scholarly journals Inhibition of Primary Photosynthesis in Desiccating Antarctic Lichens Differing in Their Photobionts, Thallus Morphology, and Spectral Properties

2021 ◽  
Vol 9 (4) ◽  
pp. 818
Author(s):  
Miloš Barták ◽  
Josef Hájek ◽  
Alla Orekhova ◽  
Johana Villagra ◽  
Catalina Marín ◽  
...  
Keyword(s):  
Spectral Reflectance ◽  
Quantum Yields ◽  
Specific Response ◽  
Variable Fluorescence ◽  
Protective Mechanisms ◽  
Quenching Analysis ◽  
Relative Water ◽  
Species Specific ◽  
Kinetics Of ◽  
Reflectance Data

Five macrolichens of different thallus morphology from Antarctica (King George Island) were used for this ecophysiological study. The effect of thallus desiccation on primary photosynthetic processes was examined. We investigated the lichens’ responses to the relative water content (RWC) in their thalli during the transition from a wet (RWC of 100%) to a dry state (RWC of 0%). The slow Kautsky kinetics of chlorophyll fluorescence (ChlF) that was recorded during controlled dehydration (RWC decreased from 100 to 0%) and supplemented with a quenching analysis revealed a polyphasic species-specific response of variable fluorescence. The changes in ChlF at a steady state (Fs), potential and effective quantum yields of photosystem II (FV/FM, ΦPSII), and nonphotochemical quenching (NPQ) reflected a desiccation-induced inhibition of the photosynthetic processes. The dehydration-dependent fall in FV/FM and ΦPSII was species-specific, starting at an RWC range of 22–32%. The critical RWC for ΦPSII was below 5%. The changes indicated the involvement of protective mechanisms in the chloroplastic apparatus of lichen photobionts at RWCs of below 20%. In both the wet and dry states, the spectral reflectance curves (SRC) (wavelength 400–800 nm) and indices (NDVI, PRI) of the studied lichen species were measured. Black Himantormia lugubris showed no difference in the SRCs between wet and dry state. Other lichens showed a higher reflectance in the dry state compared to the wet state. The lichen morphology and anatomy data, together with the ChlF and spectral reflectance data, are discussed in relation to its potential for ecophysiological studies in Antarctic lichens.

scholarly journals Enhancing the Quality of Two Species of Baby Leaves Sprayed with Moringa Leaf Extract as Biostimulant

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1399
Author(s):  
Stefania Toscano ◽  
Antonio Ferrante ◽  
Ferdinando Branca ◽  
Daniela Romano
Keyword(s):  
Leaf Extract ◽  
Foliar Application ◽  
Sugar Content ◽  
Nitrate Content ◽  
Specific Response ◽  
Yield And Quality ◽  
Negative Effect ◽  
Species Specific ◽  
Moringa Oleifera Lam

Natural biostimulants obtained by plants are intensively used nowadays to improve crop yield and quality. The current study aimed to evaluate the effects of leaf extract of moringa (Moringa oleifera Lam.) (MLE) in modifying baby leaf characteristics of two genotypes of Brassica. The trial was started in October 2020 in a greenhouse; a cultivar of kale ‘Cavolo Laciniato Nero di Toscana’ (CL) and a Sicilian landrace of sprouting broccoli ‘Broccoli Nero’ (BN) were used. The plants, after 15, 30 and 40 days from sowing, were treated with MLE, while the control plants (C) with distilled water. Treatment with MLE modified morphological and nutritional value, but with different behavior in the two genotypes. In fact, in BN the treatment reduced the antioxidant activity (2.2-diphenyl-1-picrylhydrazyl (DPPH)) by 54%, while in CL the treatment increased this parameter by 40%. For the phenolic concentration and the sugar content the values recorded were significantly increased by MLE compared to control plants in CL, where in BN a significant reduction was registered. The CL plants treated with MLE showed a significant reduction (−70%) in nitrate content compared to the control plants; a negative effect was, instead, observed in BN, where the plants treated with moringa showed an increase of 60%. Results of this study showed how the foliar application of MLE was effective in improving various nutraceutical parameters, in particular in kale, because it appears to be a species-specific response.

scholarly journals Feasibility Study of Transparency Estimation in Lake Nakaumi by SGLI using in-situ Spectral Reflectance Data

2009 ◽  
Vol 65 (1) ◽  
pp. 1046-1050
Author(s):  
Yuji SAKUNO ◽  
Yasushi MIYAMOTO ◽  
Toshiaki KOZU ◽  
Toyoshi SHIMOMAI ◽  
Tsuneo MATSUNAGA ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Spectral Reflectance ◽  
Lake Nakaumi ◽  
Reflectance Data

scholarly journals Synthesis of New Antibiotics Derivatives by the Photocatalytic Method: A Screening Research

Catalysts ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1102
Author(s):  
Wojciech Baran ◽  
Ewa Masternak ◽  
Dominika Sapińska ◽  
Andrzej Sobczak ◽  
Ewa Adamek
Keyword(s):  
Quantum Yield ◽  
Biologically Active ◽  
Quantum Yields ◽  
Photocatalytic Process ◽  
Experimental Conditions ◽  
New Antibiotics ◽  
Photocatalytic Reactions ◽  
Kinetics Of ◽  
Derivatives Of ◽  
Uva Radiation

