[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (113)

Search Parameters:
Keywords = leaf colour

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
21 pages, 3805 KiB  
Article
Embedment of Biosynthesised Silver Nanoparticles in PolyNIPAAm/Chitosan Hydrogel for Development of Proactive Smart Textiles
by Dominika Glažar, Danaja Štular, Ivan Jerman, Barbara Simončič and Brigita Tomšič
Nanomaterials 2025, 15(1), 10; https://doi.org/10.3390/nano15010010 - 25 Dec 2024
Viewed by 478
Abstract
A smart viscose fabric with temperature and pH responsiveness and proactive antibacterial and UV protection was developed. PNCS (poly-(N-isopropylakrylamide)/chitosan) hydrogel was used as the carrier of silver nanoparticles (Ag NPs), synthesised in an environmentally friendly manner using AgNO3 and a sumac leaf [...] Read more.
A smart viscose fabric with temperature and pH responsiveness and proactive antibacterial and UV protection was developed. PNCS (poly-(N-isopropylakrylamide)/chitosan) hydrogel was used as the carrier of silver nanoparticles (Ag NPs), synthesised in an environmentally friendly manner using AgNO3 and a sumac leaf extract. PNCS hydrogel and Ag NPs were applied to the viscose fabric by either in situ synthesis of Ag NPs on the surface of viscose fibres previously modified with PNCS hydrogel, or by the direct immobilisation of Ag NPs by the dehydration/hydration of the PNCS hydrogel with the nanodispersion of Ag NPs in the sumac leaf extract and subsequent application to the viscose fibres. Compared to the pre-functionalised PNCS application method, the in situ functionalisation imparted much higher concentration of Ag NPs on the fibres, colouring the samples brown to brown-green. These samples showed more than 90% reduction in the test bacteria E. coli and S. aureus and provided excellent UV protection. In this case, the PNCS hydrogel acted as a reservoir for Ag NPs, whose release was based on a diffusion-controlled mechanism. Despite the Ag NPs decreasing the responsiveness of the PNCS hydrogel, the moisture management was still preserved in the modified samples. Accordingly, the PNCS hydrogel is a suitable carrier for biosynthesized Ag NPs to tailor the protective smart surface of viscose fibres. Full article
(This article belongs to the Special Issue Antimicrobial and Antioxidant Activity of Nanoparticles)
Show Figures

Figure 1

Figure 1
<p>A schematic presentation of viscose fabric modification with PNCS hydrogel functionalised via in situ synthesis of Ag NPs (<b>a</b>) and via the direct application of a functionalised PNCS hydrogel with previously embedded Ag NPs (<b>b</b>).</p>
Full article ">Figure 2
<p>(<b>a</b>) SEM images of untreated and modified samples at 3 K magnification; (<b>b</b>) EDS spectra of modified CV_5Ag, CV_PNCS/5Ag, and CV_PNCS + 5Ag samples, with corresponding SEM/BSE images as insets; (<b>c</b>) Ag concentration in the analysed samples; (<b>d</b>) IR-ATR spectra of the analysed samples.</p>
Full article ">Figure 3
<p>(<b>a</b>) Photo images of the untreated and studied modified samples with corresponding CIE L*a*b* values; (<b>b</b>) colour change (ΔE<sub>ab</sub>*) of the studied modified samples; (<b>c</b>) colour strength (K/S) spectra of the untreated and studied modified samples.</p>
Full article ">Figure 4
<p>(<b>a</b>) The moisture content (MC) of the untreated and modified samples, and (<b>b</b>) the swelling ratio of the PNCS hydrogel triggered by temperature change (S<sub>T</sub>); (<b>c</b>) the water uptake (WU) of the untreated and modified samples with the (<b>d</b>) swelling ratio of the PNCS hydrogel triggered by pH change (S<sub>pH</sub>).</p>
Full article ">Figure 5
<p>(<b>a</b>) The growth reduction of bacteria <span class="html-italic">E. coli</span> and <span class="html-italic">S. aureus</span> in contact with the studied samples; (<b>b</b>) studied bacteria colonies grown on the agar plates after being in contact with the CV_N and CV_PNCS/1Ag samples; (<b>c</b>) the inhibition zone formed around the studied CV_PNCS/xAg and CV_PNCS + xAG samples after incubation at 20 and 37 °C; (<b>d</b>) silver (Ag) release from the CV_1Ag and CV_PNCS/1Ag samples.</p>
Full article ">Figure 6
<p>(<b>a</b>) UV transmission and (<b>b</b>) reflection spectra of untreated and studied modified samples.</p>
Full article ">
21 pages, 6478 KiB  
Article
Assessment of Dataset Scalability for Classification of Black Sigatoka in Banana Crops Using UAV-Based Multispectral Images and Deep Learning Techniques
by Rafael Linero-Ramos, Carlos Parra-Rodríguez, Alexander Espinosa-Valdez, Jorge Gómez-Rojas and Mario Gongora
Drones 2024, 8(9), 503; https://doi.org/10.3390/drones8090503 - 19 Sep 2024
Viewed by 1906
Abstract
This paper presents an evaluation of different convolutional neural network (CNN) architectures using false-colour images obtained by multispectral sensors on drones for the detection of Black Sigatoka in banana crops. The objective is to use drones to improve the accuracy and efficiency of [...] Read more.
This paper presents an evaluation of different convolutional neural network (CNN) architectures using false-colour images obtained by multispectral sensors on drones for the detection of Black Sigatoka in banana crops. The objective is to use drones to improve the accuracy and efficiency of Black Sigatoka detection to reduce its impact on banana production and improve the sustainable management of banana crops, one of the most produced, traded, and important fruits for food security consumed worldwide. This study aims to improve the precision and accuracy in analysing the images and detecting the presence of the disease using deep learning algorithms. Moreover, we are using drones, multispectral images, and different CNNs, supported by transfer learning, to enhance and scale up the current approach using RGB images obtained by conventional cameras and even smartphone cameras, available in open datasets. The innovation of this study, compared to existing technologies for disease detection in crops, lies in the advantages offered by using drones for image acquisition of crops, in this case, constructing and testing our own datasets, which allows us to save time and resources in the identification of crop diseases in a highly scalable manner. The CNNs used are a type of artificial neural network widely utilised for machine training; they contain several specialised layers interconnected with each other in which the initial layers can detect lines and curves, and gradually become specialised until reaching deeper layers that recognise complex shapes. We use multispectral sensors to create false-colour images around the red colour spectra to distinguish infected leaves. Relevant results of this study include the construction of a dataset with 505 original drone images. By subdividing and converting them into false-colour images using the UAV’s multispectral sensors, we obtained 2706 objects of diseased leaves, 3102 objects of healthy leaves, and an additional 1192 objects of non-leaves to train classification algorithms. Additionally, 3640 labels of Black Sigatoka were generated by phytopathology experts, ideal for training algorithms to detect this disease in banana crops. In classification, we achieved a performance of 86.5% using false-colour images with red, red edge, and near-infrared composition through MobileNetV2 for three classes (healthy leaves, diseased leaves, and non-leaf extras). We obtained better results in identifying Black Sigatoka disease in banana crops using the classification approach with MobileNetV2 as well as our own datasets. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
Show Figures

