Pigment Index Research Articles

Estimating leaf photosynthesis of C3 plants grown under different environments from pigment index, photochemical reflectance index, and chlorophyll fluorescence.

Photosynthetic rates vary depending on growth conditions, even within species. Remote sensing techniques have a great potential to predict the photosynthetic rates of leaves with different characteristics. Here, we demonstrate that the photosynthetic rates of leaves acclimated to different light and nutrient conditions can be estimated based on the chlorophyll fluorescence (ChlF), the photochemical reflectance index (PRI), and a chlorophyll index. Chenopodium album plants were grown under different light and nutrient conditions. PRI, ChlF parameters, and CO2/H2O gas exchange rates of leaves were simultaneously determined under the various light and CO2 conditions. PRI was used to assess non-photochemical quenching (NPQ), but the relationship between NPQ and PRI was weakened when the data on leaves grown under different conditions were pooled, because PRI in darkness ([Formula: see text]) changed with the leaf pigment composition. Among 15 pigment indices, we found that [Formula: see text], a reflectance index related to the leaf chlorophyll content, had the best correlation with [Formula: see text] ([Formula: see text]) across the studied leaves, and the correction of PRI by [Formula: see text] improved the predictability of NPQ ([Formula: see text]). Using the steady-state ChlF, the NPQ estimated from PRI and [Formula: see text], and the stomatal conductance coefficient, we calculated the CO2 assimilation rates, which were strongly correlated with the actual rates (RMSE = 4.85 [Formula: see text]mol m[Formula: see text] s[Formula: see text]), irrespective of growth conditions. Our approach has the potential to contribute to amore accurate estimation of photosynthetic rates in remote sensing. However, further studies on species variations and connecting with radiative transfer models are needed to demonstrate this at the canopy scale.

Monitoring the Vertical Distribution of Maize Canopy Chlorophyll Content Based on Multi-Angular Spectral Data

Remote sensing approaches have several advantages over traditional methods in determining information on physical and chemical parameters, including timely data acquisition, low costs, and wide coverage. Thus, remote sensing is widely used in crop growth monitoring. Unlike vertical observations, multi-angular remote sensing technology can obtain the vertical distribution information of the central and lower leaves of a crop. Furthermore, applications of remote sensing on the vertical distribution of maize canopy components is complicated, and related research is limited. In the current paper, we employed multi-angular spectral data, measured by a self-designed multi-angular observation instrument at view zenith angles (VZAs) of 0°, 10°, 20°, 30°, 40°, 50°, and 60°, to explore the monitoring strategy and monitoring precision of the vertical distribution of chlorophyll content in the maize canopy. This was then used to determine the optimal monitoring method for the chlorophyll content (soil and plant analyzer development (SPAD) value) of each layer. The correlation between SPAD value and chlorophyll sensitivity indices at different growth stages was used as the basis for screening indices and VZAs. The correlation between the selected EPI (eucalyptus pigment index) and REIP (red edge inflection point) indices and chlorophyll content indicated view zenith angles (VZAs) of 0°, 30°, and 40° as optimal for the early growth stage monitoring of chlorophyll content in the 1st, 2nd, and 3rd layers, respectively. These values were associated with RMSEs of 4.14, 1.71, and 1.11 for EPI, respectively; and 4.61, 2.31, and 1.00 for REIP, respectively. In addition, a VZA of 50° was selected to monitor the chlorophyll content of the 1st, 2nd, 3rd, and 4th layers at the late growth stage, with RMSE values of 2.97, 3.50, 2.80, and 4.80 for EPI, respectively; and 3.16, 5.02, 4.55, and 7.85 for REIP, respectively. The results demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the maize canopy chlorophyll content vertical distribution, with the VZAs of different vertical layers varying between the early and late growth stages.

Estimation of SPAD value in waterlogged winter wheat based on characteristic indices of hyperspectral and digital image.

