effective. In terms of image analysis, we find that (1) both NAIP and drone imagery are sufficient to measure irrigated area in urban settings, (2) the selection of a threshold value for the normalized difference vegetation index (NDVI) becomes less critical for higher-resolution imagery, and (3) irrigated area measurement can be enhanced by combining NDVI with other tools such as building footprint extraction, object classification, and deep learning. Full article

In the United States, several land use and land cover (LULC) data sets are available based on satellite data, but these data sets often fail to accurately represent features on the ground. Alternatively, detailed mapping of heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences among scenes, landscape heterogeneity, and computational limitations. Various machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study were to evaluate bagging (Random Forest, RF), boosting (Gradient Boosting Machines [GBM] and extreme gradient boosting [XGB]), and stacking ensemble ML models. We used these techniques on a time series of Sentinel 2A data and NAIP orthoimagery to create a LULC map of a portion of Irion and Tom Green counties in Texas (USA). We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. We then compared accuracy based on random cross-validation without accounting for spatial autocorrelation and target-oriented cross-validation accounting for spatial structures of the training data set. Comparison of random and target-oriented cross-validation results showed that autocorrelation in the training data offered overestimation ranging from 2% to 3.5%. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base learners. We show that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use/land cover classification using geographic object-based image analysis. Full article

Fibrosis is a pathological condition consisting of a delayed deposition and remodeling of the extracellular matrix (ECM) by fibroblasts. This deregulation is mostly triggered by a chronic stimulus mediated by pro-inflammatory cytokines, such as TNF-α and IL-1, which activate fibroblasts. Due to their anti-inflammatory and immunosuppressive potential, dental pulp stem cells (DPSCs) could affect fibrotic processes. This study aims to clarify if DPSCs can affect fibroblast activation and modulate collagen deposition. We set up a transwell co-culture system, where DPSCs were seeded above the monolayer of fibroblasts and stimulated with LPS or a combination of TNF-α and IL-1β and quantified a set of genes involved in inflammasome activation or ECM deposition. Cytokines-stimulated co-cultured fibroblasts, compared to unstimulated ones, showed a significant increase in the expression of IL-1β, IL-6, NAIP, AIM2, CASP1, FN1, and TGF-β genes. At the protein level, IL-1β and IL-6 release as well as FN1 were increased in stimulated, co-cultured fibroblasts. Moreover, we found a significant increase of MMP-9 production, suggesting a role of DPSCs in ECM remodeling. Our data seem to suggest a crosstalk between cultured fibroblasts and DPSCs, which seems to modulate genes involved in inflammasome activation, ECM deposition, wound healing, and fibrosis. Full article

Vegetation canopy height mapping is vital for forest monitoring. However, the high cost and inefficiency of manual tree measurements, coupled with the irregular and limited local-scale acquisition of airborne LiDAR data, continue to impede its widespread application. The increasing availability of high spatial resolution imagery is creating opportunities to characterize forest attributes at finer resolutions over large regions. In this study, we investigate the synergy of airborne lidar and high spatial resolution USDA-NAIP imagery for detailed canopy height mapping using an image-to-image deep learning approach. Our main inputs were 1 m NAIP image patches which served as predictor layers and corresponding 1 m canopy height models derived from airborne lidar data, which served as output layers. We adapted a U-Net model architecture for canopy height regression, training and validating the models with 10,000 256-by-256 pixel image patches. We evaluated three settings for the U-Net encoder depth and used both 1 m and 2 m datasets to assess their impact on model performance. Canopy height predictions from the fitted models were highly correlated (R² = 0.70 to 0.89), precise (MAE = 1.37–2.21 m), and virtually unbiased (Bias = −0.20–0.07 m) with respect to validation data. The trained models also performed adequately well on the independent test data (R² = 0.62–0.78, MAE = 3.06–4.1 m). Models with higher encoder depths (3,4) and trained with 2 m data provide better predictions than models with encoder depth 2 and trained on 1 m data. Inter-comparisons with existing canopy height products also showed our canopy height map provided better agreement with reference airborne lidar canopy height estimates. This study shows the potential of developing regional canopy height products using airborne lidar and NAIP imagery to support forest productivity and carbon modeling at spatially detailed scales. The 30 m canopy height map generated over Texas holds promise in advancing economic and sustainable forest management goals and enhancing decision-making in natural resource management across the state. Full article