The aim of our study was to assess the possibility of using the photocatalytic process conducted in the presence of TiO2 to obtain new stable derivatives of antibacterial drugs. The possibility of introducing hydroxyl, chlorine, or bromide groups into antibiotics molecules was investigated. The experiments were conducted in aqueous solutions in the presence of TiO2-P25 as a photocatalyst, Cl− and Br- ions, and antibiotics belonging to eight different chemical classes. All experiments were initiated by UVa radiation. The kinetics of photocatalytic reactions and their quantum yield were determined, and the stable products were identified. All of the antibiotics used in the experiments underwent a photocatalytic transformation, and the quantum yields were in the range from 0.63 to 22.3%. The presence of Br- or FeCl3 significantly increased the efficiency of the photocatalytic process performed in the presence of TiO2, although Br- ion also acted as an inhibitor. Potentially biologically active chlorine derivatives from Trimethoprim, Metronidazole, Chloramphenicol, and bromine derivatives from Trimethoprim, Amoxicillin were obtained under experimental conditions. The potentially inactive halogen derivatives of Sulfamethoxazole and hydroxyl derivatives described in the literature were also identified.

Resilience of cold-water coral holobionts to thermal stress

2021 ◽  
Vol 288 (1965) ◽  
Author(s):  
Leila Chapron ◽  
Pierre E. Galand ◽  
Audrey M. Pruski ◽  
Erwan Peru ◽  
Gilles Vétion ◽  
...  
Keyword(s):  
Global Warming ◽  
Cold Water ◽  
Physiological Activity ◽  
Seawater Temperature ◽  
Extreme Temperature ◽  
Specific Response ◽  
Lophelia Pertusa ◽  
Thermal Limit ◽  
Species Specific ◽  
Response To Temperature

Cold-water corals are threatened by global warming, especially in the Mediterranean Sea where they live close to their upper known thermal limit (i.e. 13°C), yet their response to rising temperatures is not well known. Here, temperature effects on Lophelia pertusa and Madrepora oculata holobionts (i.e. the host and its associated microbiome) were investigated. We found that at warmer seawater temperature (+2°C), L. pertusa showed a modification of its microbiome prior to a change in behaviour, leading to lower energy reserves and skeletal growth, whereas M. oculata was more resilient. At extreme temperature (+4°C), both species quickly lost their specific bacterial signature followed by lower physiological activity prior to death. In addition, our results showing the holobionts' negative response to colder temperatures (−3°C), suggest that Mediterranean corals live close to their thermal optimum. The species-specific response to temperature change highlights that global warming may affect dramatically the main deep-sea reef-builders, which would alter the associated biodiversity and related ecosystem services.

scholarly journals Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks

Foods ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 558 ◽  
Author(s):  
Yoshio Makino ◽  
Yumi Kousaka
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectral Reflectance ◽  
Spatial Information ◽  
Chlorophyll Concentration ◽  
Noninvasive Technique ◽  
Green Color ◽  
Artificial Neural ◽  
A Site ◽  
Reflectance Data

Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 × 10−5 mg·g−1·d−1. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads.

scholarly journals Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Zhe Xu ◽  
Xiaomin Zhao ◽  
Xi Guo ◽  
Jiaxin Guo
Keyword(s):  
Organic Matter ◽  
Deep Learning ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Near Infrared Spectroscopy ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Spectral Variation ◽  
Full Spectrum ◽  
Reflectance Data

Deep learning is characterized by its strong ability of data feature extraction. This method can provide unique advantages when applying it to visible and near-infrared spectroscopy for predicting soil organic matter (SOM) content in those cases where the SOM content is negatively correlated with the spectral reflectance of soil. This study relied on the SOM content data of 248 red soil samples and their spectral reflectance data of 400–2450 nm in Fengxin County, Jiangxi Province (China) to meet three objectives. First, a multilayer perceptron and two convolutional neural networks (LeNet5 and DenseNet10) were used to predict the SOM content based on spectral variation and variable selection, and the outcomes were compared with that from the traditional back-propagation neural network (BPN). Second, the four methods were applied to full-spectrum modeling to test the difference to selected feature variables. Finally, the potential of direct modeling was evaluated using spectral reflectance data without any spectral variation. The results of prediction accuracy showed that deep learning performed better at predicting the SOM content than did the traditional BPN. Based on full-spectrum data, deep learning was able to obtain more feature information, thus achieving better and more stable results (i.e., similar average accuracy and far lower standard deviation) than those obtained through variable selection. DenseNet achieved the best prediction result, with a coefficient of determination (R2) = 0.892 ± 0.004 and a ratio of performance to deviation (RPD) = 3.053 ± 0.056 in validation. Based on DenseNet, the application of spectral reflectance data (without spectral variation) produced robust results for application-level purposes (validation R2 = 0.853 ± 0.007 and validation RPD = 2.639 ± 0.056). In conclusion, deep learning provides an effective approach to predict the SOM content by visible and near-infrared spectroscopy and DenseNet is a promising method for reducing the amount of data preprocessing.

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