To explore the optimal monitoring method for soil and plant analyzer development (SPAD) of winter wheat under waterlogging stress based on hyperspectral and digital image techno-logy, the correlations between SPAD of the waterlogged winter wheat and fifteen indices of hyperspectral characteristic and fourteen indices of digital image feature were analyzed under a micro-plot which could be irrigated and drainage separately. Then, the BP neural network models for SPAD estimation were constructed based on the optimal monitoring feature indices. Compared with the normal winter wheat, SPAD and the value of hyperspectral reflectance did not change under short-term waterlogging (less than 7 d), whereas the SPAD was significantly decreased after more than 12 d waterlogging treatment with the value being close to zero at the late stage of growth. The estimation accuracy based on the digital image characteristics of green minus red, excess red index, norma-lized redness index and excess green index showed similar results compared to that using the BP network model based on the characteristics of the corresponding hyperspectral band. The highest R2 between the measured value and the predicted value was 0.86, while the root mean square error (RMSE) was 3.98. Compared with the BP network models built with the digital image feathers, the accuracy of the models based on the four hyperspectral characteristic indices (carotenoid reflex index, yellow edge amplitude, normalized difference vegetation index and structure insensitive pigment index) for SPAD was significantly improved, with the highest R2 of 0.97 and the lowest RMSE of 1.95. Our results suggest that both hyperspectral and digital image technology could be used to estimate SPAD value of waterlogged winter wheat and that the BP network model based on hyperspectral characteristic indices performed better in the estimation accuracy.

Analysis of Anti-interference Ability of Hyperspectral Sensitive Features to Wheat Powdery Mildew

The development of ground-based, airborne and spaceborne remote sensing has greatly facilitated the identification and diagnosis of various objects. Corresponding algorithms and methods of removing interference from remotely sensed imagery have been proposed. Nevertheless, the studies on anti-interference ability of selected features have not been fully considered. In our study, the hyperspectral reflectance of leaf-scale powdery mildew (Erysiphe graminis) on winter wheat were collected as the testing dataset. A total of seven representative spectral features of Landsat-8 Operational Land Imager (OLI) and GaoFen-1 Wide-Field-View (WFV) was selected, namely, original blue, green, red, near-infrared (NIR) bands and normalized difference vegetation index (NDVI), normalized difference greenness index (NDGI), structure insensitive pigment index (SIPI). Four hyperspectral vegetation indices including red edge (MSR) simple ratio index, NDVI, green band and SIPI were also selected. Three primary background noises including soil, cloud and white poplar (Populus alba L.) were added into the spectral signal. The correlation coefficient (R) between disease severities (0, 1, 2, 3 and 4) and spectral features was used to estimate the anti-interference ability. The results show that there is a generally similar spectral performance for the two sensors, but Landsat-8 OLI is superior to GF-1 WVF in terms of spectral response. The green band was greatly affected with the R values decreasing from 0.77 to 0.35. The MSR and NDVI showed a gradual decrease with the increase of three background noises. The study shows that background noises must be removed when acquiring spectral data and stable spectral features should be also selected by evaluating the anti-interference ability.

Gingival depigmentation with diode and Er,Cr:YSGG laser: evaluating re-pigmentation rate and patient perceptions.

To evaluate the efficiency of depigmentation, patient perceptions, and the recurrence rates of physiological gingival pigments during a 2-year follow-up after ablative depigmentation using two laser wavelengths: diode 940 nm and Er,Cr:YSGG 2780 nm. Sixty patients exhibiting physiological melanin hyperpigmentation in the buccal maxillary gingiva were randomly divided into two equal groups treated with an Er,Cr:YSGG laser at 2780 nm, and a 940 nm diode laser, respectively. The depigmentation procedure essentially involves the ablation of epithelial tissue exhibiting melanin pigmentation. The intensity and extensity indices of gingival pigments were evaluated at baseline, 1 month, 1 year, and 2 years. At all time points following depigmentation treatment, oral pigmentation index (OPI) and melanin pigmentation index (MPI) scores were less significantly (p <0.05) compared to the baseline in both groups. Treatment was significantly faster with Er,Cr:YSGG laser and required no anesthesia, with faster healing and less postoperative discomfort after 1-week of treatment, compared to the diode laser treatment (p <0.001). The re-pigmentation intensity and extensity were higher significantly in the Er,Cr:YSGG group than in the diode group at 1 year and 2 years (p <0.05). Both lasers efficiently removed gingival pigments with comparable clinical outcomes and overall positive patient experience. Diode laser treatment exhibited better long-term stability of gingival color, with a lower incidence of re-pigmentation. The color of the gingiva plays an important role in the esthetics of oral soft tissues and the overall ideal smile. Laser-assisted gingival depigmentation is an effective, comfortable, and reliable technique with good esthetical outcomes. The rate of re-pigmentation was affected by the laser wavelength and the technique used.

