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Author(s): B. Sönnichsen (corresponding author) [1]; L. B. Koski [1, 6]; A. Walsh [1]; P. Marschall [1, 6]; B. Neumann [1, 6]; M. Brehm [1]; A.-M. Alleaume [1, 6]; J. [...]

The atypical analgesic tramadol has strong structural similarities to the antidepressant venlafaxine and is a mixed noradrenaline (NA) and serotonin (5-HT) uptake inhibitor. Because tramadol has been found active in the forced swim test, a common predictor of antidepressant efficacy, we therefore examined the effects of chronic tramadol on various pre- and post-synaptic monoamine measures. Male Wistar rats (150-200 g) received tramadol (20 mg/kg i.p.) or vehicle for 21 days and were sacrificed 24 h after the last dose. Quantitative autoradiography revealed that specific frontocortical [3H]dihydroalprenolol and [3H]ketanserin binding was lower in the chronic tramadol group than controls (beta: 37+/-8 and 217+/-56 fmol/mg; 5-HT2A: 23+/-3 and 44+/-7 fmol/mg, respectively, p < 0.05). Chronic tramadol had no effect on the magnitude of electrically stimulated noradrenaline (NA) efflux or uptake in locus coeruleus (LC) slices. Although dexmedetomidine (10 nM) decreased LC NA efflux equally (by approximately 60%) in chronic tramadol and vehicle groups, desipramine (50 nM) increased LC NA efflux more in vehicle (to 164+/-7%) than tramadol-treated rats (144+/-6%; p < 0.05). Chronic tramadol had no effect on dorsal raphé (DRN) or median raphé (MRN) 5-HT efflux. However, 5-HT uptake in tramadol-treated rats was slower (p < 0.05) in MRN and nearly so (p = 0.055) in DRN. The selective 5-HT1A agonist 8-OH-DPAT reduced 5-HT efflux in both DRN and MRN. Its effect in DRN was greater in rats given chronic tramadol than in vehicle controls (54+/-2 versus 32+/-6% reduction in 5-HT efflux, respectively). In conclusion, we suggest that tramadol has many of the pre- and postsynaptic neurochemical features of a conventional antidepressant, as might be predicted from its pharmacology. [ABSTRACT FROM AUTHOR]

Infrared thermography offers a rapid, noninvasive method for measuring plant temperature, which provides a proxy for stomatal conductance and plant water status and can therefore be used as an index for plant stress. Thermal imaging can provide an efficient method for high-throughput screening of large numbers of plants. This chapter provides guidelines for using thermal imaging equipment and illustrative methodologies, coupled with essential considerations, to access plant physiological processes., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Hyperspectral imaging is a remote sensing technique that enables remote, noninvasive measurement of plant traits. Here, we outline the procedures for camera setup, scanning, and calibration, along with the acquisition of black and white reference materials, which are the key steps in collecting hyperspectral imagery. We also discuss the development of predictive models such as partial least-squares regression, using both large and small datasets, which are used to predict plant traits from hyperspectral data. To ensure practical applicability, we provide code examples that allow readers to immediately implement these techniques in real-world scenarios. We introduce these topics to beginners in an accessible and understandable manner., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

This chapter provides a methodology for evaluating plant health and leaf characteristics using spectral reflectance. It provides a step-by-step guide to using spectrometers for high-resolution point measurements of leaf spectral reflectance and multispectral imaging for capturing spatial data, emphasizing the importance of consistent measurement conditions. The chapter further explores the intricacies of multispectral imaging, including calibration, data collection, and image processing. Finally, this chapter delves into the application of various spectral indices for the quantification of key traits such as pigment content, the status of the xanthophyll cycle, water content, and how to identify spectral regions of interest for further research and development. Serving as a guide for researchers and practitioners in plant science, this chapter provides a straightforward framework for plant health assessment using spectral reflectance., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)