Figure 1

Figure 1
<p>Study area map, showing the location of the five banana plantations in Magdalena Department in Colombia, within the municipalities of Zona Bananera and El Retén where data acquisition with UAV was conducted.</p>
Full article ">Figure 2
<p>DJI Phantom 4 drone integrates six 1/2.9″ CMOS sensors, including one RGB sensor for visible light imaging and five monochrome sensors for multispectral imaging, covering the blue, green, red, red edge, and near-infrared bands [<a href="#B25-drones-08-00503" class="html-bibr">25</a>].</p>
Full article ">Figure 3
<p>(<b>A</b>) Before the alignment of images (with disparity and blurred) based on the SIFT algorithm; (<b>B</b>) after the alignment of images based on the SIFT algorithm [<a href="#B27-drones-08-00503" class="html-bibr">27</a>].</p>
Full article ">Figure 4
<p>Sample labelling on the Roboflow platform, where pixels labelled with 0 correspond to areas affected by Black Sigatoka disease, and the rest of the pixels are labelled with 1 [<a href="#B29-drones-08-00503" class="html-bibr">29</a>].</p>
Full article ">Figure 5
<p>Spectral fusion for creation of false-colour images from multispectral images.</p>
Full article ">Figure 6
<p>Creation of false-colour images for detection models using different spectrum combinations (RED, REG, NIR–GREEN, REG, NIR–BLUE, REG, NIR).</p>
Full article ">Figure 7
<p>Dataset creation for evaluation of the classification models. By subdividing and converting them into false-colour images using the UAV’s multispectral sensors, we obtained 2706 objects of diseased leaves, 3102 objects of healthy leaves, and 1192 extra objects of non-leaves to train classification algorithms. (<b>A</b>) Healthy leaf; (<b>B</b>) diseased leaf; (<b>C</b>) non-leaf extra.</p>
Full article ">Figure 8
<p>Performance using EfficientNetV2B3: accuracy and loss.</p>
Full article ">Figure 9
<p>Metrics using EfficientNetV2B3: confusion matrix.</p>
Full article ">Figure 10
<p>Open dataset including pictures of banana leaves under three categories: “healthy” banana leaves, “Xanthomonas-infected” leaves, and “Sigatoka-infected” leaves.</p>
Full article ">Figure 11
<p>Performance using EfficientNetV2B3: accuracy and loss.</p>
Full article ">Figure 12
<p>Metrics using EfficientNetV2B3: confusion matrix.</p>
Full article ">Figure 13
<p>Open dataset including pictures of banana leaves under three categories: “healthy” banana leaves, “bacteria-wilt-infected” leaves and “Sigatoka-infected” leaves.</p>
Full article ">Figure 14
<p>Performance using EfficientNetV2B3: accuracy and loss.</p>
Full article ">Figure 15
<p>Metrics using EfficientNetV2B3: Confusion matrix.</p>
Full article ">Figure 16
<p>Hybrid convolutional neural network (CNN) architecture based on current deep learning techniques for classification.</p>
Full article ">
23 pages, 1795 KiB  
Article
The Effects of Nitrogen Application and Varietal Variation on the Product Quality and In Vitro Bioaccessibility of Bioactive Compounds of Baby Spinach Varieties Grown in a Soilless Growth Medium
by Nhlanzeko Mbalenhle Bhengu, Sephora Mutombo Mianda, Martin Makgose Maboko and Dharini Sivakumar
Foods 2024, 13(17), 2667; https://doi.org/10.3390/foods13172667 - 24 Aug 2024
Cited by 3 | Viewed by 1166
Abstract
Baby spinach is becoming increasingly popular as a salad ingredient and needs high fertiliser rates to grow well and attain higher-quality leaves (dark green leaves). Chemical fertilisers, especially nitrogen (N), boost yields. There are many risks associated with nitrogen fertilisation. Additionally, spinach contains [...] Read more.
Baby spinach is becoming increasingly popular as a salad ingredient and needs high fertiliser rates to grow well and attain higher-quality leaves (dark green leaves). Chemical fertilisers, especially nitrogen (N), boost yields. There are many risks associated with nitrogen fertilisation. Additionally, spinach contains phenolic compounds and carotenoids. Nitrogen fertilisation affects growth, development, yield and metabolites. This study examined the impact of lower concentrations of N (0, 30, 60, 90, 120, 150 mg/L) on yield and colour properties [light intensity (L*) colour coordinates, unique for green colour (a*) and yellow colour (b*)], as well as the impact of varying N concentrations on the total phenolic content and p-coumaric acid, quercetin, ferulic acid, kaempferol, lutein, zeaxanthin, β-carotene and antioxidant activities in the baby spinach varieties ‘Acadia’, ‘Crosstrek’ and ‘Traverse’, and it was established that N fertilisation improves phytochemical bioaccessibility and antioxidant activity. In a split strip plot design, three baby spinach varieties were treated with different N concentrations, including 0, 30, 60, 90, 120 and 150 mg/L. For 40 days, three baby spinach varieties were grown on soilless Mikskaar Professional substrate 300. During both seasons, ’Crosstrek’ had the highest fresh mass (921.4 g/m2, 856.3 g/m2) at 120 mg/L N, while ‘Traverse’ had the highest fresh mass at 554.8 g/m2 and at 564.3 g/m2 at 90 mg/L N and did not differ significantly from 90 to 150 mg/L N during either season. During both seasons, ‘Acadia’ at 90 mg/L N increased fresh mass to 599 g/m2 and 557.9 g/m2. The variety × N supply interaction significantly affected the leaf colour; chlorophyll content across seasons; the levels of bioactive compounds, p-coumaric acid, quercetin, ferulic acid, kaempferol, lutein, zeaxanthin and β-carotene in spinach varieties; the in vitro bioaccessibility; and the antioxidant activity. Varietal differences influenced the bioaccessibility of phenolic compounds and carotenoid components. The appropriate N levels can be used during plant cultivation to optimise the bioaccessibility of this spinach variety. Thus, fertilising ‘Traverse’ with 90 mg/N mL increased the in vitro bioaccessibility of β-carotene (35.2%), p-coumaric acid (7.13%), quercetin (8.29%) and ferulic acid (1.92%) without compromising the yield. Full article
Show Figures

Figure 1

Figure 1
<p>An unsupervised PCA scores plot of phenolic and carotenoid metabolites obtained via the HPLC-UV analysis of three spinach varieties and different nitrogen supplies. ‘Acadia’ (A), ‘Crosstrek’ (C) and ‘Traverse’ (T) were treated with different N concentrations including 0, 30, 60, 90, 120 and 150 mg/L.</p>
Full article ">Figure 2
<p>VIP scores in PLS-DA assigned to phenolic and carotenoid compounds found in baby spinach cultivars grown with different N concentration levels. ‘Acadia’ (A), ‘Crosstrek’ (C) and ‘Traverse’ (T) were treated with different N concentrations including 0, 30, 60, 90, 120 and 150 mg/L.</p>
Full article ">Figure 3
<p>Heat map showing the phenolic and carotenoid compounds found in different baby spinach varieties grown with different nitrogen concentration levels. The rows represent the compounds, and the columns represent the spinach varieties at different N concentrations. The colours red and blue indicate high and low levels, respectively. ‘Acadia’ (A), ‘Crosstrek’ (C) and ‘Traverse’ (T) were treated with different N concentrations including 0, 30, 60, 90, 120 and 150 mg/L.</p>
Full article ">
18 pages, 19274 KiB  
Article
Morphological Differentiation, Yield, and Cutting Time of Lolium multiflorum L. under Acid Soil Conditions in Highlands
by William Carrasco-Chilón, Marieta Cervantes-Peralta, Laura Mendoza, Yudith Muñoz-Vílchez, Carlos Quilcate, David Casanova Nuñez-Melgar, Héctor Vásquez and Wuesley Yusmein Alvarez-García
Plants 2024, 13(16), 2331; https://doi.org/10.3390/plants13162331 - 21 Aug 2024
Viewed by 1764
Abstract
Livestock production in the basins of the northern macro-region of Peru has as its primary source pastures of Lolium multiflorum L. ‘Cajamarquino ecotype’ (ryegrass CE) in monoculture, or in association with white clover Ladino variety, for feeding. The objective of this research work [...] Read more.
Livestock production in the basins of the northern macro-region of Peru has as its primary source pastures of Lolium multiflorum L. ‘Cajamarquino ecotype’ (ryegrass CE) in monoculture, or in association with white clover Ladino variety, for feeding. The objective of this research work was the morphological characterisation, yield evaluation, and cutting time evaluation of two local genotypes (LM-58 and LM-43) of Lolium multiflorum L. in six locations. An ANOVA was performed to compare fixed effects and interaction. It was determined that the LM-58 genotype is intermediate, growing semi-erect, with a dark green colouring and 0.8 cm broadleaf, and can reach an average stem length of 46 cm, up to 1.6 cm. day−1, achieving fourth-leaf growth at 28 days under appropriate management conditions. Despite the differentiated characteristics, according to BLASTn evaluation, the ITS1 sequences showed a greater than 99.9% similar identification to Lolium multiflorum L., characterising it as such. It was determined that the LM-58 genotype outperforms LM-43, achieving a forage yield of 4.49 Mg. ha−1, a seed production of 259.23 kg. ha−1, and an average of 13.48% crude protein (CP). The best biomass yield (49.10 Mg. ha−1.yr−1) is reached at 60 days; however, at 30 days, there is a high level of CP (14.84%) and there are no differences in the annual protein production at the cutting age of 60 and 45 days. With the results of the present study, LM-58 from a selection and crossbreeding of 680 ryegrass EC accessions emerges as an elite genotype adapted to the conditions of the northern high Andean zone of Peru. Full article
(This article belongs to the Special Issue Effects of Conservation Tillage on Crop Cultivation and Production)
Show Figures

Figure 1

Figure 1
<p>Interaction between cutting time and (<b>A</b>) annual biomass (Mg. ha<sup>−1</sup>. yr<sup>−1</sup>), (<b>B</b>) protein percentage (%), (<b>C</b>) protein yield per cut (Mg. ha<sup>−1</sup>. yr<sup>−1</sup>), and (<b>D</b>) annual protein yield (Mg. ha<sup>−1</sup>. yr<sup>−1</sup>) for ryegrass ‘CE’ genotype LM-58.</p>
Full article ">
26 pages, 4754 KiB  
Article
Skin and Scalp Health Benefits of a Specific Botanical Extract Blend: Results from a Double-Blind Placebo-Controlled Study in Urban Outdoor Workers
by Vincenzo Nobile, Enza Cestone, Sabrina Ghirlanda, Andrea Poggi, Pau Navarro, Adrián García, Jonathan Jones and Nuria Caturla
Cosmetics 2024, 11(4), 139; https://doi.org/10.3390/cosmetics11040139 - 14 Aug 2024
Viewed by 3440
Abstract
Environmental pollution is increasingly recognized as a significant contributor to skin and scalp damage. Oral supplementation with a specific blend of four standardized botanical extracts (Rosmarinus officinalis, Lippia citriodora, Olea europaea leaf, and Sophora japonica) has been previously demonstrated [...] Read more.
Environmental pollution is increasingly recognized as a significant contributor to skin and scalp damage. Oral supplementation with a specific blend of four standardized botanical extracts (Rosmarinus officinalis, Lippia citriodora, Olea europaea leaf, and Sophora japonica) has been previously demonstrated to enhance skin health in individuals exposed to high environmental stress. Thus, it might represent a convenient strategy to also improve their scalp health aspect, particularly in subjects with sensitive scalps. To support these effects, a 12-week double-blind, randomized, placebo-controlled trial was performed in 66 women working outdoors in urban areas with high particulate matter (PM) levels and taking 250 mg of the botanical blend daily. Product efficacy was measured as follows: skin antioxidant status (FRAP); skin and scalp moisturization (corneometer), transepidermal water loss (tewameter), and oiliness (sebumeter); skin radiance and colour (spectrophotometer), elasticity and firmness (cutometer) and wrinkle depth (image analysis); and scalp clinical evaluation. Despite constant exposure to increased levels of PM, the tested product positively influenced all monitored parameters compared to both baseline and the placebo-treated group, in as early as 4 weeks. At the end of the study, key improvements included increased skin FRAP (21.9%), moisturization (9.5%), radiance (24.9%) and reduced wrinkle depth (−16.5%), dark spot pigmentation (−26.2%), and skin oiliness (−19.3%). For the scalp, moisturization increased (14.1%), TEWL decreased (−13.8%), and sebum content reduced by 16.2%. Additionally, 71% of subjects with sensitive scalps experienced reduced redness. These findings highlight the extensive benefits of the ingredient, expanding its application beyond conventional skin treatments to also alleviate scalp issues. Full article
Show Figures

Figure 1

Figure 1
<p>Participants flow diagram.</p>
Full article ">Figure 2
<p>Change on facial skin FRAP analysis versus baseline after 28 and 84 days in the ZP treatment (blue bars) and placebo (yellow bars) group. Data are means ± SEM. Intergroup (vs. placebo) statistical analysis is reported inside the bars of the histograms. **** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 3
<p>Change in facial skin moisturisation. (<b>a</b>) Skin moisturisation variation versus baseline after 28, 56, and 84 days in the ZP treatment (blue bars) and placebo (yellow bars) group. (<b>b</b>) Changes in skin hydration versus baseline in subjects in the dry skin type subgroup in the ZP (dark blue bars) and placebo groups (orange bars). Data are means ± SEM. Intergroup (vs. placebo) statistical analysis is reported inside the bars of the histograms as follows: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 4
<p>Change in skin transepidermal water loss (TEWL). (<b>a</b>) TEWL variation a variation versus baseline after 28, 56, and 84 days in the ZP treatment (blue bars) and placebo groups (yellow bar). (<b>b</b>) Changes in TEWL versus baseline in the TEWL subgroups: (TEWL &lt; 15 g·h<sup>−1</sup>·m<sup>−2</sup> (healthy condition)) and TEWL between 15–25 g × h<sup>−1</sup> × m<sup>−2</sup> (normal condition) both in the ZP treatment (blue and dark blue bars) and placebo groups (blue and orange bars). Data are means ± SEM. Intergroup (vs. placebo) statistical analysis is reported on the bars of the histograms. Statistical analysis is reported as follows: * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.01 and **** <span class="html-italic">p</span> &lt; 0.001. Inter-subgroup statistical analysis is reported with + upon the bars of the histograms as follows: + <span class="html-italic">p</span> &lt; 0.05 and ++ <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 5
<p>Periocular wrinkles evaluated with Primos<sup>CR</sup> of one the volunteers (volunteer 62) in the ZP treatment group. The skin surface is red in colour value, corresponding approximatively to a height of 0 mm (according to the colour scale present in the upper part of the figure). Green and blue colours represent negative values, indicating the presence of wrinkles. Orange and yellow colours represent values higher than zero.</p>
Full article ">Figure 6
<p>Change in skin sebum content. (<b>a</b>) Skin sebum variation versus baseline after 28, 56, and 84 days in the ZP treatment (blue bars) and placebo (yellow bars) group. (<b>b</b>) Changes in the skin sebum versus baseline in subjects with &gt;100 µg/cm<sup>2</sup> of sebum (oily skin subgroup in the ZP (dark blue bars) and placebo groups (orange bars). Data are means ± SEM. Intergroup (vs. placebo) statistical analysis is reported inside the bars of the histograms as follows: ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 7
<p>Changes in TEWL versus baseline in the TEWL subgroups: (TEWL &lt; 15 g·h<sup>−1</sup>·m<sup>−2</sup> (healthy condition)) and TEWL between 15–25 g × h<sup>−1</sup> × m<sup>−2</sup> (normal condition) both in the ZP treatment (blue and dark blue bars) and placebo groups (blue and orange bars). Data are means ± SEM. Intergroup (vs. placebo) statistical analysis is reported on the bars of the histograms. Statistical analysis is reported as follows: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Inter-subgroup statistical analysis is reported with + upon the bars of the histograms as follows: +++ <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 8
<p>Change in scalp sebum content. (<b>a</b>) Scalp sebum variation versus baseline after 28, 56, and 84 days in the ZP treatment (blue bars) and placebo (yellow bars) groups. (<b>b</b>) Changes in skin sebum versus baseline in subjects with &gt;100 µg/cm<sup>2</sup> of sebum (oily skin subgroup) in the ZP (dark blue bars) and placebo group (orange bars). Data are means ± SEM. Intergroup (vs. placebo) statistical analysis is reported inside the bars of the histograms as follows: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 9
<p>Scalp aspect improvement. (<b>a</b>) Percentage of subjects that improved their scalp condition in the entire panel and in the sensitive subgroup. (<b>b</b>) Images of the scalp of a volunteer with sensitive scalp in the active treatment group at baseline (T0) and at the different checkpoints. Digital pictures were acquired by means of DermoGenius ultra (DermoScan GmbH, Regensburg, Germany). Starting from T28, scalp redness is less evident.</p>
Full article ">
21 pages, 1465 KiB  
Article
Evaluating the Cold Tolerance of Stenotaphrum Trin Plants by Integrating Their Performance at Both Fall Dormancy and Spring Green-Up
by Jia Qu, Dong-Li Hao, Jin-Yan Zhou, Jing-Bo Chen, Dao-Jin Sun, Jian-Xiu Liu, Jun-Qin Zong and Zhi-Yong Wang
Horticulturae 2024, 10(7), 761; https://doi.org/10.3390/horticulturae10070761 - 18 Jul 2024
Viewed by 848
Abstract
Owing to the poor cold tolerance of Stenotaphrum Trin and the urgent need for shade-tolerant grass species in temperate regions of East China, this study evaluated the cold tolerance of 55 Stenotaphrum accessions, aiming to provide shade-tolerant materials for temperate regions. A fine [...] Read more.
Owing to the poor cold tolerance of Stenotaphrum Trin and the urgent need for shade-tolerant grass species in temperate regions of East China, this study evaluated the cold tolerance of 55 Stenotaphrum accessions, aiming to provide shade-tolerant materials for temperate regions. A fine cold-tolerant turfgrass should have both the advantages of delayed fall dormancy and early spring green-up. However, previous research on the cold resistance of turfgrass has mainly focused on the performance of the spring green-up, with less attention paid to the fall dormancy, which has affected the ornamental and application value of turfgrass. This study first dynamically investigated the leaf colour of each accession during the fall dormancy and the coverage during the spring green-up and evaluated the cold resistance of the accession through membership functions and cluster analysis. Significant differences in the cold resistance were found with the assignment of breeding lines to four categories. The weak correlation (R2 = 0.1682) between leaf colour during the fall dormancy and coverage during the spring green-up indicates that using the performance of a single period to represent the cold resistance of accessions is not appropriate. To test whether using the laboratory-based LT50 and stolon regrowth rating analysis can replace the above-improved method, we conducted a related analysis and found that the fit between these two methods is very poor. This phenomenon is attributed to the poor correlation between the laboratory-based parameters and the pot-investigated data. Therefore, this study presents a cold resistance evaluation method for Stenotaphrum that integrates performance in both the fall dormancy and spring green-up periods. This improved evaluation method cannot be simplified by the growth performance of a single period or replaced by using laboratory-based LT50 and stolon regrowth tests. With the help of this improved method, several excellent cold tolerance accessions (ST003, S13, and S12) were identified for temperate regions of East China. Full article
(This article belongs to the Special Issue Tolerance and Response of Ornamental Plants to Abiotic Stress)
Show Figures

