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Search Results (26,468)

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22 pages, 1007 KiB  
Review
Interactions Between Forest Cover and Watershed Hydrology: A Conceptual Meta-Analysis
by Mathurin François, Terencio Rebello de Aguiar Junior, Marcelo Schramm Mielke, Alain N. Rousseau, Deborah Faria and Eduardo Mariano-Neto
Water 2024, 16(23), 3350; https://doi.org/10.3390/w16233350 - 21 Nov 2024
Abstract
The role of trees in watershed hydrology is governed by many environmental factors along with their inherent characteristics and not surprisingly has generated diverse debates in the literature. Herein, this conceptual meta-analysis provides an opportunity to propose a conceptual model for understanding the [...] Read more.
The role of trees in watershed hydrology is governed by many environmental factors along with their inherent characteristics and not surprisingly has generated diverse debates in the literature. Herein, this conceptual meta-analysis provides an opportunity to propose a conceptual model for understanding the role of trees in watershed hydrology and examine the conditions under which they can be an element that increases or decreases water supply in a watershed. To achieve this goal, this conceptual meta-analysis addressed the interaction of forest cover with climatic conditions, soil types, infiltration, siltation and erosion, water availability, and the diversity of ecological features. The novelty of the proposed conceptual model highlights that tree species and densities, climate, precipitation, type of aquifer, and topography are important factors affecting the relationships between trees and water availability. This suggests that forests can be used as a nature-based solution for conserving and managing natural resources, including water, soil, and air. To sum up, forests can reduce people’s footprint, thanks to their role in improving water and air quality, conserving soil, and other ecosystem services. The outcomes of this study should be valuable for decision-makers in understanding the types of forests that can be used in an area, following an approach of environmental sustainability and conservation aiming at restoring hydrological services, mitigating the costs of environmental services, promoting sustainable land use, managing water resources, and preserving and restoring soil water availability (SWA) when investing in reforestation for watershed hydrology, which is important for the human population and other activities. Full article
(This article belongs to the Special Issue Soil Dynamics and Water Resource Management)
24 pages, 535 KiB  
Review
Current Management of Locally Recurrent Rectal Cancer
by Claudio Coco, Gianluca Rizzo, Luca Emanuele Amodio, Donato Paolo Pafundi, Federica Marzi and Vincenzo Tondolo
Cancers 2024, 16(23), 3906; https://doi.org/10.3390/cancers16233906 - 21 Nov 2024
Abstract
Locally recurrent rectal cancer (LRRC), which occurs in 6–12% of patients previously treated with surgery, with or without pre-operative chemoradiation therapy, represents a complex and heterogeneous disease profoundly affecting the patient’s quality of life (QoL) and long-term survival. Its management usually requires a [...] Read more.
Locally recurrent rectal cancer (LRRC), which occurs in 6–12% of patients previously treated with surgery, with or without pre-operative chemoradiation therapy, represents a complex and heterogeneous disease profoundly affecting the patient’s quality of life (QoL) and long-term survival. Its management usually requires a multidisciplinary approach, to evaluate the several aspects of a LRRC, such as resectability or the best approach to reduce symptoms. Surgical treatment is more complex and usually needs high-volume centers to obtain a higher rate of radical (R0) resections and to reduce the rate of postoperative complications. Multiple factors related to the patient, to the primary tumor, and to the surgery for the primary tumor contribute to the development of local recurrence. Accurate pre-treatment staging of the recurrence is essential, and several classification systems are currently used for this purpose. Achieving an R0 resection through radical surgery remains the most critical factor for a favorable oncologic outcome, although both chemotherapy and radiotherapy play a significant role in facilitating this goal. If a R0 resection of a LRRC is not feasible, palliative treatment is mandatory to reduce the LRRC-related symptoms, especially pain, minimizing the effect of the recurrence on the QoL of the patients. The aim of this manuscript is to provide a comprehensive narrative review of the literature regarding the management of LRRC. Full article
(This article belongs to the Special Issue Advances in Cancer Therapeutics)
18 pages, 8011 KiB  
Article
Reduced Soil Quality but Increased Microbial Diversity in Cultivated Land Compared to Other Land-Use Types in the Longzhong Loess Plateau
by Hang Xiang, Jingjing Xu, Hang Yang, Jianchao Song and Xiaojun Yu
Agriculture 2024, 14(12), 2106; https://doi.org/10.3390/agriculture14122106 - 21 Nov 2024
Abstract
Soil microorganisms, as a vital part of terrestrial ecosystems, play a key role in sustaining essential soil functions. However, the impact of cultivated land (CL) on soil quality and microbial communities compared to other land-use types is still unclear. This study investigated the [...] Read more.