In contrast to agricultural settings, irrigation of residential properties in urban settings is typified by small and irregular areas, many untrained water users, limited end-use metering, and differing groundcover. This makes analyzing irrigation patterns to promote efficient water use challenging. We explore the use of remote sensing tools and data sets to help characterize urban irrigation use in the United States. Herein, we review available multispectral imagery datasets and discuss tradeoffs among spatial resolution, collection frequency, and historical availability. We survey options for evapotranspiration data at various spatial and temporal scales that could be paired with the multispectral imagery to estimate irrigation demand. We call the general approach Analysis of Irrigation by Remote Sensing (AIRS). We discuss the potential of drones to capture higher-resolution temporal or spatial data in study areas and/or multiple flights in a single season to provide ground truth or establish patterns. We present data and analysis options that may be suitable depending on specific project objectives. Through a case study scenario, we illustrate some tradeoffs. As a starting point, we recommend public 1 m National Agriculture Imagery Program (NAIP) images for irrigated area estimates and normalized difference vegetation index (NDVI) calculations, combined with open-source OpenET for evapotranspiration, to provide historical snapshots of water use, vegetation quality, and general irrigation efficiency in urban areas. The method is most effective when paired with optional water use data and can provide information with which to design more optimal studies. Full article

Classification of remotely sensed imagery for reliable land use and land cover (LULC) remains a challenge in areas where spectrally similar LULC features occur. For example, bare soils of harvested crop fields in agricultural watersheds exhibit spectral characteristics similar to high-intensity developed regions and impede an accurate classification. The goal of this study is to improve the accuracy of LULC classification of satellite imagery for the Big Sunflower River Watershed, Mississippi using ancillary data, multiple classification methods, and a post-classification correction (PCC). To determine the best approach, the methodology was applied to Landsat 8 Operational Land Imager (OLI) imagery during the growing season and post-harvest. Imagery for the growing season was acquired on 25 August 2015, and post-harvest was acquired on 7 January 2018. Three classification methods were applied: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). LULC imagery was classified as open water, woody wetlands, harvested crop, rangeland, cultivated crop, high-intensity developed, and mid-low intensity developed areas. Ancillary data such as normalized difference vegetation index (NDVI), thematic maps of urban areas, river networks, transportation networks, high-resolution National Agriculture Imagery Program (NAIP) imagery, Google Earth time-series data, and phenology were used to determine the training dataset. Initially none of the three classification methods performed adequately. Hence, a post-classification correction (PCC) was implemented by masking and applying a majority filter using thematic maps of urban areas. Once PCC was implemented, the accuracies from each of the classification methods increased significantly with the SVM classification method performing best in both the growing season and post-harvest with an overall classification accuracy of 93.5% with a Kappa statistic of 0.88 in the post-harvest imagery and an overall classification accuracy of 84% with a Kappa statistic of 0.789 in the imagery from the growing season. It was found that SVM was the best classification method while PCC is an effective strategy to implement when dealing with spectrally similar LULC features. The use of SVM together with PCC increased the reliability of the information extracted. Strategies from this study can help to evaluate the LULC in agricultural and other watersheds. Full article

Geovisualization tools can supplement the statistical analyses of landscape-level wildfire behavior by enabling the discovery of nuanced information regarding the relationships between fire spread, topography, fuels, and weather. The objectives of this study were to develop and evaluate the effectiveness of geovisualization tools for analyzing wildfire behavior and specifically to apply those tools to study portions of the Thomas and Detwiler wildfire events that occurred in California in 2017. Fire features such as active fire fronts and rate of spread (ROS) vectors derived from repetitive airborne thermal infrared (ATIR) imagery sequences were incorporated into geovisualization tools hosted in a web geographic information systems application. This geovisualization application included ATIR imagery, fire features derived from ATIR imagery (rate of spread vectors and fire front delineations), growth form maps derived from NAIP imagery, and enhanced topographic rasters for visualizing changes in local topography. These tools aided in visualizing and analyzing landscape-level wildfire behavior for study portions of the Thomas and Detwiler fires. The primary components or processes of fire behavior analyzed in this study were ROS, spotting, fire spread impedance, and fire spread over multidirectional slopes. Professionals and researchers specializing in wildfire-related topics provided feedback on the effectiveness and utility of the geovisualization tools. The geovisualization tools were generally effective for visualizing and analyzing (1) fire spread over multidirectional slopes; (2) differences in spread magnitudes within and between sequences over time; and (3) the relative contributions of fuels, slope, and weather at any given point within the sequences. Survey respondents found the tools to be moderately effective, with an average effectiveness score of 6.6 (n = 5) for the visualization tools on a scale of 1 (ineffective) to 10 (effective) for postfire spread analysis and visualizing fire spread over multidirectional slopes. The results of the descriptive analysis indicate that medium- and fine-scale topographic features, roads, and riparian fuels coincided with cases of fire spread impedance and exerted control over fire behavior. Major topographic features such as ridges and valleys slowed, or halted, fire spread consistently between study areas. The relationships between spotting, fuels, and topography were inconclusive. Full article

