Watershed planning is a pivotal exercise for all jurisdictions irrespective of size, landscape complexity, or other nuances. As a result of the intricate relationship between land use/land cover (LULC) and water resources, it becomes prudent to not only develop historical and contemporary LULC data for watershed planning purposes, but more importantly, the production of future LULC datasets has the potential to better inform watershed planners. This study explored an optimal workflow that can be adopted for the production of baseline LULC input images from a moderate spatial resolution sensor such as Landsat, and the identification, translation, and configuration of land change drivers and regional comprehensive plan prescriptions in the creation of future LULC data for a regional watershed. The study conducted in the Lower Chippewa River Watershed, Wisconsin, USA demonstrated that an object-based hybrid classification approach resulted in the generation of improved projected images with a 15% increase in area under the curve (AUC) value compared to a pixel-based hybrid classification method even though both methods displayed comparable overall image classification accuracies (≤ 1.8%). Results further displayed that configuring anthropogenic drivers in a trend format rather than individual year values can result in a more efficient training of a multi-layer perceptron neural network – Markov Chain model. The calibrated and validated model demonstrated that on average, residential, commercial, institutional, green vegetation/shrub, and industrial LULC are expected to grow through 2050, though at a slower rate (12%) compared to contemporary period (39%), while forest and agricultural lands are slated to decline (−2%).

This study examined the potential of integrating fractal texture with spectral information on urban landscape characterization by the maximum likelihood image classifier. The fractal texture was first derived from the red band of a Landsat ETM+ image by applying the triangular prism algorithm at different window sizes. The quality of the twenty-five resultant texture bands were then analyzed by fourteen approaches at both of the pre- and post-classification stages. Results showed all evaluations employed at the pre-classification stage are useful to screen out texture bands more useful than others to facilitate the later supervised image classification. This texture information is observed useful only for classifying medium- and low- density residential categories. The window size leading to the best overall classification accuracy is identified as 31 x 31, implying this scale should be kept as a guideline for future studies if a similar methodology is adopted.

This study mainly utilized the Soil and Water Assessment Tool (SWAT) together with SWAT-CUP, both free and open source software (FOSS), to construct a distributed hydrologic flow model for the Rokel-Seli River basin, Sierra Leone, in a bid to spatiotemporally evaluate the role of changes in land use/land cover (LULC) and microclimate on streamflow regimes. The model was informed by LULC data derived from three Landsat satellite images collected in 2002, 2010, and 2016. The LULC data was generated with the aid of several python libraries accessed through the FOSS Anaconda Navigator. LULC change analysis demonstrated that between 2002 and 2016, urban, agricultural, water, and mining lands expanded significantly but forest cover reduced the most (−5.7 %). While average annual surface runoff dramatically increased from 2002 to 2010 (31.7 %), the period after the reservoir construction (2010–2016) recorded lower increase in surface runoff (0.9 %). Result of the study suggested that the construction of a major reservoir to support hydroelectricity in concert with significant loss of forest cover and shrub played a greater role in increasing surface runoff compared with the influence of microclimate.

Cytochrome P450 enzyme 2D6 (CYP2D6) is an important member of the cytochrome P450 enzyme superfamily, with more than 100 CYP2D6 allelic variants being previously reported. The aim of this study was to assess the catalytic characteristics of 25 alleles (CYP2D6.1 and 24 CYP2D6 variants) and their effects on the metabolism of propafenone in vitro. Twenty-five CYP2D6 alleles were expressing in 21 Spodoptera frugiperda (Sf) insect cells, and each variant was evaluated using propafenone as the substrate. Reactions were performed at 37 °C with 1–100 μmol/L propafenone for 30 min. After termination, the product 5-OH-propafenone was extracted and used for signal collection by ultra-performance liquid chromatography (UPLC). Compared with wild type CYP2D6.1, the intrinsic clearance (V_max and K_m) values of all variants were significantly altered. Three variants (CYP2D6.87, CYP2D6.90, CYP2D6.F219S) exhibited markedly increased intrinsic clearance values (129% to 165%), whereas 21 variants exhibited significantly decreased values (16% to 85%) due to increased K_m and (or) decreased V_max values. These results indicated that the majority of tested alleles had significantly altered catalytic activity towards propafenone hydroxylation in this expression system. Attention should be paid to subjects carrying these rare alleles when treated with propafenone.

Elevated urban temperatures are a significant concern across the globe due to their negative health effects and increased energy use. Understanding the spatial variation in urban air temperatures can lead to informed mitigation and planning efforts. Air temperatures for multiple urban areas in the state of Iowa, USA, at three times of the day, were collected using customized sensors mounted on vehicles driven through a variety of landscapes in each urban area. Geographic information systems technology was used to process high-resolution landscape datasets and derive variables that summarize the urban landscape surrounding each temperature measurement point. Five different statistical models: standard regression, trend surface, geostatistical, time series, and random forest, were fitted to nighttime data in the Waterloo–Cedar Falls urban area. We demonstrate that the best method for predicting Waterloo–Cedar Falls nighttime data is to use Waterloo–Cedar Falls data collected at a different time of day. However, when data are not available in the same city for which predicted air temperatures are needed, we explore which substitute city’s data best forecast the target city’s air temperature, via four cross-validation strategies. We find that, when predicting evening and nighttime air temperatures for the Iowa urban areas, choosing the closest-in-population-size substitute city provides the best predicted air temperatures.