Soil microorganisms, as a vital part of terrestrial ecosystems, play a key role in sustaining essential soil functions. However, the impact of cultivated land (CL) on soil quality and microbial communities compared to other land-use types is still unclear. This study investigated the soil quality index (SQI) along with bacterial and fungal communities across various land-use types, including abandoned land, cultivated land, forest land, and grassland, in the Longzhong region of the Loess Plateau. The results showed that CL had the lowest SQI, but the diversity of soil bacterial and fungal communities in CL was significantly higher than that of other land-use types. The relative abundance of Ascomycota in CL fungal communities is significantly higher than that of other land-use types. Soil water content, organic matter, alkaline nitrogen, total nitrogen, and nitrate nitrogen all have an impact on soil bacterial and fungal communities in CL. The diversity of soil bacterial and fungal communities is mainly influenced by pH, nitrate nitrogen, and available phosphorus. This study emphasizes the impact of human activities such as tillage on soil quality, as well as the structure and diversity of soil microbial communities, in cultivated land compared to other different land-use methods. Full article
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<p>Location of the study area and experimental sites. AL, abandoned land; CL, cultivated land; FL, forest land; GL, grassland.</p>
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<p>SQI values across various land-use types. AL, abandoned land; CL, cultivated land; FL, forest land; GL, grassland. Different letters show significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Relative abundance of soil bacteria at phylum (<b>a</b>) and genus levels (<b>b</b>). Relative abundance of soil fungi at phylum level (<b>c</b>) and genus level (<b>d</b>). AL: abandoned land; CL: cultivated land; FL: forest land; GL: grassland (feature = 15).</p>
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<p>Venn diagram showing the OTUs of soil microbes (<b>a</b>), bacterial OTUs (<b>b</b>), and fungal OTUs (<b>c</b>) across various land-use types. AL, abandoned land; CL, cultivated land; FL, forest land; GL, grassland.</p>
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<p>Differences in the compositions of soil bacterial (<b>a</b>) and fungal (<b>b</b>) communities under different land-use types were analyzed using linear discriminant analysis effect size (LEfSe). Each small circle at a classification level represents a taxon, with the diameter indicating the relative abundance of that taxon. Species with no significant differences are uniformly colored in yellow, while those with significant differences are represented by colored biomarkers. The names of species associated with the biomarkers are displayed on the right side, with letters and numbers corresponding to the figure. AL, abandoned land; CL, cultivated land; FL, forest land; GL, grassland.</p>
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<p>Soil bacterial (<b>a</b>) and fungal (<b>b</b>) community diversity indexes across different land-use patterns. Letters indicate significant differences in diversity (<span class="html-italic">p</span> &lt; 0.05). AL, abandoned land; CL, cultivated land; FL, forest land; GL, grassland.</p>
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<p>Principal component analysis (PCoA) of bacterial (<b>a</b>) and fungal (<b>b</b>) communities in different land-use types. Nodes indicate the OTUs, with distinct modules displayed in various colors. The red line represents a positive correlation, while the green line denotes a negative correlation. AL, abandoned land; CL, cultivated land; FL, forest land; GL, grassland.</p>
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<p>Co-occurrence networks of soil bacterial (<b>a</b>) and fungal (<b>b</b>) communities across different land-use types. AL, abandoned land; CL, cultivated land; FL, forest land; GL, grassland.</p>
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<p>Redundancy analysis (RDA) of bacterial (<b>a</b>) and fungal (<b>b</b>) communities and physicochemical properties of soil across different land-use types. AL, abandoned land; CL, cultivated land; FL, forest land; GL, grassland; SWC, soil water content; BD, bulk density; SOM, soil organic matter; TN, total nitrogen; AN, ammonia nitrogen; NN, nitrate nitrogen; SAN, alkali-hydrolyzable nitrogen; TP, total phosphorus; AP, available phosphorus.</p>