The Potential of Apulian Olive Biodiversity: The Case of Oliva Rossa Virgin Olive Oil.

In this study, the drupes and virgin olive oils extracted from the Oliva Rossa landrace are characterized. Oliva Rossa is an old landrace part of the autochthonous Apulian olive germplasm for which only few data have been reported till now. During the study, the maturity patterns of the drupes had been followed. Four samplings per year were planned, one every 14 days starting from the middle of October. The pigmentation index, the oil content and the total phenolic content of the drupes were measured. Simultaneously, virgin olive oils were extracted at the lab scale and analyzed for the fatty acid composition, the basic quality parameters and the content of minor compounds. The pigmentation pattern of the drupes was different among the years and, despite this trend, at the third sampling time the stage of maximum oil accumulation was always over. The extracted virgin olive oils had a medium to high level of oleic acid. With colder temperatures, a higher level of monounsaturated fatty acids, oleic/linoleic ratio and antioxidants was observed. The phenolic profile was dominated by 3,4-DPHEA-EDA and p-HPEA-EDA while the volatile profile by (E)-2-hexenal and 3-ethyl-1,5-octadiene.

Modelling vegetation health and stress using hypersepctral remote sensing data

Vegetation health mapping is extremely helpful for distinguishing nuisance and scourge conditions and it is additionally valuable in surveying zones of lumber gather. Decline in forest health occurs due to many reasons, including land-use intensification, forest disturbances and bad management practices at various scale that can be assessed and monitored using geoinformatics. Among various remote sensing (RS) techniques, the use of hyperspectral band information suits more for vegetation stress and health analysis and possesses better accuracy. Besides, RS gives lot of opportunity to monitor and manage natural resources at multi-temporal, multi-spectral and multi-spatial level. In this study, we evaluated the applications of hyperspectral RS for forest health assessment and mapping in terms of vegetation health analysis, stress and fire fuel vegetation analysis and linked these to forest management. We analysed the vegetation health of the Lonar forest (Maharashtra) using hyperspectral imagery using ENVI software for the management of forest and its conservation in terms of healthy and stressed vegetation using different indices. The background studies suggest that a forest displaying low pressure conditions comprise sound vegetation, although a forest under high pressure conditions gives the indication of dry and kicking the bucket plant material, exceptionally thick and inadequate overhang and wasteful light use. We examined different vegetation models to compare and identify the index which resulted showing the best accuracy. We executed six Vegetation Index models (VIM) i.e. Enhanced Vegetation Index (EVI), Sum Green Index (SGI), Moisture Stress Index (MSI), Structure Insensitive Pigment Index (SIPI), Atmospherically Resistant Vegetation Index (ARVI) and Normalized Difference Vegetation Index (NDVI) for evaluation of the timberland strength of the woods locale. The study suggest that each vegetation model index has its specific expression for healthy and stressed vegetation and its own suitability for specific uses and also some limiting factors. We argue on the importance to select an appropriate vegetation model to evaluate vegetation health and stress that must be based on environmental conditions, forest composition, leaf type and other biophysical factors.

Detecting Biophysical Characteristics and Nitrogen Status of Finger Millet at Hyperspectral and Multispectral Resolutions

