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Harmful and nuisance algal blooms are becoming a greater concern to public health, riverine ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of nitrogen from various sources. To improve understanding of bloom dynamics and how they affect water quality, often measured as algal biomass measured through pigment standing crops, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River in western Montana. Image data were collected across the spectral range of 400–1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chl a and phycocyanin standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to their growth form. Estimates of chl a and phycocyanin standing crops generated through a linear regression analysis are compared to in situ data, resulting in a maximum R2 of 0.96 for estimating fila/epip chl-a and 0.94 when estimating epiphytic phycocyanin. Estimates of pigment standing crops from total abundance, epiphytic, and the sum of filamentous and epiphytic sources are also included, resulting in a promising method for remotely estimating algal standing crops. This method addresses the shortcomings of current monitoring techniques, which are limited in spatial and temporal scale, by proposing a method for rapid collection of high-spatial-resolution pigment abundance estimates. 

Germline mutations provide the raw material for all evolutionary processes and contribute to the occurrence of spontaneous human diseases and disorders. Yet despite the daily interaction of humans and other organisms with an increasing number of chemicals that are potentially mutagenic, precise measurements of chemically induced changes to the genome-wide rate and spectrum of germline mutation are lacking. A large-scale mutation-accumulation experiment was propagated in the presence and absence of an environmentally relevant cadmium concentration to quantify the influence of cadmium on germline mutation rates and spectra. Cadmium exposure dramatically changed the genome-wide rates and regional spectra of germline mutations. In comparison with those in control conditions, exposed to cadmium had a higher overall mutation rates and a lower overall mutation rate. exposed to cadmium had a higher intergenic mutation rate and a lower exonic mutation rate. The higher intergenic mutation rate under cadmium exposure was the result of an elevated intergenic rate, whereas the lower exon mutation rate in cadmium was the result of a complete loss of exonic mutations-mutations that are known to be enriched at 5-hydroxymethylcytosine. We experimentally show that cadmium exposure significantly reduced 5-hydroxymethylcytosine levels. These results provide evidence that cadmium changes regional mutation rates and can influence regional rates by interfering with an epigenetic process in the germline. We further suggest these observed cadmium-induced changes to the germline mutation rate may be explained by cadmium's inhibition of zinc-containing domains. The cadmium-induced changes to germline mutation rates and spectra we report provide a comprehensive view of the mutagenic perils of cadmium and give insight into its potential impact on human population health. https://doi.org/10.1289/EHP8932. 

Hyperspectral imaging systems are becoming widely used due to their increasing accessibility and their ability to provide detailed spectral responses based on hundreds of spectral bands. However, the resulting hyperspectral images (HSIs) come at the cost of increased storage requirements, increased computational time to process, and highly redundant data. Thus, dimensionality reduction techniques are necessary to decrease the number of spectral bands while retaining the most useful information. Our contribution is two-fold: First, we propose a filter-based method called interband redundancy analysis (IBRA) based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. Second, we apply a wrapper-based approach called greedy spectral selection (GSS) to the results of IBRA to select bands based on their information entropy values and train a compact convolutional neural network to evaluate the performance of the current selection. We also propose a feature extraction framework that consists of two main steps: first, it reduces the total number of bands using IBRA; then, it can use any feature extraction method to obtain the desired number of feature channels. We present classification results obtained from our methods and compare them to other dimensionality reduction methods on three hyperspectral image datasets. Additionally, we used the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager.