Investigating the Potential of UAV-Based Low-Cost Camera Imagery for Measuring Biophysical Variables in Maize

The potential for improved crop productivity is readily investigated in agronomic field experiments. Frequent measurements of biophysical crop variables are necessary to allow for confident statements on crop performance. Commonly, in-field measurements are tedious, labour-intensive, costly and spatially selective and therefore pose a challenge in field experiments. With the versatile, flexible employment of the platform and the high spatial and temporal resolution of the sensor data, Unmanned Aerial Vehicle (UAV)-based remote sensing offers the possibility to derive variables quickly, contactless and at low cost. This thesis examined if UAV-borne modified low-cost camera imagery allowed for remote estimation of the crop variables green leaf area index (gLAI) and radiation use efficiency (RUE) in a maize field trial under different management influences. For this, a field experiment was established at the university's research station Campus Klein-Altendorf southwest of Bonn in the years 2015 and 2016. In four treatments (two levels of nitrogen fertilisation and two levels of plant density) with five repetitions each, leaf growth of maize plants was supposed to occur differently. gLAI and biomass was measured destructively, UAV-based data was acquired in 14-day intervals over the entire experiment. Three studies were conducted and submitted for peer-review in international journals. In study I, three selected spectral vegetation indices (NDVI, GNDVI, 3BSI) were related to the gLAI measurements. Differing but definite relationships per treatment factor were found. gLAI estimation using the two-band indices (NDVI, GNDVI) yielded good results up to gLAI values of 3. The 3-bands approach (3BSI) did not provide improved accuracies. Comparing gLAI results to the spectral vegetation indices, it was determined that sole reliance on these was insufficient to draw the right conclusions on the impact of management factors on leaf area development in maize canopies. Study II evaluated parametric and non-parametric regression methods on their capability to estimate gLAI in maize, relying on UAV-based low-cost camera imagery with non-plants pixels (i.e. shaded and illuminated soil background) a) included in and b) excluded from the analysis. With regard to the parametric regression methods, all possible band combinations for a selected number of two- and three-band formulations as well as different fitting functions were tested. With regard to non-parametric methods, six regression algorithms (Random Forests Regression, Support Vector Regression, Relevance Vector Machines, Gaussian Process Regression, Kernel Regularized Least Squares, Extreme Learning Machine) were tested. It was found that all non-parametric methods performed better than the parametric methods, and that kernel-based algorithms outperformed the other tested algorithms. Excluding non-plant pixels from the analysis deteriorated models' performances. When using parametric regression methods, signal saturation occurred at gLAI values of about 3, and at values around 4 when employing non-parametric methods. Study III investigated if a) UAV-based low-cost camera imagery allowed estimating RUEs in different experimental plots where maize was cultivated in the growing season of 2016, b) those values were different from the ones previously reported in literature and c) there was a difference between RUEtotal and RUEgreen. Fractional cover and canopy reflectance was determined based on the RS imagery. Our study showed that RUEtotal ranges between 4.05 and 4.59, and RUEgreen between 4.11 and 4.65. These values were higher than those published in other research articles, but not outside the range of plausibility. The difference between RUEtotal and RUEgreen was minimal, possibly due to prolonged canopy greenness induced by the stay-green trait of the cultivar grown. In conclusion, UAV-based low-cost camera imagery allows for estimation of plant variables within a range of limitations

Commensal-Related Changes in the Epidermal Barrier Function Lead to Alterations in the Benzo[a]Pyrene Metabolite Profile and Its Distribution in 3D Skin

Polycyclic aromatic hydrocarbons (PAH) such as benzo[a]pyrene (B[a]P) are among the most abundant environmental pollutants, resulting in continuous exposure of human skin and its microbiota. However, effects of the latter on B[a]P toxicity, absorption, metabolism, and distribution in humans remain unclear. Here, we demonstrate that the skin microbiota does metabolize B[a]P on and in human skin in situ, using a recently developed commensal skin model. In this model, microbial metabolism leads to high concentrations of known microbial B[a]P metabolites on the surface as well as in the epidermal layers. In contrast to what was observed for uncolonized skin, B[a]P and its metabolites were subject to altered rates of skin penetration and diffusion, resulting in up to 58% reduction of metabolites recovered from basal culture medium. The results indicate the reason for this altered behavior to be a microbially induced strengthening of the epidermal barrier. Concomitantly, colonized models showed decreased formation and penetration of the ultimate carcinogen B[a]P-7,8-dihydrodiol-9,10-epoxide (BPDE), leading, in consequence, to fewer BPDE-DNA adducts being formed. Befittingly, transcript and expression levels of key proteins for repairing environmentally induced DNA damage such as xeroderma pigmentosum complementation group C (XPC) were also found to be reduced in the commensal models, as was expression of B[a]P-associated cytochrome P450-dependent monooxygenases (CYPs). The results show that the microbiome can have significant effects on the toxicology of external chemical impacts. The respective effects rely on a complex interplay between microbial and host metabolism and microbe-host interactions, all of which cannot be adequately assessed using single-system studies. IMPORTANCE Exposure to xenobiotics has repeatedly been associated with adverse health effects. While the majority of reported cases relate to direct substance effects, there is increasing evidence that microbiome-dependent metabolism of xenobiotic substances likewise has direct adverse effects on the host. This can be due to microbial biotransformation of compounds, interaction between the microbiota and the host’s endogenous detoxification enzymes, or altered xenobiotic bioavailability. However, there are hardly any studies addressing the complex interplay of such interactions in situ and less so in human test systems. Using a recently developed microbially competent three-dimensional (3D) skin model, we show here for the first time how commensal influence on skin physiology and gene transcription paradoxically modulates PAH toxicity

Nudged Elastic Band calculation of the binding potential for liquids at interfaces

The wetting behavior of a liquid on solid substrates is governed by the nature of the effective interaction between the liquid-gas and the solid-liquid interfaces, which is described by the binding or wetting potential $g(h)$ which is an excess free energy per unit area that depends on the liquid film height $h$. Given a microscopic theory for the liquid, to determine $g(h)$ one must calculate the free energy for liquid films of any given value of $h$; i.e. one needs to create and analyze out-of-equilibrium states, since at equilibrium there is a unique value of $h$, specified by the temperature and chemical potential of the surrounding gas. Here we introduce a Nudged Elastic Band (NEB) approach to calculate $g(h)$ and illustrate the method by applying it in conjunction with a microscopic lattice density functional theory for the liquid. We show too that the NEB results are identical to those obtained with an established method based on using a fictitious additional potential to stabilize the non-equilibrium states. The advantages of the NEB approach are discussed.Comment: 5 pages, 2 figure

Microbially competent 3D skin: a test system that reveals insight into host–microbe interactions and their potential toxicological impact

The skin`s microbiome is predominantly commensalic, harbouring a metabolic potential far exceeding that of its host. While there is clear evidence that bacteria-dependent metabolism of pollutants modulates the toxicity for the host there is still a lack of models for investigating causality of microbiome-associated pathophysiology or toxicity. We now report on a biologically characterised microbial–skin tissue co-culture that allows studying microbe–host interactions for extended periods of time in situ. The system is based on a commercially available 3D skin model. In a proof-of-concept, this model was colonised with single and mixed cultures of two selected skin commensals. Two different methods were used to quantify the bacteria on the surface of the skin models. While Micrococcus luteus established a stable microbial–skin tissue co-culture, Pseudomonas oleovorans maintained slow continuous growth over the 8-day cultivation period. A detailed skin transcriptome analysis showed bacterial colonisation leading to up to 3318 significant changes. Additionally, FACS, ELISA and Western blot analyses were carried out to analyse secretion of cytokines and growth factors. Changes found in colonised skin varied depending on the bacterial species used and comprised immunomodulatory functions, such as secretion of IL-1α/β, Il-6, antimicrobial peptides and increased gene transcription of IL-10 and TLR2. The colonisation also influenced the secretion of growth factors such as VFGFA and FGF2. Notably, many of these changes have already previously been associated with the presence of skin commensals. Concomitantly, the model gained first insights on the microbiome’s influence on skin xenobiotic metabolism (i.e., CYP1A1, CYP1B1 and CYP2D6) and olfactory receptor expression. The system provides urgently needed experimental access for assessing the toxicological impact of microbiome-associated xenobiotic metabolism in situ

Stabilizing Soybean Production in Northeast Texas with Early Planting of Early-Maturing Soybean Varieties.

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An investigation of a genomewide supported psychosis variant in ZNF804A and white matter integrity in the human brain

ZNF804A, a genomewide supported susceptibility gene for schizophrenia and bipolar disorder, has been associated with task-independent functional connectivity between the left and right dorsolateral prefrontal cortices. Several lines of evidence have converged on the hypothesis that this effect may be mediated by structural connectivity. We tested this hypothesis using diffusion tensor magnetic resonance imaging in three samples: one German sample of 50 healthy individuals, one Scottish sample of 83 healthy individuals and one Scottish sample of 84 unaffected relatives of bipolar patients. Voxel-based analysis and tract-based spatial statistics did not detect any fractional anisotropy (FA) differences between minor allele carriers and individuals homozygous for the major allele at rs1344706. Similarly, region-of-interest analyses and quantitative tractography of the genu of the corpus callosum revealed no significant FA differences between the genotype groups. Examination of effect sizes and confidence intervals indicated that this negative finding is very unlikely to be due to a lack of statistical power. In summary, despite using various analysis techniques in three different samples, our results were strikingly and consistently negative. These data therefore suggest that it is unlikely that the effects of genetic variation at rs1344706 on functional connectivity are mediated by structural integrity differences in large, long-range white matter fiber connections

Web-based mindfulness and skills-based distress reduction for patients with cancer: study protocol of the multicentre, randomised, controlled confirmatory intervention trial Reduct