Finger millet (Eleusine coracana Gaertn L.) is an important grain crop for small farmers in many countries. Reliable estimates of crop parameters, such as crop growth and nitrogen (N) content, through remote sensing techniques can improve in-season management of finger millet. This study investigated the relationships of hyperspectral reflectance with canopy height, green canopy cover, leaf area index (LAI), and N concentrations of finger millet using an optimal waveband selection procedure with partial least square regression (PLSR). Predictive performance of 13 vegetation indices (VIs) computed from the original hyperspectral data as well as synthesized Landsat-8 and Sentinel-2 data were evaluated and compared for estimating various crop parameters with simple linear regression (SLR) and multilinear regression (MLR) models. The optimal wavebands determined by PLSR were mostly concentrated within 1,000–1,100 nm for both LAI and dry biomass but were scattered for other canopy parameters. The SLR statistics resulted in the simple ratio pigment index (SRPI) and red/green index (RGI) performing best when predicting LAI (R2v = 0.53–0.59) and canopy cover (R2v = 0.72–0.76). The blue/green index (BGI1) was strongly related to canopy height (R2v = 0.65–0.78), dry biomass (R2v = 0.42–0.49), and N concentration (R2v = 0.70–0.83) of finger millet, regardless of spectral resolutions. The MLR approach, using four maximum VIs as input variables, improved the prediction accuracy of N concentration by 14% compared to both SLR and waveband selection methods. VIs computed from synthesized Landsat-8 and Sentinel-2 satellite data resulted in similar or greater prediction accuracy than hyperspectral data for various canopy parameters of finger millet, indicating publicly accessible multispectral data could serve as alternative to hyperspectral data for improved crop management decisions via precision agriculture.

Assessment of dwarf mistletoe (Arceuthobium sichuanense) infection in spruce trees by using hyperspectral data

SummaryDwarf mistletoe (Arceuthobiumspp.,Viscaceae) (DM), a hemiparasitic plant, is nutritionally dependent upon its host tree and causes tree mortality in forests worldwide. Dwarf mistletoe infection results in physiological changes in the host, eventually causing the death of the host tree. In this study, we evaluated the changes in leaf reflectance of spruce trees,Picea crassifolia, infected by DM (Arceuthobium sichuanense) with ground‐based hyperspectral technology. A total of 112 sample sites were selected for analysis in the eastern part of Qinghai Province, China, including healthy spruce tree, light DM‐infected spruce tree, moderate DM‐infected spruce tree and high DM‐infected spruce tree in each site. The results showed that leaf reflectance was significantly different among healthy spruce, DM‐infected spruce, DM and dead spruce in the near‐infrared regions ranging from ~750 to 900 nm. Dwarf mistletoe infection resulted in the increase of leaf reflectance of host tree, and the intensity of DM infection was positively related to reflectance. However, five selected hyperspectral indices that commonly used to estimate the physiological status of plants (carotenoid reflectance index, photochemical reflectance index, normalized difference vegetation index, structure‐insensitive pigment index and water index) were not linearly correlated with the intensity of DM infection. Collectively, the hyperspectral characteristics of spruce infected by DM can be potentially used to detect DM‐induced changes on a landscape scale with satellite remote sensing, which can be used as an efficient advanced tool for DM disease monitoring.

Аналіз характеристик та обґрунтування індексів рослинності

In this article there has been given the analyzing of non-fiction sources in order to find out the popular indexes of the vegetable control parameters. There are such parameters as chlorophyll presence, water containing, the efficiency of a light use, nitrogen and carbon presence, foliage pigmentation.&nbsp; The chlorophyll presence control is conducted by such indexes as the difference vegetation index, the global environmental monitoring index, the infrared percentage vegetation index, the modified simple ratio, the modified Soil adjusted vegetation index 2, the non-linear index, the normalized difference vegetation index, the optimized soil adjusted vegetation index, the renormalized difference vegetation index, the soil adjusted vegetation index, the simple ratio etc. The control of water content can be based on the moisture stress index, the normalized difference infrared index, the normalized difference water index, the normalized multi-band drought index, the water band index. There is the photochemical reflectance index, the structure insensitive pigment index, the red green ratio index for the efficiency control of the light use. The nitrogen presence is controlled by the normalized differential nitrogen index. The carbon presence can be detected by the implementing of cellulose absorption index, the lignin cellulose absorption index, the normalized difference lignin index, the plant senescence reflectance index. The foliage pigmentations can be controlled by the means of the anthocyanin reflectance index 1, the anthocyanin reflectance index 2, the carotenoid reflectance index 1, and the carotenoid reflectance index 2. The specificity of the presented indexes is described; the calculation formulas and its main straits are proposed.

Near UV-Vis and NMR Spectroscopic Methods for Rapid Screening of Antioxidant Molecules in Extra-Virgin Olive Oil.