Figure 1

Figure 1
<p>Cluster analysis based on pot experiment results.</p>
Full article ">Figure 2
<p>Correlation between the average greenness during the fall dormancy and average coverage during the spring green-up. The P and R<sup>2</sup> are the fitted parameters. The symbol * indicates a correlation.</p>
Full article ">Figure 3
<p>Cluster analysis based on laboratory results.</p>
Full article ">Figure 4
<p>Correlation between LT50 and other parameters. (<b>A</b>) Correlation between LT50 and average greenness. (<b>B</b>) Correlation between LT50 and average coverage. (<b>C</b>) Correlation between LT50 and total relative regrowth rate. The P and R<sup>2</sup> are the fitted parameters. The symbol * indicates a correlation.</p>
Full article ">Figure 5
<p>Correlation between total relative regrowth rate and other parameters. (<b>A</b>) Correlation between total relative regrowth rate and average greenness. (<b>B</b>) Correlation between total relative regrowth rate and average coverage. The P and R<sup>2</sup> are the fitted parameters. The symbol * indicates a correlation.</p>
Full article ">
16 pages, 834 KiB  
Article
Flour Functionality, Nutritional Composition, and In Vitro Protein Digestibility of Wheat Cookies Enriched with Decolourised Moringa oleifera Leaf Powder
by Temitayo D. Agba, Nurat O. Yahaya-Akor, Amarjit Kaur, Moira Ledbetter, James Templeman, Jonathan D. Wilkin, Bukola A. Onarinde and Samson A. Oyeyinka
Foods 2024, 13(11), 1654; https://doi.org/10.3390/foods13111654 - 25 May 2024
Cited by 1 | Viewed by 1645
Abstract
This study investigated the potential of decolourised Moringa oleifera leaf powder (D-MOLP) in cookies to meet consumer demand for healthier food options, addressing the issue of low acceptability due to its green colour. D-MOLP and its non-decolourised counterpart (ND-MOLP) were incorporated into wheat [...] Read more.
This study investigated the potential of decolourised Moringa oleifera leaf powder (D-MOLP) in cookies to meet consumer demand for healthier food options, addressing the issue of low acceptability due to its green colour. D-MOLP and its non-decolourised counterpart (ND-MOLP) were incorporated into wheat flour to produce cookies. The results showed that neither decolourisation nor addition level (2.5 or 7.5%) significantly affected water activity or flour functionality, though slight differences in cookie colour were observed. The Moringa-enriched cookies exhibited an improved spread ratio as well as higher protein, phenolic content, antioxidant activity, and in vitro protein digestibility compared to control cookies. The detected phenolic acids included chlorogenic, ferulic, and fumaric acids, with the D-MOLP cookies showing superior nutritional properties, likely due to nutrient concentration and reduced antinutrients. Notably, glutamic acid was the major amino acid in all the cookies, but only lysine significantly increased across the cookie types. This suggests D-MOLP could be a promising alternative for food enrichment. Future research should address the consumer acceptability, volatile components, and shelf-life of D-MOLP-enriched cookies. Full article
(This article belongs to the Section Grain)
Show Figures

Figure 1

Figure 1
<p>Phenolic acids of wheat–Moringa cookies. A: 100% wheat flour; B: 97.5% wheat flour + 2.5% decolourised Moringa; C: 92.5% wheat flour + 7.5% decolourised Moringa; D: 97.5% wheat flour + 2.5% non-decolourised Moringa; E: 92.5% wheat flour + 7.5% non-decolourised Moringa. Error bars indicate standard deviation (N = 3). Different letters mean significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 2
<p>Protein digestibility of wheat–Moringa cookies. A: 100% wheat flour; B: 97.5% wheat flour + 2.5% decolourised Moringa; C: 92.5% wheat flour + 7.5% decolourised Moringa; D: 97.5% wheat flour + 2.5% non-decolourised Moringa; E: 92.5% wheat flour + 7.5% non-decolourised Moringa. Error bars indicate standard deviation (N = 3). Different letters mean significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">
17 pages, 1836 KiB  
Article
Yield and Fruit Characteristics of Tomato Crops Grown with Mineral Macronutrients: Impact of Organo-Mineral Fertilizers through Foliar or Soil Applications
by Grazia Disciglio, Annalisa Tarantino and Laura Frabboni
Plants 2024, 13(11), 1458; https://doi.org/10.3390/plants13111458 - 24 May 2024
Cited by 3 | Viewed by 1222
Abstract
The utilization of plant biostimulants has gained importance as a strategy by which to improve plant productivity and soil health. Two independent trials were conducted across two seasons (2021 and 2023) to evaluate the effects of foliar or soil applications of various commercial [...] Read more.
The utilization of plant biostimulants has gained importance as a strategy by which to improve plant productivity and soil health. Two independent trials were conducted across two seasons (2021 and 2023) to evaluate the effects of foliar or soil applications of various commercial organo-mineral fertilizers (Futuroot®, Radicon® Amifort®) with biostimulant action that is exerted on yield and fruit characteristics of processing tomato crops (cv Taylor F1) that have been exposed to mineral macronutrients. These treatments were administered three times during the season: at the transplanting, pre-flowering and berry development stages. In the first trial, conducted in two fields characterized respectively by low and high fertility, foliar applications of Radicon®, which is based on humic acid and amino acids, increased the leaf greenness index SPAD compared with the control. Furthermore, the leaf green colour intensity (SPAD index), measured during the reproductive phases of the tomato exhibited a positive correlation (R2 = 0.726) with the marketable yield obtained. This increase in marketable yield was significant in the biostimulant treatment compared with the control in both soils, especially in the soil characterized by lower fertility (16.1%), when compared with the more fertile soil (6.8%). In the second trial, conducted in the low-fertility field mentioned above, soil applications of all biostimulants (Futuroot®, Radicon® and the combinations [Radicon® + Amifort-Plus®]) significantly increased the marketable yield by 27.8%, 13.5% and 27.7%, respectively, compared with the control. The most significant beneficial effects of both Futuroot® and [Radicon® + Amifort®] could be attributed to the combination of humic acids and auxins, cytokinins or microelements (Zn, Mn, MgO) present in the formulation of these products. Furthermore, the increase in marketable yield obtained when Radicon® was applied to leaves was higher (16.1%) than that observed with soil application (13.5%). In both trials, no relevant effects of biostimulant products were observed on most of the physicochemical characteristics of tomato fruits. In conclusion, the biostimulants based on humic acid and amino acids combined with chemical fertilizers tested in the present study and applied by fertigation were more effective in improving tomato yield, and therefore they can be recommended for efficient agricultural production. Full article
(This article belongs to the Special Issue Effects of Biostimulants on Plant Physiology and Metabolic Profile)
Show Figures