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<p>Mantel test showing the correlations between soil factors and microbial diversity. The width of the edges represents the absolute value of the correlation coefficients derived from the Mantel test. Colors show the strength of significant correlations. Pairwise comparisons of soil factors are displayed in rectangles, with color gradients demonstrating Pearson’s correlation coefficients. AL, abandoned land; CL, cultivated land; FL, forest land; GL, grassland; SWC, soil water content; BD, bulk density; SOM, soil organic matter; TN, total nitrogen; AN, ammonia nitrogen; NN, nitrate nitrogen; SAN, alkali-hydrolyzable nitrogen; TP, total phosphorus; AP, available phosphorus; MBC, microbial biomass carbon; MBN, microbial biomass nitrogen; MBP, microbial biomass phosphorus; UER, soil urease; ALP, soil alkaline phosphatase; CAT, soil catalase; SCL, soil cellulase; SUC, soil sucrase; PPO, soil polyphenol oxidase.</p>
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13 pages, 693 KiB  
Article
Environmental Racism in the Accessibility of Urban Green Space: A Case Study of a Metropolitan Area in an Emerging Economy
by Adriano Bressane, Anna Isabel Silva Loureiro and Rogério Galante Negri
Urban Sci. 2024, 8(4), 224; https://doi.org/10.3390/urbansci8040224 - 21 Nov 2024
Abstract
Urban Green Spaces (UGS) are integral to advancing urban sustainability and improving the quality of life in cities. However, in rapidly urbanizing regions like the São Paulo Metropolitan Region (MRSP), significant environmental injustices in UGS accessibility present a complex challenge that requires in-depth [...] Read more.
Urban Green Spaces (UGS) are integral to advancing urban sustainability and improving the quality of life in cities. However, in rapidly urbanizing regions like the São Paulo Metropolitan Region (MRSP), significant environmental injustices in UGS accessibility present a complex challenge that requires in-depth understanding. Notably, existing studies predominantly focus on developed countries, leaving a gap in research concerning emerging economies in the Global South. This study aims to analyze the associations between sociodemographic factors and environmental racism in UGS accessibility within the municipalities of MRSP. The research utilizes Spearman Rank Correlation and multiple linear regression analyses on data sourced from the Brazilian Institute of Geography and Statistics and the Urban Green Data Platform. Key variables include the number of inhabitants, territorial area, population density, urbanization rate, gross domestic product (GDP), human development index (HDI), urban vegetation coverage, UGS per capita, and the difference between the total population and the Black or Indigenous populations residing outside the vicinity of UGSs as an indicator of environmental racism. The findings reveal significant correlations between higher GDP and HDI with increased environmental racism in UGS accessibility, suggesting that, in the absence of equitable policies, economic and human development may exacerbate disparities in green space distribution. Moreover, the study demonstrates that increased urban vegetation coverage is significantly associated with reduced environmental disparities, underscoring the role of urban greenery in mitigating inequality. These results emphasize the need for comprehensive urban planning and targeted policies that prioritize the equitable development of UGS, particularly in underserved areas. Future research should explore longitudinal data to establish causality and consider additional variables such as political governance and cultural factors, which could provide a more comprehensive understanding of environmental racism in UGS accessibility. Full article
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<p>Relationships between sociodemographic factors and environmental racism in urban green space accessibility.</p>
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64 pages, 4621 KiB  
Review
A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring
by Haoyuan Zhang, Shanglei Jiang, Defeng Gao, Yuwen Sun and Wenxiang Bai
Machines 2024, 12(12), 833; https://doi.org/10.3390/machines12120833 - 21 Nov 2024
Abstract
Abstract: Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two [...] Read more.