Several spectroscopic techniques have been optimized to check extra-virgin olive oil quality and authenticity, as well as to detect eventual adulterations. These methods are usually complementary and can give information about different olive oil chemical components with bioactive and antioxidant properties. In the present work, a well-characterized set of extra-virgin olive oil (cultivar Frantoio) samples from a specific area of Tuscany (Italy) were investigated by combining near UV-Vis absorption spectroscopy, 1H and 13C nuclear magnetic resonance (NMR) to identify and quantify different chemical components, such as pigments, secoiridoids and squalene, related to the nutritional and quality properties of olive oils. Moreover, the pigmentation index of olives, organoleptic and sensory properties, total phenolic compound contents and the lipidic fractions of olive oils were investigated. The results obtained are, finally, compared and discussed in order to correlate several properties of both olives and olive oils with specific features of the cultivation area.

A Validation Experiment of the Reflectance Products of KOMPSAT-3A Based on RadCalNet Data and Its Applicability to Vegetation Indexing

Surface reflectance products obtained through the absolute atmospheric correction of multispectral satellite images are useful for precise scientific applications. For broader applications, the reflectance products computed using high-resolution images need to be validated with field measurement data. This study dealt with 2.2-m resolution Korea Multi-Purpose Satellite (KOMPSAT)-3A images with four multispectral bands, which were used to obtain top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The open-source Orfeo Toolbox (OTB) extension was used to generate these products. Next, these were subsequently validated by considering three sites (i.e., Railroad Valley Playa, NV, USA (RVUS), Baotou, China (BTCN), and La Crau, France (LCFR)) in RadCalNet, as well as a calibration and validation portal for remote sensing. We conducted the validations comparing satellite image-based reflectance products and field measurement reflectance based on data sets acquired at different times. The experimental results showed that the overall trend of validation accuracy of KOPSAT-3A was well fitted in all the RadCalNet sites and that the accuracy remained quite constant. Reflectance bands showing the minimum and maximum differences between the sets of experimental data are presented in this paper. The vegetation indices (i.e., the atmospherically resistant vegetation index (ARVI) and the structure insensitive pigment index (SIPI)) and three TOC reflectance bands obtained from KOMPSAT-3A were computed as a case study and used to achieve a detailed vegetation interpretation; finally, the correspondent results were compared with those obtained from Landsat-8 images (downloaded from the Google Earth Engine (GEE)). The validation and the application scheme presented in this study can be potentially applied to the generation of analysis ready data from high-resolution satellite sensor images.

Visible and near-infrared spectroscopy for detection of powdery mildew in Cucurbita pepo L. leaves

Cucurbits plants are very susceptible to fungal diseases as powdery mildew (PM) infection. Currently, the PM early detection in the field is the cutting-edge research. The objectives of our study were to assess visible and near-infrared spectroscopy of normal and infected leaves for early detection of PM in Cucurbita pepo L. plants. Samples for spectral analysis were taken three days a week (Monday, Wednesday, and Friday) after the first true leaves appeared. The reflectance spectra of leaves were collected using an HR4000CG-UV-NIR spectrometer (Ocean Optics) with a fiber optics probe. Vegetative indices were used for discrimination between infected and healthy plants. The calculated vegetation indices (green normalized difference vegetation index, triangular greenness index, single-band reflectance index, simple ratio indices, and anthocyanins reflectance index) showed the highest sensitivity to differentiate healthy, infected plants at a symptomless stage, first symptoms, and diseased plants. The best prediction on early detection in sampling days were structure-independent pigment index and red-edge reflection point with an R of 0.211 and 0.1893, respectively. Our study shows the efficacy of the identification based on reflectance spectra for an early distinction of PM disease and could be used in cucurbits plants with similar characteristics on leaves.

Efficacy of 980 nm diode laser and 2940 nm Er: YAG laser in gingival depigmentation: A comparative study.