IntroductionMany patients with cancer experience severe psychological distress, but as a result of various barriers, few of them receive psycho-oncological support. E-mental health interventions try to overcome some of these barriers and the limitation of healthcare offers, enabling patients with cancer to better cope with psychological distress. In the proposed trial, we aim to assess the efficacy and cost-effectiveness of the manualised e-mental health intervention Make It Training- Mindfulness-Based and Skills-Based Distress Reduction in Oncology. Make It Training is a self-guided and web-based psycho-oncological intervention, which includes elements of cognitive behavioural therapy, mindfulness-based stress reduction and acceptance and commitment therapy. The training supports the patients over a period of 4 months. We expect the Make It Training to be superior to treatment as usual optimised (TAU-O) in terms of reducing distress after completing the intervention (T1, primary endpoint).Methods and analysisThe study comprises a multicentre, prospective, randomised controlled confirmatory interventional trial with two parallel arms. The proposed trial incorporates four distinct measurement time points: the baseline assessment before randomisation, a post-treatment assessment and 3 and 6 month follow-up assessments. We will include patients who have received a cancer diagnosis in the past 12 months, are in a curative treatment setting, are 18–65 years old, have given informed consent and experience high perceived psychological distress (Hospital Anxiety and Depression Scale ≥13) for at least 1 week. Patients will be randomised into two groups (Make It vs TAU-O). The aim is to allocate 600 patients with cancer and include 556 into the intention to treat analysis. The primary endpoint, distress, will be analysed using a baseline-adjusted ANCOVA for distress measurement once the intervention (T1) has been completed, with study arm as a binary factor, baseline as continuous measurement and study centre as an additional categorical covariate.Ethics and disseminationThe Ethics Committee of the Medical Faculty Essen has approved the study (21-10076-BO). Results will be published in peer-reviewed journals, conference presentations, the project website, and among self-help organisations.Trial registration numberGerman Clinical Trial Register (DRKS); DRKS-ID: DRKS00025213

The cross-sectional GRAS sample: A comprehensive phenotypical data collection of schizophrenic patients

<p>Abstract</p> <p>Background</p> <p>Schizophrenia is the collective term for an exclusively clinically diagnosed, heterogeneous group of mental disorders with still obscure biological roots. Based on the assumption that valuable information about relevant genetic and environmental disease mechanisms can be obtained by association studies on patient cohorts of ≥ 1000 patients, if performed on detailed clinical datasets and quantifiable biological readouts, we generated a new schizophrenia data base, the GRAS (Göttingen Research Association for Schizophrenia) data collection. GRAS is the necessary ground to study genetic causes of the schizophrenic phenotype in a 'phenotype-based genetic association study' (PGAS). This approach is different from and complementary to the genome-wide association studies (GWAS) on schizophrenia.</p> <p>Methods</p> <p>For this purpose, 1085 patients were recruited between 2005 and 2010 by an invariable team of traveling investigators in a cross-sectional field study that comprised 23 German psychiatric hospitals. Additionally, chart records and discharge letters of all patients were collected.</p> <p>Results</p> <p>The corresponding dataset extracted and presented in form of an overview here, comprises biographic information, disease history, medication including side effects, and results of comprehensive cross-sectional psychopathological, neuropsychological, and neurological examinations. With >3000 data points per schizophrenic subject, this data base of living patients, who are also accessible for follow-up studies, provides a wide-ranging and standardized phenotype characterization of as yet unprecedented detail.</p> <p>Conclusions</p> <p>The GRAS data base will serve as prerequisite for PGAS, a novel approach to better understanding 'the schizophrenias' through exploring the contribution of genetic variation to the schizophrenic phenotypes.</p

Towards Remote Estimation of Radiation Use Efficiency in Maize Using UAV-Based Low-Cost Camera Imagery

Radiation Use Efficiency (RUE) defines the productivity with which absorbed photosynthetically active radiation (APAR) is converted to plant biomass. Readily used in crop growth models to predict dry matter accumulation, RUE is commonly determined by elaborate static sensor measurements in the field. Different definitions are used, based on total absorbed PAR (RUEtotal) or PAR absorbed by the photosynthetically active leaf tissue only (RUEgreen). Previous studies have shown that the fraction of PAR absorbed (fAPAR), which supports the assessment of RUE, can be reliably estimated via remote sensing (RS), but unfortunately at spatial resolutions too coarse for experimental agriculture. UAV-based RS offers the possibility to cover plant reflectance at very high spatial and temporal resolution, possibly covering several experimental plots in little time. We investigated if (a) UAV-based low-cost camera imagery allowed estimating RUEs in different experimental plots where maize was cultivated in the growing season of 2016, (b) those values were different from the ones previously reported in literature and (c) there was a difference between RUEtotal and RUEgreen. We determined fractional cover and canopy reflectance based on the RS imagery. Our study found that RUEtotal ranges between 4.05 and 4.59, and RUEgreen between 4.11 and 4.65. These values are higher than those published in other research articles, but not outside the range of plausibility. The difference between RUEtotal and RUEgreen was minimal, possibly due to prolonged canopy greenness induced by the stay-green trait of the cultivar grown. The procedure presented here makes time-consuming APAR measurements for determining RUE especially in large experiments superfluous