Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery

Natural protected areas are vital for biodiversity, climate change mitigation, and supporting ecological processes. Despite their significance, comprehensive mapping is hindered by a lack of understanding of their characteristics and a missing land cover class definition. This paper aims to advance the explanation of the designating patterns forming protected and wild areas. To this end, we propose a novel framework that uses activation maximization and a generative adversarial model. With this, we aim to generate satellite images that, in combination with domain knowledge, are capable of offering complete and valid explanations for the spatial and spectral patterns that define the natural authenticity of these regions. Our proposed framework produces more precise attribution maps pinpointing the designating patterns forming the natural authenticity of protected areas. Our approach fosters our understanding of the ecological integrity of the protected natural areas and may contribute to future monitoring and preservation efforts

Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery

Wilderness areas offer important ecological and social benefits and therefore warrant monitoring and preservation. Yet, the characteristics of wilderness are little known, making the detection and monitoring of wilderness areas via remote sensing techniques a challenging task. We explore the appearance and characteristics of the vague concept of wilderness via multispectral satellite imagery. For this, we apply a novel explainable machine learning technique to a dataset consisting of wild and anthropogenic areas in Fennoscandia. With our technique, we predict continuous, detailed, and high-resolution sensitivity maps of unseen remote sensing data for wild and anthropogenic characteristics. Our neural network provides an interpretable activation space in which regions are semantically arranged regarding these characteristics and certain land cover classes. Interpretability increases confidence in the method and allows for new explanations of the investigated concept. Our model advances explainable machine learning for remote sensing, offers opportunities for comprehensive analyses of existing wilderness, and has practical relevance for conservation efforts

[18F]-Fluorodeoxyglucose-positron emission tomography in rats with prolonged cocaine self-administration suggests potential brain biomarkers for addictive behavior

The DSM5-based dimensional diagnostic approach defines substance use disorders on a continuum from recreational drug use to habitual and ultimately addicted behavior. Biomarkers that are indicative of recreational drug use and addicted behavior are lacking. We performed a translational [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) study in the multi-dimensional 0/3crit model of cocaine addiction. Addict-like (3crit) and non-addict-like (0crit) rats, which shared identical life conditions and levels of cocaine self-administration, were acquired for FDG-PET under baseline conditions and following cocaine and yohimbine challenges. Compared to cocaine-naïve control rats, 0crit animals showed higher glucose uptake in the caudate putamen (CPu) and medial prefrontal cortex (mPFC) respect to naïve controls. 3crit animals did not show this adaptive higher glucose utilization, but had lower uptake in several cortical areas. Both cocaine and yohimbine challenges affected glucose uptake in control rats in several brain sites, but not in 0crit and 3crit rats, indicating that impaired glucose mobilization in response to these challenges is not specifically associated with addictive behavior. Compared to 0crit, 3crit rats showed higher reinstatement responses, which were negatively associated with glucose uptake in the ventral tegmental area. Data indicate that cocaine non-addict- and addict-like phenotypes are associated with several potential biomarkers. Specifically, we propose that increased glucose uptake in the CPu and mPFC is a function of controlled drug use, whereas a loss of striatal and prefrontal metabolic activity and reduced uptake in cortical areas are indicative of addictive behavior

Guanosine nucleotides regulate B2 kinin receptor affinity of agonists but not of antagonists: Discussion of a model proposing receptor precoupling to G protein

The effect of nucleotides on binding of the B2 kinin (BK) receptor agonist {[}H-3]BK and the antagonist {[}H-3]NPC17731 to particulate fractions of human foreskin fibroblasts was studied. At 0 degrees C, particulate fractions exhibited a single class of binding sites with a Kd of 2.3 nM for {[}H-3]BK and a K-d Of 3.8 nM for the antagonist {[}H-3]NPC17731. Incubation with radioligands at 37 degrees C for 5 min gave a reduction of agonist, as well as antagonist, binding that was between 0-40% depending on the preparation, even in the absence of guanosine nucleotides. As shown by Scatchard analysis, this reduction in specific binding was due to a shift in the affinity of at least a fraction of the receptors. The presence at 37 degrees C of the guanine nucleotides GTP, GDP and their poorly hydrolyzable analogs left {[}H-3]-NPC17731 binding unaffected, but reduced the receptor affinity for {[}H-3]BK to a K-d Of about 15 nM. The maximal number of receptors, however, was unchanged. This affinity change was strongly dependent on the presence of bivalent cations, in particular Mg2+. It was reversed by incubation at 0 degrees C, The rank order of the guanosine nucleotides for {[}H-3]BK binding reduction was GTP{[}gamma S] = Gpp{[}NH]p > GTP = GDP > GDP{[}beta S]. GMP, ATP, ADP and AMP showed no influence on agonist binding. A model for the interaction of the B2 kinin receptor with G proteins is discussed