Abstract: Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two decades, scholars have conducted extensive research on tool wear monitoring (TWM) and obtained a series of remarkable research achievements. However, physics-based models have difficulty predicting tool wear accurately. Meanwhile, the diversity of actual machining environments further limits the application of physical models. Data-driven models can establish the deep mapping relationship between signals and tool wear, but they only fit trained data well. They still have difficulty adapting to complex machining conditions. In this paper, physics-based and data-driven TWM models are first reviewed in detail, including the factors that affect tool wear, typical data-based models, and methods for extracting and selecting features. Then, tracking research hotspots, emerging physics–data fusion models are systematically summarized. Full article
(This article belongs to the Section Advanced Manufacturing)
10 pages, 278 KiB  
Article
Evaluating the Implementation of Adolescent- and Youth-Friendly Services in the Selected Primary Healthcare Facilities in Vhembe District, Limpopo Province
by Mukovhe Rammela and Lufuno Makhado
Int. J. Environ. Res. Public Health 2024, 21(12), 1543; https://doi.org/10.3390/ijerph21121543 - 21 Nov 2024
Abstract
Background: The adolescent- and youth-friendly services (AYFS) programme has the potential to address several diverse problems within adolescents’ healthcare systems by improving the quality, accessibility, efficiency, and effectiveness of healthcare services. The country continues to suffer from structural and systemic factors that hinder [...] Read more.
Background: The adolescent- and youth-friendly services (AYFS) programme has the potential to address several diverse problems within adolescents’ healthcare systems by improving the quality, accessibility, efficiency, and effectiveness of healthcare services. The country continues to suffer from structural and systemic factors that hinder the effective provision and implementation of AYFS despite its comprehensive legal and policy framework and commitment to enhancing young people’s health. Vhembe District has not been evaluated regarding the implementation of AYFS based on WHO global standards. Therefore, the objective of this study was to evaluate the implementation of AYFS against the World Health Organization (WHO) global standards for quality healthcare services for adolescents to strengthen these services in Vhembe District, Limpopo. Methods: A cross-sectional study was used to evaluate the implementation of AYFS against the WHO global standards for quality healthcare services for adolescents in Vhembe District, Limpopo. Evaluating the implementation of AYFS was conducted through questionnaires distributed to healthcare providers in the selected primary healthcare facilities in Vhembe District. For descriptive statistical analysis, research data were analysed using Statistical Package for the Social Sciences (SPSS). Results: The AYFS have been evaluated in depth across eight WHO global standards for quality health-care services for adolescents, with areas of success and areas for improvement identified. Provider competency reveals a disparity, with a majority (67.0%) of healthcare providers trained in effective communication with adolescents. In comparison, significantly fewer have received specific training in AYFS (16%) or on Pre-Exposure Prophylaxis (PrEP) (25.9%), underscoring the need for a more balanced approach to training focus. Conclusion: Research findings highlight the strengths and gaps of AYFS in Vhembe District, aligned with government and WHO priorities for adolescent health. Addressing the identified gaps is vital to ensuring that healthcare facilities are adolescent- and youth-friendly, easily accessible, and can be implemented effectively to address adolescent and youth health challenges in Vhembe District. Full article
(This article belongs to the Special Issue Prevention and Management of Sexually Transmitted Disease)
18 pages, 7048 KiB  
Article
Evaluation of Promising Characteristics of Rhizomatous Alfalfa Male Sterile Mutant Accessions
by Ming Wang, Shangli Shi, Wenjuan Kang, Fang Jing, Xi Cheng, Yuanyuan Du and Yilin Han
Agronomy 2024, 14(12), 2759; https://doi.org/10.3390/agronomy14122759 - 21 Nov 2024
Abstract
Evaluating key traits of male sterile mutant accessions in rhizomatous alfalfa (Medicago sativa L.) is crucial for selecting plants for artificial hybrid breeding of rhizomatous maternal lines. In this study, branch cuttings from four male sterile mutant accessions of ‘Qingshui’ alfalfa were [...] Read more.