Gingival hyper-pigmentation is an esthetics problem in patients with excessive gingival display. Laser ablation has been suggested recently as the most effective, pleasant, and reliable technique for gingival depigmentation compared to blade surgery. The present study aimed to identify and compare the effectiveness of two types of lasers, Er:YAG laser-2940nm and Diode laser-980nm, in gingival depigmentation in the maxilla and mandible. 20 participants, 16 males, and 4 females, whose ages ranged between 18 and 35years, were recruited using simplified random sampling. Oral pigmentation was measured at pre- and two postoperative follow-ups, 1month, and 6months, according to the oral pigmentation index. Furthermore, the study assessed postoperative intensity of pain using the Visual Analogue Scale, bleeding and healing process in both groups. There was a significant decrease in the oral pigmentation index scores, 1month (0.90±0.64) and 6months (1.55±0.60) after the operation (P<.001). No significant differences were detected between the two study groups regarding the mean ranking on the oral pigmentation index pre-operatively (P=.547), 1month after the operation (P=.154), and 6months after the operation (P=.831). The Er:YAG laser and Diode laser both significantly reduced gingival pigmentation with no significant differences between the effectiveness of the two types of laser in treating gingival hyper-pigmentation.

Comparative Analysis of Chlorophyll a Content in Freshwater and Marine Waterbodies

The data on chlorophyll content in the water and bottom sediments in freshwater Rybinsk Reservoir (Upper Volga) and Aniva Sea Bay (Sea of Okhotsk) are summarized. The average concentration of chlorophyll a in plankton of the reservoir is 23.0 ± 1.9 μg/L, in the bay 0.77 ± 0.06 μg/L, in the bottom sediments 144.2 ± 14.9, and 8.2 ± 0.7 μg/g in the dry sediment, respectively. Correlations between pigment concentrations in the euphotic zone, in the aphotic bottom layer, the whole column of water, and the upper layer of bottom sediments, as well as the relationship of pigments with depth (within 6–100 m) and transparency (from 1 to 18 m), were revealed. It is shown that the general relations for two waterbodies are approximated by nonlinear equations with determination coefficient values (R2) from 0.37 to 0.93. The patterns of the relationship between pigment indices are similar in both waterbodies.

Thermal melanism explains macroevolutionary variation of dorsal pigmentation in Eurasian vipers

Colouration may endorse thermoregulatory and antipredatory functions in snakes. The thermal melanism hypothesis predicts that dark-coloured individuals are ecologically favoured in cool climates. However, the loss of aposematic and cryptic colourations may imply high predation for melanistic snakes. Here, we used the monophyletic group of Eurasian vipers (subfamily Viperinae) to test whether an increase in the extent of dark area inside the characteristic zigzag dorsal pattern is associated to colder environments. We measured two colouration traits in zigzag-patterned individuals (number of dorsal marks and weighted pigmentation index) and used a phylogenetic comparative approach to explore macroevolutionary patterns of dorsal pigmentation and test whether its extent is associated to ecogeographic characteristics of lineages’ ranges. Phylogenetically-naïve and phylogenetically-informed analyses yielded a significant association between the degree of pigmentation of the zigzag pattern and environmental variables such as solar radiation, elevation and latitude. The degree of pigmentation of the zigzag pattern is highlighted as an adaptive trait that matches range attributes mirroring cold environments irrespective of the phylogeny. These results constitute the first large-scale evidence supporting the thermal melanism hypothesis in snakes, opening new avenues of inquiry for the mechanisms that shape the evolution of colour phenotypes.

Response of phytoplankton to heavy pollution of water bodies

Abstract Phytoplankton structure was studied in five ponds (located in the Oleksandriya Nature Park, Ukraine), which significantly differed in the level of their contamination. The concentration of ammonium (NH4 +-N) in the studied water bodies was 0.02–74.00 mg l−1, of nitrite (NO2 −-N) – 0.002–1.750 mg l−1, nitrate (NO3 −-N) – 0.13–58.00 mg l−1, inorganic compounds of phosphorus (Pinorg) – 0.041–0.160 mg l−1, chloride (Cl−) – 35.4–560.5 mg l−1, whereas the content of organic matter (PO and DO) – 4.4–10.4 and 18.0–81.0 mg O l−1, respectively. It has been found that the response of phytoplankton to heavy pollution consisted in changes in its species richness, taxonomic structure, species composition, quantitative indices, numbers and biomass structure, dimensional structure, pigment index, dominant complex, as well as changes in its ecological spectrum. Individual divisions of algae differed in their response to heavy pollution of water bodies, which was conditioned by the specificity of algae metabolism. Bacillariophyta and Cyanoprokaryota proved to be more sensitive to contamination, whereas Chlorophyta and Euglenophyta – more tolerant. The obtained data can be used to monitor the status of water bodies and their biota and to determine the type and intensity of contamination.