Initial steps for high-throughput phenotyping in vineyards

The evaluation of phenotypic characters of grapevines is required directly in vineyards and is strongly limited by time, costs and the subjectivity of person in charge. Sensor-based techniques are prerequisite in order to allow non-invasive phenotyping of individual plant traits, to increase the quantity of object records and to reduce error variation. Thus, a Prototype-Image-Acquisition-System (PIAS) was developed for semi-automated capture of geo-referenced images in an experimental vineyard. Different strategies were tested for image interpretation using MATLAB®. The interpretation of images from the vineyard with real background is more practice-oriented but requires the calculation of depth maps. Different image analysis tools were verified in order to enable contactless and non-invasive detection of bud burst and quantification of shoots at an early developmental stage (BBCH 10) and enable fast and accurate determination of the grapevine berry size at BBCH 89. Depending on the time of image acquisition at BBCH 10 up to 94 % of green shoots were visible in images. The mean berry size (BBCH 89) was recorded non-invasively with a precision of 1 mm.

Metabolomics Unravel Contrasting Effects of Biodiversity on the Performance of Individual Plant Species

In spite of evidence for positive diversity-productivity relationships increasing plant diversity has highly variable effects on the performance of individual plant species, but the mechanisms behind these differential responses are far from being understood. To gain deeper insights into the physiological responses of individual plant species to increasing plant diversity we performed systematic untargeted metabolite profiling on a number of herbs derived from a grassland biodiversity experiment (Jena Experiment). The Jena Experiment comprises plots of varying species number (1, 2, 4, 8, 16 and 60) and number and composition of functional groups (1 to 4; grasses, legumes, tall herbs, small herbs). In this study the metabolomes of two tall-growing herbs (legume: Medicago x varia; non-legume: Knautia arvensis) and three small-growing herbs (legume: Lotus corniculatus; non-legumes: Bellis perennis, Leontodon autumnalis) in plant communities of increasing diversity were analyzed. For metabolite profiling we combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and UPLC coupled to FT-ICR-MS (LC-FT-MS) analyses from the same sample. This resulted in several thousands of detected m/z-features. ANOVA and multivariate statistical analysis revealed 139 significantly changed metabolites (30 by GC-TOF-MS and 109 by LC-FT-MS). The small-statured plants L. autumnalis, B. perennis and L. corniculatus showed metabolic response signatures to increasing plant diversity and species richness in contrast to tall-statured plants. Key-metabolites indicated C- and N-limitation for the non-leguminous small-statured species B. perennis and L. autumnalis, while the metabolic signature of the small-statured legume L. corniculatus indicated facilitation by other legumes. Thus, metabolomic analysis provided evidence for negative effects of resource competition on the investigated small-statured herbs that might mechanistically explain their decreasing performance with increasing plant diversity. In contrast, taller species often becoming dominant in mixed plant communities did not show modified metabolite profiles in response to altered resource availability with increasing plant diversity. Taken together, our study demonstrates that metabolite profiling is a strong diagnostic tool to assess individual metabolic phenotypes in response to plant diversity and ecophysiological adjustment

Relationships between ecosystem functions vary among years and plots and are driven by plant species richness

Ecosystem management aims at providing many ecosystem services simultaneously. Such ecosystem service multifunctionality can be limited by tradeoffs and increased by synergies among the underlying ecosystem functions (EF), which need to be understood to develop targeted management. Previous studies found differences in the correlation between EFs. We hypothesised that correlations between EFs are variable even under the controlled conditions of a field experiment and that seasonal and annual variation, plant species richness, and plot identity (identity effects of plots, such as the presence and proportion of functional groups) are drivers of these correlations. We used data on 31 EFs related to plants, consumers, and physical soil properties that were measured over 5 to 19 years, up to three times per year, in a temperate grassland experiment with 80 different plots, constituting six sown plant species richness levels (1, 2, 4, 8, 16, 60 species). We found that correlations between pairs of EFs were variable, and correlations between two particular EFs could range from weak to strong or negative to positive correlations among the repeated measurements. To determine the drivers of pairwise EF correlations, the covariance between EFs was partitioned into contributions from species richness, plot identity, and time (including years and seasons). We found that most of the covariance for synergies was explained by species richness (26.5%), whereas for tradeoffs, most covariance was explained by plot identity (29.5%). Additionally, some EF pairs were more affected by differences among years and seasons, showing a higher temporal variation. Therefore, correlations between two EFs from single measurements are insufficient to draw conclusions on tradeoffs and synergies. Consequently, pairs of EFs need to be measured repeatedly under different conditions to describe their relationships with more certainty and be able to derive recommendations for the management of grasslands