The benefits and services of urban forests are becoming increasingly well documented, with carbon storage being the main focus of attention. Recent efforts in urban remote sensing have incorporated additional data such as LiDAR data but have been limited to sections of an urban area or only certain species. Existing models are not generalizable to remaining unmeasured urban trees. To make a generalizable individual urban tree model, we used metrics from NAIP aerial imagery and NOAA and USGS LiDAR data for 2013 and 2019, and two crown-level urban tree biomass models were developed. We ran a LASSO regression, which selected the best variables for the biomass model, followed by a 10-fold cross-validation. The 2013 model had an adjusted R² value of 0.85 and an RMSE of 1797 kg, whereas the 2019 model had an adjusted R² value of 0.87 and an RMSE of 1444 kg. The 2019 model was then applied to the rest of the unsampled trees to estimate the total biomass and total carbon stored for all the trees in the county. Recommendations include changes to ground inventory techniques to adapt to the current methods and limitations of remote sensing biomass estimation. Full article

As the world’s urban population increases to the predicted 70% of the total population, urban infrastructure and built-up land will continue to grow as well. This growth will continue to have an impact on the urban heat island effect in all of the world’s cities. The urban tree canopy has been found to be one of the few factors that can lessen the effects of the urban heat island effect. This study seeks to accomplish two objectives: first, we examine the use of a commonly used machine learning classifier (e.g., Support Vector Machine) for identifying the urban tree canopy using no-cost high resolution NAIP imagery. Second, we seek to use Land Surface Temperature (LST) maps derived from no-cost Landsat thermal imagery to identify correlations between canopy loss and temperature hot spot increases over a 14-year period in Columbia, SC, USA. We found the SVM imagery classifier was highly accurate in classifying both the 2005 imagery (94.3% OA) and the 2019 imagery (94.25% OA) into canopy and other classes. We found the color infrared image available in the 2019 NAIP imagery better for identifying canopy than the true color images available in 2005 (97.8% vs. 90.2%). Visual analysis based on the canopy maps and LST maps showed temperatures rose near areas where tree canopy was lost, and urban development continued. Future studies will seek to improve classification methods by including other classes, other ancillary data sets (e.g., LiDAR), new classification methods (e.g., deep learning), and analytical methods for change detection analysis. Full article

Reproductive failure is a major contributor to inefficiency within the cow-calf industry. Particularly problematic is the inability to diagnose heifer reproductive issues prior to pregnancy diagnosis following their first breeding season. Therefore, we hypothesized that gene expression from the peripheral white blood cells at weaning could predict the future reproductive potential of beef heifers. To investigate this, the gene expression was measured using RNA-Seq in Angus–Simmental crossbred heifers sampled at weaning and retrospectively classified as fertile (FH, n = 8) or subfertile (SFH, n = 7) after pregnancy diagnosis. We identified 92 differentially expressed genes between the groups. Network co-expression analysis identified 14 and 52 hub targets. ENSBTAG00000052659, OLR1, TFF2, and NAIP were exclusive hubs to the FH group, while 42 hubs were exclusive to the SFH group. The differential connectivity between the networks of each group revealed a gain in connectivity due to the rewiring of major regulators in the SFH group. The exclusive hub targets from FH were over-represented for the CXCR chemokine receptor pathway and inflammasome complex, while for the SFH, they were over-represented for immune response and cytokine production pathways. These multiple interactions revealed novel targets and pathways predicting reproductive potential at an early stage of heifer development. Full article

