Analysis and use of neural networks as a tool for a rapid non-invasive estimation

Water deficit is one of the most important environmental factors limiting sustainable crop yields and it requires a reliable tool for fast and precise quantification. In this work we use simultaneously recorded signals of photoinduced prompt fluorescence (PF) and delayed fluorescence (DF) as well as modulated reflection (MR) of light at 820 nm for analysis of the changes in the photosynthetic activity in detached bean leaves during drying. Depending on the severity of the water deficit we identify different changes in the primary photosynthetic processes. When the relative water content (RWC) is decreased to 60% there is a parallel decrease in the ratio between the rate of excitation trapping in the Photosystem (PS) II reaction center and the rate of reoxidation of reduced PSII acceptors. A further decrease of RWC to 20% suppresses the electron transfer from the reduced plastoquinone pool to the PSI reaction center. At RWC below values 15%, the reoxidation of the photoreduced primary quinone acceptor of PSII, QA–, is inhibited and at less than 5%, the primary photochemical reactions in PSI and II are inactivated. Using the collected sets of PF, DF and MR signals, we construct and train an artificial neural network, capable of recognizing the RWC in a series of “unknown” samples with a correlation between calculated and gravimetrically determined RWC values of about R2 ≈ 0.98. Our results demonstrate that this is a reliable method for determination of RWC in detached leaves and after further development it could be used for quantifying of drought stress of crop plants in situ. This article is part of a Special Issue entitled: Photosynthesis Research for Sustainability: from Natural to Artificial

Sulfides of the Modern Kamchatka Hydrothermal Systems

ABSTRACT Sulfides pyrite, melnikovite-pyrite, marcasite, sphalerite, chalcopyrite, galena, cinnabar, coloradoite, metacinnabar are precipitating at the modern geothermal systems of Kamchatka: Kireunsky, Dvukhyurtochny and Apapel'sky in Central Kamchatka, Vilyuchinsky and Mutnovsky in Southern Kamchatka. Ore deposits are spatially associated with hydrothermal springs. Pyrite is the most common mineral precipitated at the discharge of hydrothermal style. It varies in mode of occurrence, size, inner structure, chemical composition and microstructure. Frequently pyrite occurs as framboids, idiomorphic crystals and their aggregates. By chemical composition, two varieties of pyrite are observed: homogeneous and heterogeneous. Heterogeneity of composition is due to impurities of As, Cu, Sb, Hg and Ag. Au as impurity in pyrite was relieved only in pyrite from Voinovsky hot springs in Southern Kamchatka. Cinnabar is the next most common occurring mineral at the modern hydrothermal systems in Kamchatka. Chalcopyrite, galena, sphalerite and gold are rare minerals. The modern hydrothermal systems in Kamchatka provide the opportunity to study sulfide typomorphism and physico-chemical conditions of the deposition mechanism. We suppose that some of them are the elements of the long-life ore generating hydrothermal systems

Drought-induced modifications of photosynthetic electron transport in intact leaves: Analysis and use of neural networks as a tool for a rapid non-invasive estimation

AbstractWater deficit is one of the most important environmental factors limiting sustainable crop yields and it requires a reliable tool for fast and precise quantification. In this work we use simultaneously recorded signals of photoinduced prompt fluorescence (PF) and delayed fluorescence (DF) as well as modulated reflection (MR) of light at 820nm for analysis of the changes in the photosynthetic activity in detached bean leaves during drying. Depending on the severity of the water deficit we identify different changes in the primary photosynthetic processes. When the relative water content (RWC) is decreased to 60% there is a parallel decrease in the ratio between the rate of excitation trapping in the Photosystem (PS) II reaction center and the rate of reoxidation of reduced PSII acceptors. A further decrease of RWC to 20% suppresses the electron transfer from the reduced plastoquinone pool to the PSI reaction center. At RWC below values 15%, the reoxidation of the photoreduced primary quinone acceptor of PSII, QA–, is inhibited and at less than 5%, the primary photochemical reactions in PSI and II are inactivated. Using the collected sets of PF, DF and MR signals, we construct and train an artificial neural network, capable of recognizing the RWC in a series of “unknown” samples with a correlation between calculated and gravimetrically determined RWC values of about R2≈0.98. Our results demonstrate that this is a reliable method for determination of RWC in detached leaves and after further development it could be used for quantifying of drought stress of crop plants in situ. This article is part of a Special Issue entitled: Photosynthesis Research for Sustainability: from Natural to Artificial