Figure 1

Figure 1
<p>Average SPAD values ± std. dev. detected on 1, 8 and 28 June 2021, on tomato plants treated with biostimulant and the untreated control grown in Fields 1 and 2. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05, according to Tukey’s test.</p>
Full article ">Figure 2
<p>Linear SPAD index—marketable yield ratio obtained for biostimulant-treated and untreated control tomato crops grown in both Fields 1 and 2.</p>
Full article ">Figure 3
<p>Effect of biostimulant treatment and control on (<b>A</b>) marketable yield, (<b>B</b>) green fruits, (<b>C</b>) rotten fruits, (<b>D</b>) total yield, (<b>E</b>) plant biomass, (<b>F</b>) fruit weight, (<b>G</b>) fruit length, and (<b>H</b>) fruit width. Average values ± std. dev. of biostimulant treatment and control in each field and the relative average of two fields are shown (different lowercase letters per field and different capital letters between fields each indicate significant differences at <span class="html-italic">p</span> &lt; 0.05). Graph without letters means there were no significant differences between mean.</p>
Full article ">Figure 4
<p>Effect of biostimulant treatments and control on (<b>A</b>) marketable yield, (<b>B</b>) green fruits, (<b>C</b>) rotten fruits, (<b>D</b>) total yield, (<b>E</b>) plant biomass, (<b>F</b>) fruit weight, (<b>G</b>) fruit length, and (<b>H</b>) fruit width. Average values ± std. dev. of biostimulant treatments and control are shown (different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05). Graph without letters means there were no significant differences between mean.</p>
Full article ">
17 pages, 4347 KiB  
Article
Water Use Efficiency in a Deficit-Irrigated Orange Orchard
by Fiorella Stagno, Massimo Brambilla, Giancarlo Roccuzzo and Alberto Assirelli
Horticulturae 2024, 10(5), 498; https://doi.org/10.3390/horticulturae10050498 - 11 May 2024
Cited by 2 | Viewed by 1568
Abstract
Citrus is a subtropical fruit tree with high water requirements. This study aimed to determine the effects of water deficit on an orange orchard subjected to different water-saving strategies. The study was realised in an orange orchard in a semiarid area by adopting [...] Read more.
Citrus is a subtropical fruit tree with high water requirements. This study aimed to determine the effects of water deficit on an orange orchard subjected to different water-saving strategies. The study was realised in an orange orchard in a semiarid area by adopting four different water management techniques: 100% crop evapotranspiration (control); SSDI—subsurface sustained deficit irrigation; RDI—regulated deficit irrigation; PRD—partial rootzone drying treatment during five growing seasons. The experimental design foresaw a randomised block design with six replicates per treatment (24 index plants). The results of the study showed that the water-saving strategies reduced irrigation water consumption by 25% (SSDI), 33% (RDI), and 49% (PRD) compared to the fully irrigated treatment without yield reduction, thus increasing water use efficiency. Mineral nutrition of the trees was slightly affected by irrigation treatments; element concentration in leaves was generally in the optimal range; only potassium showed values below the recommended leaf concentrations. Regarding fruit quality parameters, the vitamin C concentration in RDI showed significant differences with a value of 62.7 mg 100 mL−1 compared to 58.5 mg 100 mL−1 in the control. Plants subjected to SSDI and PRD strategies showed increased levels of pulp colour index with significant values of 10 and 9.90, respectively, compared to the control (8.44). By implementing targeted water management, citrus growers could save water and increase the ascorbic acid and sugar concentration in the fruits; anthocyanins also increased but not significantly. These findings open new market opportunities for citrus growers in marginal areas, where they cannot rely solely on producing citrus fruits to remain competitive. Full article
Show Figures

Figure 1

Figure 1
<p>Average air temperature, humidity, and rainfall during the experiment in the 2013–2018 period.</p>
Full article ">Figure 2
<p>Stem water potential (Ψ<sub>stem</sub>) and the stomatal conductance (g<sub>s</sub>) for various days of the year (DOYs) in the years 2013 (<b>a</b>,<b>b</b>), 2014 (<b>c</b>,<b>d</b>), 2015 (<b>e</b>,<b>f</b>), 2016 (<b>g</b>,<b>h</b>), and 2017 (<b>i</b>,<b>j</b>). Error bars represent the standard deviation from the mean (n = 6).</p>
Full article ">Figure 2 Cont.
<p>Stem water potential (Ψ<sub>stem</sub>) and the stomatal conductance (g<sub>s</sub>) for various days of the year (DOYs) in the years 2013 (<b>a</b>,<b>b</b>), 2014 (<b>c</b>,<b>d</b>), 2015 (<b>e</b>,<b>f</b>), 2016 (<b>g</b>,<b>h</b>), and 2017 (<b>i</b>,<b>j</b>). Error bars represent the standard deviation from the mean (n = 6).</p>
Full article ">Figure 3
<p>Average potassium leaf concentration and standard deviation (g kg<sup>−1</sup><sub>d.w.</sub>) in each treatment and per year of the trial. Error bars represent the standard deviation from the mean (n = 6).</p>
Full article ">Figure 4
<p>Average magnesium concentration and standard deviation in plants’ leaves (g kg<sup>−1</sup><sub>d.w.</sub>): (<b>a</b>) depending on treatments; (<b>b</b>) depending on year. Averages not sharing a letter are significantly different (<span class="html-italic">p</span> &lt; 0.05 at post hoc Tukey HSD test). Error bars represent the standard deviation from the mean (n = 30 for the treatments and 24 for the years).</p>
Full article ">Figure 5
<p>Average weight per fruit and standard deviation (g<sub>f.w.</sub>) and yield of the orchard (t ha<sup>−1</sup>). Fruit weight averages that do not share a letter are significantly different (<span class="html-italic">p</span> &lt; 0.05 at post hoc Tukey HSD test). Error bars represent the standard deviation from the mean (n = 30).</p>
Full article ">Figure 6
<p>Average WUE of total acids yield and standard deviation (g m<sup>−3</sup>) according to treatment (on the <b>left</b>) and year (on the <b>right</b>). Averages that do not share a letter are significantly different (Tukey HSD post hoc test, <span class="html-italic">p</span> &lt; 0.05). Error bars represent the standard deviation from the mean (n = 30 for the treatments and 24 for the years).</p>
Full article ">Figure 7
<p>Average WUE of total soluble solids yield and standard deviation (g m<sup>−3</sup>) according to treatment (on the <b>left</b>) and year (on the <b>right</b>). Averages that do not share a letter are significantly different (Tukey HSD post hoc test, <span class="html-italic">p</span> &lt; 0.05). Error bars represent the standard deviation from the mean (n = 30 for the treatments and 24 for the years).</p>
Full article ">Figure 8
<p>Average WUE of anthocyanins yield and standard deviation (g m<sup>−3</sup>) according to treatment (on the <b>left</b>) and year (on the <b>right</b>). Error bars represent the standard deviation from the mean (n = 30 for the treatments and 24 for the years).</p>
Full article ">Figure 9
<p>Average WUE of vitamin C yield and standard deviation (g m<sup>−3</sup>) according to treatment (on the <b>left</b>) and year (on the <b>right</b>). Averages that do not share a letter are significantly different (Tukey HSD post hoc test, <span class="html-italic">p</span> &lt; 0.05). Error bars represent the standard deviation from the mean (n = 30 for the treatments and 24 for the years).</p>
Full article ">
27 pages, 1980 KiB  
Article
Apple Tree Leaves (Malus domestica Borkh) as a Valuable Source of Polyphenolic Compounds with a High Antioxidant Capacity
by Andrzej Cendrowski, Zuzanna Jakubowska and Jarosław L. Przybył
Appl. Sci. 2024, 14(8), 3252; https://doi.org/10.3390/app14083252 - 12 Apr 2024
Cited by 2 | Viewed by 1677
Abstract
The aim of the study was to compare the antioxidant activity and polyphenol content in extracts prepared from freeze-dried leaves of three apple cultivars: Ligol, Gala, and Gloster, using different solvents and extraction methods. The content of total polyphenols was determined using the [...] Read more.
The aim of the study was to compare the antioxidant activity and polyphenol content in extracts prepared from freeze-dried leaves of three apple cultivars: Ligol, Gala, and Gloster, using different solvents and extraction methods. The content of total polyphenols was determined using the Folin–Ciocâlteu reagent method, and a qualitative and quantitative analysis of polyphenols was performed using the HPLC method. The antioxidant capacity of the extracts was determined using the DPPH radical method. The colour parameters (in the CIEL*a*b system) of the obtained extracts were also determined. The antioxidant activity of apple leaf extracts increased with increasing polyphenol content. Water–alcoholic extracts from apple leaves were characterised by a significantly higher antioxidant capacity and polyphenol content in comparison with water extracts. The best solvent was a mixture of water and methanol (80%). Among the phenolic compounds identified in the extracts, the most common was phloridzin. The highest content of phloridzin (105.0 mg/1 g of dry weight) was found in water–methanol extracts from the leaves of the Ligol variety obtained with ultrasound-assisted extraction. The extracts with the highest antioxidant activity (131.2 μmol of Trolox/1 g of dry weight) and polyphenol content (81.9 mg GAE/1 g of dry weight) were water–methanol from the leaves of the Ligol cultivar, obtained by shaking them with a solvent. Full article
Show Figures