Due to their therapeutic potential, mesenchymal stem cells are the subject of intensive research on the use of their potential in the treatment of, among others, neurodegenerative diseases or immunological diseases. They are among the newest in the field of medicine. The presented study aimed to evaluate the expression of eight genes from the IAP family and the gene regulating IAP—XAF1—in stem cells derived from human milk, using the qPCR method. The relationships between the expression of genes under study and clinical data, such as maternal age, maternal BMI, week of pregnancy in which the delivery took place, bodyweight of the newborn, the number of pregnancies and deliveries, and the time elapsed since delivery, were also analyzed. The research was carried out on samples of human milk collected from 42 patients hospitalized in The Clinic of Obstetrics and Perinatology of the Independent Public Clinical Hospital No. 4, in Lublin. The conducted research confirmed the expression of the following genes in the tested material: NAIP, BIRC2, BIRC3, BIRC5, BIRC6, BIRC8, XIAP, XAF1, OCT4 and SOX2. Moreover, several dependencies of the expression of individual genes on the maternal BMI (BIRC5, XAF1 and NAIP), the time since childbirth (BIRC5, BIRC6, XAF1 and NAIP), the number of pregnancies and deliveries (BIRC2, BIRC5, BIRC6 and XAF1), the manner of delivery (XAF1 and OCT4), preterm labor (BIRC6 and NAIP) were demonstrated. Additionally, we found positive relationships between gene expression of BIRC7, BIRC8 and XAF1 and the main factors of pluripotency: SOX2 and OCT4. This work is the first to investigate the expression of genes from the IAPs family in mother’s milk stem cells. Full article

Estimates of riparian vegetation water use are important for hydromorphological assessment, partitioning within human and natural environments, and informing environmental policy decisions. The objectives of this study were to calculate the actual evapotranspiration (ETa) (mm/day and mm/year) and derive riparian vegetation annual consumptive use (CU) in acre-feet (AF) for select riparian areas of the Little Colorado River watershed within the Navajo Nation, in northeastern Arizona, USA. This was accomplished by first estimating the riparian land cover area for trees and shrubs using a 2019 summer scene from National Agricultural Imagery Program (NAIP) (1 m resolution), and then fusing the riparian delineation with Landsat-8 OLI (30-m) to estimate ETa for 2014–2020. We used indirect remote sensing methods based on gridded weather data, Daymet (1 km) and PRISM (4 km), and Landsat measurements of vegetation activity using the two-band Enhanced Vegetation Index (EVI2). Estimates of potential ET were calculated using Blaney-Criddle. Riparian ETa was quantified using the Nagler ET(EVI2) approach. Using both vector and raster estimates of tree, shrub, and total riparian area, we produced the first CU measurements for this region. Our best estimate of annual CU is 36,983 AF with a range between 31,648–41,585 AF and refines earlier projections of 25,387–46,397 AF. Full article

Nucleotide-binding oligomerization domain receptors (NOD-like receptors, NLRs) are involved in modulating the innate immune responses of the trophoblast and the placenta in normal pregnancy. The thymus participates in regulation of innate and adaptive immune responses. However, it is unclear whether expression of NLR is modulated in the maternal thymus during early pregnancy. In this study, thymuses were sampled at day 16 of the estrous cycle, and at days 13, 16 and 25 of gestation (n = 6 for each group) from ewes after slaughter. Different stages were chosen because the maternal thymus was under the different effects of interferon-tau and/or progesterone or not. RT-qPCR, Western blot and immunohistochemistry analysis were used to analyze the expression of the NLR family, including NOD1; NOD2; major histocompatibility complex class II transactivator (CIITA); NLR family apoptosis inhibitory protein (NAIP); nucleotide-binding oligomerization domain and Leucine-rich repeat and Pyrin domain containing protein 1 (NLRP1), NLRP3 and NLRP7. The results showed that expression level of NOD1 was changed with the pregnancy stages, and expression levels of NOD2, CIITA, NAIP, NLRP1, NLRP3 and NLRP7 mRNA and proteins were peaked at day 13 of pregnancy. The levels of NOD2 and CIITA were increased during early pregnancy. The stainings for NOD2 and NLRP7 proteins were located in epithelial reticular cells, capillaries and thymic corpuscles. In summary, pregnancy stages changed expression of NLR family in the maternal thymus, which may be related to the modulation of maternal thymic immune responses, and beneficial for normal pregnancy in sheep. Full article

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at $t_1$ together with low-resolution images at a future time $t_2$ from the same location as inputs and fuses them to generate high-resolution images for the same location at $t_2$. We propose an arithmetic operation applied to the low-resolution images at the two time points in feature space to take care of temporal changes. We evaluated the proposed model on three modality pairs for multimodal temporal image fusion, including downsampled WorldView-2/original WorldView-2, Landsat-8/Sentinel-2, and Sentinel-2/NAIP. Experimental results show that our model outperforms traditional algorithms and recent deep learning-based models by large margins in most scenarios, achieving sharp fused images while appropriately addressing temporal changes. Full article