Evaluating key traits of male sterile mutant accessions in rhizomatous alfalfa (Medicago sativa L.) is crucial for selecting plants for artificial hybrid breeding of rhizomatous maternal lines. In this study, branch cuttings from four male sterile mutant accessions of ‘Qingshui’ alfalfa were used as experimental samples. We evaluated phenotypic traits, which included pollen viability and stigma receptivity, as well as nutritional quality, using difference analysis, correlation analysis, and principal component analysis. Prioritizing pollen viability and stigma receptivity, while considering phenotypic traits and nutritional quality as supplementary factors, allowed us to comprehensively evaluate 24 rhizomatous alfalfa individuals. This evaluation led to the identification of four male sterile mutant accessions with superior traits. The pollen from accession 4-4 was found to be partially fertile, whereas the remaining 23 alfalfa individuals were entirely male sterile. All 24 individuals exhibited stigma receptivity levels suitable for effective pollination. Principal component analysis revealed that among the assessed traits, the leaf–stem ratio contributed most significantly, followed by crude protein content, while neutral detergent fiber content had the least impact on overall quality. Additionally, the number of branches showed a strong positive correlation with individual plant yield (p < 0.01). No significant correlations were detected among plant height, stem diameter, forage grading index, crude protein, neutral detergent fiber, acid detergent fiber content, and yield. Overall, our comprehensive evaluation suggests that accessions 1-2, 2-2, 3-1, and 4-3 are most suitable for use as parental lines in artificial hybrid breeding. Full article
(This article belongs to the Section Horticultural and Floricultural Crops)
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<p>Comparison of pollen viability among 24 alfalfa plants. After one-way ANOVA, different letters indicate significant differences determined by Tukey’s HSD test. The lower-case letters in different columns show significant differences between accessions at the 0.05 level.</p>
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<p>Comparison of stigma receptivity in 24 individual alfalfa plants. After one-way ANOVA, different letters indicate significant differences determined by Tukey’s HSD test. The lower-case letters in different columns show significant differences between accessions at the 0.05 level.</p>
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<p>Phenotypic traits analysis of 23 individual alfalfa plants. (<b>A</b>) Plant height, (<b>B</b>) Stem thickness, (<b>C</b>) Number of branches, (<b>D</b>) Leaf–stem ratio, (<b>E</b>) Fresh weight per plant, (<b>F</b>) Dry weight per plant, (<b>G</b>) Stem dry weight, (<b>H</b>) Leaf dry weight. Red, blue, yellow, and green in the figure represent QS1, QS2, QS3, and QS4. After one-way ANOVA, different letters indicate significant differences determined by Tukey’s HSD test. The lower-case letters in different columns show significant differences between accessions at the 0.05 level.</p>
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<p>Analysis of nutritional indexes and variability in 23 individual alfalfa plants. (<b>A</b>) Crude protein (CP), (<b>B</b>) Ether extract (EE), (<b>C</b>) Crude ash (Ash), (<b>D</b>) Neutral detergent fiber (NDF), (<b>E</b>) Acid detergent fiber (ADF), and (<b>F</b>) Forage grading index (GI). Red, blue, yellow, and green in the figure represent QS1, QS2, QS3, and QS4. After one-way ANOVA, different letters indicate significant differences determined by Tukey’s HSD test. The lower-case letters in different columns show significant differences between accessions at the 0.05 level.</p>
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<p>Correlation analysis of phenotypic traits and nutritional indexes in 23 individual alfalfa plants. (**) Indicates highly significant correlation at the 0.01 level, and (*) indicates highly significant correlation at the 0.05 level. The narrower the ellipse, the stronger the correlation; conversely, the wider the ellipse, the weaker the correlation. CP—crude protein; NDF—neutral detergent fiber; ADF—acid detergent fiber; EE—ether extract; Ash—crude ash; GI—forage grading index.</p>
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19 pages, 764 KiB  
Systematic Review
Impacts of Square Stepping Exercise on Physical-Cognitive Function, Biomarkers, Body Composition and Mental Health in Healthy Senior Aged 60 and Above: A Systematic Review
by Juan Manuel Franco-García, Jorge Carlos-Vivas, Antonio Castillo-Paredes, Noelia Mayordomo-Pinilla, Jorge Rojo-Ramos and Jorge Pérez-Gómez
Healthcare 2024, 12(23), 2325; https://doi.org/10.3390/healthcare12232325 - 21 Nov 2024
Abstract
Background: The aim of this systematic review is to analyze the effects of Square Stepping Exercise (SSE) on physical and cognitive function in older people, including its effects on biomarkers, body composition and mental health, focusing only on research that assessed the [...] Read more.