Figure 1

Figure 1
<p>Content of total polyphenols in apple leaf extracts obtained with ultrasound-assisted extraction.</p>
Full article ">Figure 2
<p>Content of total polyphenols in apple leaf extracts obtained with accelerated solvent extraction.</p>
Full article ">Figure 3
<p>Content of total polyphenols in apple leaf extracts obtained using various extraction methods. Abbreviations: SSE—Shaking Solvent Extraction; UAE—Ultrasound-Assisted Extraction; ASE—Accelerated Solvent Extraction.</p>
Full article ">Figure 4
<p>Sample chromatogram of apple leaf extract (at 254 nm). Peaks: 1—(-) epicatechin, 2—rutin, 3—hyperoside, 4—isoquercitrin, 5, 7, 8—quercetin glycosides, 6—phloretin xyloglucoside, 9—quercitrin, 10—phloridzin, 11—naringenin, 12—phloretin.</p>
Full article ">Figure 5
<p>Values of colour parameters in extracts made using various extraction methods.</p>
Full article ">Figure 6
<p>Values of colour parameters in extracts made from different cultivars of apple leaves.</p>
Full article ">Figure 7
<p>Values of colour parameters in extracts made using various solvents.</p>
Full article ">Figure 8
<p>Antioxidant capacity in apple leaf extracts prepared with shaking solvent extraction (SSE).</p>
Full article ">Figure 9
<p>Antioxidant capacity in apple leaf extracts prepared using the UAE method.</p>
Full article ">Figure 10
<p>Antioxidant capacity in apple leaf extracts prepared using the ASE method.</p>
Full article ">Figure 11
<p>Antioxidant capacity of apple leaf extracts obtained using different extraction methods.</p>
Full article ">
15 pages, 2020 KiB  
Article
Comparative Phytoprofiling of Achillea millefolium Morphotypes: Assessing Antioxidant Activity, Phenolic and Triterpenic Compounds Variation across Different Plant Parts
by Lina Raudone, Gabriele Vilkickyte, Mindaugas Marksa and Jolita Radusiene
Plants 2024, 13(7), 1043; https://doi.org/10.3390/plants13071043 - 8 Apr 2024
Cited by 5 | Viewed by 1966
Abstract
Achillea millefolium L., commonly known as yarrow, is a versatile and widely distributed plant species with a rich history of ethnopharmacological significance. This study aimed to evaluate the comparative differences of A. millefolium inflorescence morphotypes. The phytochemical profile of white and pink inflorescence [...] Read more.
Achillea millefolium L., commonly known as yarrow, is a versatile and widely distributed plant species with a rich history of ethnopharmacological significance. This study aimed to evaluate the comparative differences of A. millefolium inflorescence morphotypes. The phytochemical profile of white and pink inflorescence morphotypes was characterised by a complex of thirty-four phenolic and triterpene compounds. The species has distinct morphotypes of white and pink inflorescence. Phenolic and triterpenic profiles were determined, and individual compounds were quantified in inflorescence, leaf, and stem samples of two morphotypes tested. The antioxidant activity of plant extracts was evaluated by free radical scavenging (ABTS) and ferric-reducing antioxidant power (FRAP) assays. Caffeoylquinic acids predominated in all parts of the plant tested. Chlorogenic acid and 3,5-dicaffeoylquinic acid were the principal compounds in the phenolic profile. Betulin, betulinic acid, and α-amyrin were the prevailing triterpenic components in the triterpenic profiles of Achillea millefolium morphotypes. The predominant flavonoids in inflorescences were flavones, while in leaves, flavonols were the organ-specific compounds. The quantitative differences were observed between plant parts of morphotypes. Leaves consistently displayed the highest amounts of identified compounds and have been testified as the main source of antioxidant activity. Overall, white inflorescences accumulated a higher total amount of compounds compared to pink ones. The observed differences between morphotypes derived from the same population reflect the differences in specialised metabolites and their chemotypes. This study addresses gaps in knowledge, particularly in phenolic and triterpenic profiling of coloured inflorescence morphotypes, enhancing our understanding of chemotypes and morphotypes within the species. Full article
Show Figures

Figure 1

Figure 1
<p>PCA-1 score plot model presenting the amounts of predominant phenolic and triterpenic compounds in plant organs (I−inflorescences; L−leaves; S−stems) of P (blue circles) and W (red circles) of <span class="html-italic">A. millefolium</span> morphotypes.</p>
Full article ">Figure 2
<p>PCA-2 score plot model presenting the amounts of predominant phytochemical compounds in inflorescences (I−inflorescences) of P (blues circles) and W (red circles) <span class="html-italic">A. millefolium</span> morphotypes.</p>
Full article ">Figure 3
<p>PCA-3 score plot model presenting phytochemical compound groups in plant organs (I−inflorescences; L−leaves; S−stems) of P (blue circles) and W (red circles) <span class="html-italic">Achillea millefolium</span> morphotypes.</p>
Full article ">Figure 4
<p>Antioxidant activity mean Trolox equivalent (TE) values (µmol/g, DW) of inflorescences, leaves and stems of white and pink <span class="html-italic">A. millefolium</span> morphotypes.</p>
Full article ">Figure 5
<p>Two wild morphotypes of <span class="html-italic">Achillea millefolium</span> ((<b>A</b>)—mixed stands; (<b>B</b>)—pink morphotype; (<b>C</b>)—white morphotype). Photo by authors.</p>
Full article ">
14 pages, 2903 KiB  
Article
Identification and Characterisation of the CircRNAs Involved in the Regulation of Leaf Colour in Quercus mongolica
by Yangchen Yuan, Xinbo Pang, Jiushuai Pang, Qian Wang, Miaomiao Zhou, Yan Lu, Chenyang Xu and Dazhuang Huang
Biology 2024, 13(3), 183; https://doi.org/10.3390/biology13030183 - 14 Mar 2024
Viewed by 1481
Abstract
Circular RNAs (circRNAs) are important regulatory molecules involved in various biological processes. However, the potential function of circRNAs in the turning red process of Quercus mongolica leaves is unclear. This study used RNA-seq data to identify 6228 circRNAs in leaf samples from four [...] Read more.
Circular RNAs (circRNAs) are important regulatory molecules involved in various biological processes. However, the potential function of circRNAs in the turning red process of Quercus mongolica leaves is unclear. This study used RNA-seq data to identify 6228 circRNAs in leaf samples from four different developmental stages and showed that 88 circRNAs were differentially expressed. A correlation analysis was performed between anthocyanins and the circRNAs. A total of 16 circRNAs that may be involved in regulating the colour of Mongolian oak leaves were identified. CircRNAs may affect the colour of Q. mongolica leaves by regulating auxin, cytokinin, gibberellin, ethylene, and abscisic acid. This study revealed the potential role of circRNAs in the colour change of Q. mongolica leaves. Full article
(This article belongs to the Special Issue Recent Advances in Biosynthesis and Degradation of Plant Anthocyanin)
Show Figures