Phytoplankton pigment indices and pigment-based phytoplankton size classes along the south-eastern Black Sea

Phytoplankton size classes (PSCs) estimated by using high-performance liquid chromatography (HPLC) pigment data and pigment indices in the south-eastern Black Sea were used to characterise seasonal phytoplankton structure from November 2015 to August 2016. Results revealed significant seasonal variation in phytoplankton size classes hence community composition and pigment indices along the south-eastern Black Sea, varied from 2% to 71%; from 3% to 77%; from 5% and 91% for picophytoplankton, nanophytoplankton and microphytoplankton, respectively. Phytoplankton community composition was dominated by microphytoplankton with a high content of photosynthetic carotenoids. In-situ Chl-a concentrations changed during the study period, the highest concentration was detected in autumn (2.60 μg.l−1) and spring (2.48 μg.l−1). Pigment indices indicated that microphytoplankton carrying majority of photosynthetic carotenoids (PSCTP) dominated the community along the south-eastern Black Sea. PSCTP positively correlated with MicroDP, indicated that microphytoplankton seasonally optimise their light-harvesting capability along the south-eastern Black Sea. However, concomitant increase in PPCTPwith PicoDP and dominancy of PPCTP in summer indicated enhancing photoprotective carotenoid content during warm and nutrient depleted periods along the study area. Canonical correspondence analysis (CCA) explained general pattern of PSCs along the study area are controlled seasonally by physicochemical drivers (i.e. nutrient dynamics and pigment content). The differences detected phytoplankton community composition also indicated phytoplankton adaptive strategy to changing environmental factors.

Observations of surface water phytoplankton community in the Indian Ocean: A transect from tropics to polar latitudes

Phytoplankton, the primary producers in all aquatic systems, plays an important role in key biogeochemical processes that are linked to higher trophic levels and climate variability. The present study deals with the phytoplankton community structure in the Indian Ocean, particularly in the higher latitudes with respect to environmental variables to understand the region specific dominant community and its governing environmental settings. The study areas were selected along the latitudinal transect between 3oN and 53oS (northern Indian Ocean to Indian Ocean sector of Southern Ocean). The surface water phytoplankton community based on microscopy coupled with diagnostic pigment indices showed marked variation in community structure from tropical to polar latitudes of the Indian Ocean. The Prokaryotic diagnostic pigment (ProkDP) dominated in the Equatorial and South Equatorial regions, the Flagellate diagnostic pigment (FlagDP) in the North Equatorial region (NER), Southern Tropical Indian Ocean and Sub-tropical Front (STF) region whereas, the Diatom diagnostic pigment (DiatDP) dominated only at the Polar Front (PF) region. The influence of a suite of environmental variables - temperature, nutrients, salinity and mixed layer depth (MLD), on the dominant phytoplankton groups at the STF and PF was observed. This understanding of community dominance from this poorly explored area with respect to influencing factors is very vital baseline information to design the perturbation experiments in future work to understand the phytoplankton process studies of each region.

The comparison of some depigmentation techniques towards reccurence of gingival hyperpigmentation: a systematic review

Objective: To compare the best gingival depigmentation technique toward the recurrence of gingival hyperpigmentation. Methods: PubMed and Wiley Online Library were conducted to identify articles from January 2013 to November 2018 about the techniques of depigmentation gingiva in patient with gingiva hyperpigmentation. After the selection of articles, 121 articles were obtained but only 3 articles meet the requirements. Dummett oral pigmentation index (DOPI) and Hedin melanin index (HMI) were used to compare the techniques of gingival depigmentation. Results: The clinical parameters have shown reduction of the pigmentation from a patient, in 1, 3 and 6 months. In the second article showed the DOPI only in baseline and 6 months. In third article showedthe DOPI only in baseline and 1 month. Conclusion: The conventional surgical technique eliminates all the hyperpigmentation and showed the less recurrence.