Background: The aim of this systematic review is to analyze the effects of Square Stepping Exercise (SSE) on physical and cognitive function in older people, including its effects on biomarkers, body composition and mental health, focusing only on research that assessed the efficacy of SSE-based interventions. Methods: PubMed, Web of Science, Scopus and Cochrane databases were searched from June 2006 to June 2024 according to the PRISMA guidelines. The main search terms used were related to “older people” and “square-stepping exercise”. Controlled trials that included at least one intervention group focused on SSE were included. Participants had to be healthy, without physical or cognitive impairment, and the studies published in English or Spanish. The methodological quality of the selected research was assessed using the Physiotherapy Evidence Database (PEDro). Results: Twelve studies were selected from a total of 444 original records, with a total sample size of 577 participants. The health parameters of the participants were homogeneous, with ages ranging from 60 to 80 years. Significant gains were reported in certain physical function assessments, including balance, lower body strength and power, gait speed and flexibility. There were also significant findings in cognitive function, particularly in general cognitive status, focused attention, response time, basic task performance, and executive function. In addition, SSE can improve metrics such as body composition, brain-derived neurotrophic factor (BDNF), and mental health characteristics. Conclusions: SSE has the potential to significantly improve physical function, cognitive performance and body composition, as well as provide mental health benefits and have variable effects on biomarkers and cardiovascular health. Full article
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<p>PRISMA flow chart illustrates the exclusion criteria and study selection.</p>
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12 pages, 1608 KiB  
Review
The Biotechnological Potential of Crickets as a Sustainable Protein Source for Fishmeal Replacement in Aquafeed
by Aldo Fraijo-Valenzuela, Joe Luis Arias-Moscoso, Oscar Daniel García-Pérez, Libia Zulema Rodriguez-Anaya and Jose Reyes Gonzalez-Galaviz
BioTech 2024, 13(4), 51; https://doi.org/10.3390/biotech13040051 - 21 Nov 2024
Abstract
As aquaculture production grows, so does the demand for quality and cost-effective protein sources. The cost of fishmeal (FM) has increased over the years, leading to increased production costs for formulated aquafeed. Soybean meal (SBM) is commonly used as an FM replacer in [...] Read more.
As aquaculture production grows, so does the demand for quality and cost-effective protein sources. The cost of fishmeal (FM) has increased over the years, leading to increased production costs for formulated aquafeed. Soybean meal (SBM) is commonly used as an FM replacer in aquafeed, but anti-nutritional factors could affect the growth, nutrition, and health of aquatic organisms. Cricket meal (CM) is an alternative source with a nutrient profile comparable to FM due to its high protein content, digestibility, and amino acid profile. CM use in aquafeed influences growth and reproductive performance while modulating the gut microbiota and immune response of fish and shrimp. However, consistent regulation and scaling up are necessary for competitive prices and the marketing of CM. Moreover, the chitin content in CM could be an issue in some fish species; however, different strategies based on food biotechnology can improve the protein quality for its safe use in aquafeed. Full article
(This article belongs to the Section Agricultural and Food Biotechnology)
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<p>Nutritional composition of cricket meal. Created with BioRender.com (accessed on 9 October 2024).</p>
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<p>Illustration of cricket meal’s effects on aquaculture. Created with BioRender.com (accessed on 9 October 2024).</p>
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<p>Illustration of different processing methods to improve protein quality of cricket meal. Created with BioRender.com (accessed on 9 October 2024).</p>
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21 pages, 7859 KiB  
Article
Flavonoid Fisetin Alleviates Ovarian Aging of Laying Chickens by Enhancing Antioxidant Capacity and Glucose Metabolic Homeostasis
by Zhaoyu Yang, Jiaxuan Zhang, Qiongyu Yuan, Xinyu Wang, Weidong Zeng, Yuling Mi and Caiqiao Zhang
Antioxidants 2024, 13(12), 1432; https://doi.org/10.3390/antiox13121432 - 21 Nov 2024
Abstract
Oxidative stress is a crucial factor contributing to ovarian follicular atresia and an imbalance in ovarian energy metabolism in poultry, leading to decreased laying performance in aging hens. This study aimed to investigate the effects of a natural flavonoid, fisetin, on laying performance, [...] Read more.