Figure 1

Figure 1
<p><span class="html-italic">Q. mongolica</span> phenotypes during the four developmental stages: young leaf stage (S1), green leaf stage (S2), colour change stage (S3), and red leaf stage (S4).</p>
Full article ">Figure 2
<p>Changes in the (<b>a</b>) leaf colour parameters, (<b>b</b>) chlorophyll contents, (<b>c</b>) carotenoid content, (<b>d</b>) anthocyanin content, and (<b>e</b>) five hormone contents of <span class="html-italic">Q. mongolica</span>. Different lowercase letters indicate significant differences between the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>Statistical analysis of the identified circRNAs and the differentially expressed circRNAs (DECs) in S1, S2, S3, and S4. (<b>a</b>) The proportions of the various types of circular RNAs. (<b>b</b>) Venn diagram analysis of the DECs in S1–S4, S2–S4, and S3–S4. (<b>c</b>) The number of upregulated and downregulated DECs in each comparison.</p>
Full article ">Figure 4
<p>Heatmap of the correlations between the 5 hormones and 16 DECs. * Indicates significant correlation (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 5
<p>Validation of <span class="html-italic">Q. mongolica</span> (<b>a</b>) RNA-seq data and qRT-PCR results through (<b>b</b>) comparisons of the log2 gene expression ratios between the RNA-seq and qRT-PCR results, respectively.</p>
Full article ">
12 pages, 2767 KiB  
Article
Ethyl Methanesulfonate Mutant Library Construction in Tartary Buckwheat with Agronomic Trait and Flavonoid Screening for Germplasm Innovation
by Huajie Guo, Zhiying Qin, Wei Ren, Hongmei Feng, Wenliang Chen, Longlong Liu and Zhaoxia Sun
Agronomy 2024, 14(3), 547; https://doi.org/10.3390/agronomy14030547 - 7 Mar 2024
Cited by 1 | Viewed by 1252
Abstract
As a nutrient-rich multigrain crop, buckwheat is a typical “medicinal food homology” crop that is rich in flavonoids, including rutin and various vitamins. It has positive anti-oxidant and anti-tumour properties and lowers blood pressure. However, due to strict self-crossing characteristics, slow progress has [...] Read more.
As a nutrient-rich multigrain crop, buckwheat is a typical “medicinal food homology” crop that is rich in flavonoids, including rutin and various vitamins. It has positive anti-oxidant and anti-tumour properties and lowers blood pressure. However, due to strict self-crossing characteristics, slow progress has been made in Tartary buckwheat (TB) cross-breeding, resulting in the slow breeding of new varieties of new TB varieties, which has limited the improvement of yield and quality. Therefore, mutant breeding is a rapid and effective technique for broadening and innovating TB breeding. In recent years, improving qualities related to yield, lodging resistance, and stability have become key points in TB breeding. Based on the above findings, excellent, potentially valuable TB lines with rich phenotypes were obtained for the TB mutation library via ethyl methanesulfonate (EMS), laying a foundation for creating new TB germplasms. In this study, we systematically investigated more than 10 agronomic traits of JQ2 and JQ4 mutants, including plant type, leaf colour, grain type, grain colour, grain number per plant, grain length, grain width, grain weight per plant, and 1000-grain weight. The results show that the maximum number of grains per plant was 1956, the weight was 32.84 g, and the 1000-grain weight was 30.89 g. The maximum number of grains per JQ4 plant was 2308, and the weight was 44.82 g. The maximum 1000-grain weight was 24.7 g. Among the 295 JQ2 mutants and 153 JQ4 mutants, 10 flavonoids (orientin, morin, quercetin, kaempferol, luteolin, naringin, hesperetin, myricetin, hesperidin, and rutin) were detected with near infrared spectroscopy (NIR). The mutants were divided into five groups according to the flavonoid content of the JQ2 mutants, of which the first group included 31 individual lines. and the second to fifth groups included 70, 69, 72, and 53 lines, respectively. The JQ4 mutants were divided into four classes, of which 41, 50, 32, and 30 were individual lines, respectively, with the highest rutin content being 82.06 mg/g. In summary, through systematic analysis and screening of the agronomic traits and flavonoid contents of JQ2 and JQ4 mutant seeds, we obtained three lines with a high 1000-grain weight, including two JQ2 mutant lines (30.89 g) and one JQ4 mutant line, which reached 24.70 g and ten lines with high grain weight per plant. This included 8 JQ2 mutants and 2 JQ4 mutants, as well as 72 high-rutin mutants (including 31 lines from JQ2 and 41 lines from JQ4 mutants). These elite lines provide the material basis for creating TB germplasms with excellent qualities and cultivation characteristics. Full article
(This article belongs to the Special Issue Genetic Potentials and Breeding Progress in Cereal Grains)
Show Figures

Figure 1

Figure 1
<p>Variation of plant type of ‘JQ 2’ and ‘JQ 4’ mutants. (<b>A</b>) JQ2. (<b>B</b>) JQ2-1510-1. (<b>C</b>) JQ2-319-2. (<b>D</b>) JQ2 dwarfing line statistics. (<b>E</b>) JQ4. (<b>F</b>) JQ4-7-17-1. (<b>G</b>) JQ4-8-22-4. (<b>H</b>) JQ4 dwarfing line statistics. Through Duncan’s multiple comparative analysis, ‘*’ indicated significant difference at <span class="html-italic">p</span> &lt; 0.05 level. ‘***’ indicated significant difference at <span class="html-italic">p</span> &lt; 0.001 level.</p>
Full article ">Figure 2
<p>Leaf colour of JQ2 green (<b>A</b>) and JQ2 mutants: light yellow(<b>B</b>), dark green (<b>C</b>), and dark green (<b>D</b>). Leaf colour of JQ4 (<b>E</b>) and JQ4 mutants: light green (<b>E</b>), light green (<b>F</b>), dark green (<b>G</b>), dark green (<b>H</b>).</p>
Full article ">Figure 3
<p>Grain variation of ‘JQ 2’ and ‘JQ 4’ mutants—grain length. (<b>A</b>) Comparison of grain length between JQ2 and JQ2-281-2, JQ4 and JQ4-1561. (<b>B</b>) Comparison of grain lengths of ‘JQ2’, ‘JQ4’, and mutants under the microscope (whole grain, longitudinal cut, transverse cut from top to bottom, respectively). (<b>C</b>,<b>D</b>) Comparative analysis of grain length. (<b>E</b>,<b>F</b>) Comparative analysis of grain cross-cut area. Through Duncan’s multiple comparative analysis, the difference of ‘**’ <span class="html-italic">p</span> &lt; 0.01 was significant.</p>
Full article ">Figure 4
<p>Grain variation of ‘JQ2’ and ‘JQ4’ mutants—thin shell. (<b>A</b>) Comparison of grain types between JQ2 and JQ2-1253, JQ4 and JQ4-1551. (<b>B</b>) Comparison of grain morphology of JQ2, JQ4, and mutant under microscope (whole grain from top to bottom, longitudinal cut, transverse cut, respectively). (<b>C</b>,<b>D</b>) Comparative analysis of grain length. (<b>E</b>) Comparative analysis of grain cross-cut thickness. (<b>F</b>) Comparative analysis of grain cross-cut area. Through Duncan’s multiple comparative analysis, the difference of ‘**’ <span class="html-italic">p</span> &lt; 0.01 was significant.</p>
Full article ">Figure 5
<p>Cluster heat map of flavonoid content in the mutant of ‘JQ 2’ (<b>A</b>) and ‘JQ 4’s (<b>B</b>). Note: The enlarged part showed the high rutin content lines.</p>
Full article ">
20 pages, 3727 KiB  
Article
Potential of Unmanned Aerial Vehicle Red–Green–Blue Images for Detecting Needle Pests: A Case Study with Erannis jacobsoni Djak (Lepidoptera, Geometridae)
by Liga Bai, Xiaojun Huang, Ganbat Dashzebeg, Mungunkhuyag Ariunaa, Shan Yin, Yuhai Bao, Gang Bao, Siqin Tong, Altanchimeg Dorjsuren and Enkhnasan Davaadorj
Insects 2024, 15(3), 172; https://doi.org/10.3390/insects15030172 - 4 Mar 2024
Cited by 2 | Viewed by 1753
Abstract
Erannis jacobsoni Djak (Lepidoptera, Geometridae) is a leaf-feeding pest unique to Mongolia. Outbreaks of this pest can cause larch needles to shed slowly from the top until they die, leading to a serious imbalance in the forest ecosystem. In this work, to address [...] Read more.
Erannis jacobsoni Djak (Lepidoptera, Geometridae) is a leaf-feeding pest unique to Mongolia. Outbreaks of this pest can cause larch needles to shed slowly from the top until they die, leading to a serious imbalance in the forest ecosystem. In this work, to address the need for the low-cost, fast, and effective identification of this pest, we used field survey indicators and UAV images of larch forests in Binder, Khentii, Mongolia, a typical site of Erannis jacobsoni Djak pest outbreaks, as the base data, calculated relevant multispectral and red–green–blue (RGB) features, used a successive projections algorithm (SPA) to extract features that are sensitive to the level of pest damage, and constructed a recognition model of Erannis jacobsoni Djak pest damage by combining patterns in the RGB vegetation indices and texture features (RGBVI&TF) with the help of random forest (RF) and convolutional neural network (CNN) algorithms. The results were compared and evaluated with multispectral vegetation indices (MSVI) to explore the potential of UAV RGB images in identifying needle pests. The results show that the sensitive features extracted based on SPA can adequately capture the changes in the forest appearance parameters such as the leaf loss rate and the colour of the larch canopy under pest damage conditions and can be used as effective input variables for the model. The RGBVI&TF-RF440 and RGBVI&TF-CNN740 models have the best performance, with their overall accuracy reaching more than 85%, which is a significant improvement compared with that of the RGBVI model, and their accuracy is similar to that of the MSVI model. This low-cost and high-efficiency method can excel in the identification of Erannis jacobsoni Djak-infested regions in small areas and can provide an important experimental theoretical basis for subsequent large-scale forest pest monitoring with a high spatiotemporal resolution. Full article
(This article belongs to the Section Insect Pest and Vector Management)
Show Figures

Figure 1

Figure 1
<p>Location of the study area and sample trees.</p>
Full article ">Figure 2
<p>Tree canopy vectorisation (red border) and damage level assignment.</p>
Full article ">Figure 3
<p>The distribution of tree vegetation indices at different damage levels.</p>
Full article ">Figure 4
<p>Variance of RGB features.</p>
Full article ">Figure 5
<p>The importance of sensitive RGB features for optimal RGB<sub>VI&amp;TF</sub> models.</p>
Full article ">Figure 6
<p>Confusion matrices of different classification models.</p>
Full article ">Figure 7
<p>Modelling accuracy of entire features and sensitive features to different levels of trees.</p>
Full article ">
16 pages, 1709 KiB  
Article
Production of Acid and Rennet-Coagulated Cheese Enriched by Olive (Olea europaea L.) Leaf Extract—Determining the Optimal Point of Supplementation and Its Effects on Curd Characteristics
by Elizabeta Zandona, Lucija Vranković, Sandra Pedisić, Tomislava Vukušić Pavičić, Ana Dobrinčić, Nives Marušić Radovčić, Katarina Lisak Jakopović, Marijana Blažić and Irena Barukčić Jurina
Foods 2024, 13(4), 616; https://doi.org/10.3390/foods13040616 - 18 Feb 2024
Cited by 2 | Viewed by 2240
Abstract
This study investigated the potential of olive leaf extract (OLE), as a functional ingredient, to improve cheese properties, because it is rich in phenols. Milk and dairy products are poor in phenolic compounds. The main objective was to determine the most effective coagulation [...] Read more.
This study investigated the potential of olive leaf extract (OLE), as a functional ingredient, to improve cheese properties, because it is rich in phenols. Milk and dairy products are poor in phenolic compounds. The main objective was to determine the most effective coagulation method and timing of OLE supplementation to maximize retention in the cheese matrix. Experimental cheeses were produced using the rennet and acid coagulation methods, with OLE added either directly to the cheese milk or to the curd phase. Three OLE effective concentrations corresponding to 25%, 50%, and 75% inhibition of DPPH reagent (EFC25, EFC50, and EFC75, respectively) were added, i.e., 11.5 mg GAE L−1, 16.6 mg GAE L−1, and 26.3 mg GAE L−1, respectively. The results showed that OLE significantly increased the concentration of total phenols, total flavonoids, and antioxidant activity in all cheese samples and in the residual whey, especially at higher effective concentrations (EFC 50 and EFC 75). Rennet-coagulated cheese to which OLE was added prior to coagulation (EM 25, EM 50, EM 75) exhibited higher hardness, gumminess, and chewiness but lower elasticity, suggesting alterations in the paracasein matrix. OLE did not adversely affect acidity, water activity, or cheese yield. However, higher EFC resulted in significant colour changes (∆E* > 3.0). In conclusion, the enrichment of cheesemaking milk with OLE and the application of the rennet coagulation method are the most suitable to optimise the production of OLE-enriched cheese. This research shows the potential to improve the nutritional value of cheese while maintaining its desired characteristics. Full article
(This article belongs to the Special Issue Recent Advances in Cheese and Fermented Milk Production)
Show Figures

Figure 1

Figure 1
<p>The scheme of cheese curd production enriched with olive leaf extract (OLE) at different stages of production.</p>
Full article ">Figure 2
<p>The textural properties of (<b>a</b>) hardness, (<b>b</b>) gumminess, (<b>c</b>) chewiness, (<b>d</b>) and springiness of OLE-enriched enzymatic (EM, EC) and acid-coagulated (AM, AC) cheese curds in relation to the added effective olive leaf extract (OLE) concentrations (0, 25, 50, 75). The addition of OLE to milk is represented by a solid line, while the dashed line corresponds to the addition of OLE to cheese curds. The black lines indicate enzymatic coagulation, while the grey ones correspond to the acid coagulation.</p>
Full article ">Figure 3
<p>Total phenols (TP) (mg GAE g<sup>−1</sup>) and antioxidant activity (AA) (µmol TEQ g<sup>−1</sup>) in olive leaf extract (OLE) (0, 25, 50, 75) enriched cheese curds produced by rennet (EM, EC) (<b>a</b>) or acid coagulation (AM, AC) (<b>b</b>) and the remaining whey (WEM, WEC, WAM, WAC). Black columns indicate TP in cheese curd samples, while grey columns correspond to TP in whey samples. Solid fill represents milk supplementation with OLE, while the dotted pattern indicates curd supplementation with OLE. The black line represents an AA (µmol TEQ g<sup>−1</sup>) in cheese curd samples, while the grey line corresponds to AA in whey samples.</p>
Full article ">Figure 4
<p>Total flavonoids (mg QE g<sup>−1</sup>) in olive leaf extract (OLE) (0, 25, 50, 75) enriched cheese curds produced by rennet (EM, EC) (<b>a</b>) or acid coagulation (AM, AC) (<b>b</b>) and the remaining whey (WEM, WEC, WAM, WAC). Black columns indicate TF in cheese curd samples, while grey columns correspond to TF in whey samples. Solid fill represents milk supplementation with OLE, while the dotted pattern indicates curd supplementation with OLE.</p>
Full article ">Figure 4 Cont.
<p>Total flavonoids (mg QE g<sup>−1</sup>) in olive leaf extract (OLE) (0, 25, 50, 75) enriched cheese curds produced by rennet (EM, EC) (<b>a</b>) or acid coagulation (AM, AC) (<b>b</b>) and the remaining whey (WEM, WEC, WAM, WAC). Black columns indicate TF in cheese curd samples, while grey columns correspond to TF in whey samples. Solid fill represents milk supplementation with OLE, while the dotted pattern indicates curd supplementation with OLE.</p>
Full article ">
Back to TopTop