Oxidative stress is a crucial factor contributing to ovarian follicular atresia and an imbalance in ovarian energy metabolism in poultry, leading to decreased laying performance in aging hens. This study aimed to investigate the effects of a natural flavonoid, fisetin, on laying performance, ovarian redox status, and energy metabolism in laying chickens. The results showed that dietary fisetin supplementation improved egg production and eggshell quality in aging laying chickens, reduced follicular atresia rate, promoted ovarian cell proliferation, elevated serum estrogen and progesterone levels, restored ovarian antioxidant capacity, and improved energy metabolism. Furthermore, fisetin treatment increased the activity of antioxidant enzymes by inhibiting NF-κB signaling and COX-2 expression while promoting SIRT1 expression in the H2O2-induced small white follicle (SWF). Additionally, fisetin significantly enhanced the anti-apoptotic capacity of SWF and promoted glucose catabolism by activating the AKT and JNK signaling pathways. In summary, fisetin supplementation can alleviate ovarian oxidative stress in aging laying chickens by upregulating SIRT1 expression and inhibiting NF-κB signaling. The activation of AKT and JNK signaling pathways by fisetin contributes to the balance of energy metabolism and promotion of follicular development in the ovaries of aging laying chickens, thereby retarding ovarian aging in poultry production. Full article
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<p>Effect of fisetin on follicle development, egg quality, and hormone balance in NA-OF model. (<b>A</b>) Experimental design of fisetin treatment and analysis in NA-OF model. (<b>B</b>) Scanning electron microscope (SEM) images of eggshells, displaying cross-sections, outer surfaces, and inner surfaces. TT: Total thickness; ET: effective thickness. White arrows indicate calcareous fibers. (<b>C</b>) Eggshell effective thickness (<span class="html-italic">n</span> = 6). (<b>D</b>) Ovaries of aged laying chickens (580-day-old). Avian ovarian follicles are generally categorized by size or color: large preovulatory follicles (F1, F2, F3, etc.), small yellow follicle (SYF), large white follicle (LWF), and small white follicle (SWF). Black arrow: post-ovulation follicle; AF: atretic follicle. (<b>E</b>) Levels of serum estrogen (E<sub>2</sub>) and progesterone (P<sub>4</sub>) (<span class="html-italic">n</span> = 15). (<b>F</b>) Relative mRNA expression of steroid synthesis-related genes (<span class="html-italic">CYP11A1</span>, <span class="html-italic">CYP19A1</span>) in SWF. (<b>G</b>) Images and rate of atretic follicles (<span class="html-italic">n</span> = 6). (<b>H</b>) Western blot and quantitative analysis of CYP11A1 expression in SWF (<span class="html-italic">n</span> = 3). Values are shown as mean ± SEM. Different letters represent statistically significant differences among the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of fisetin on proliferation and antioxidant enzymes in NA-OF model. (<b>A</b>) Typical images of PCNA expression (TRITC, red) in the ovary and SWF from young (280-day-old) and aged (580-day-old) laying chickens. TL: theca layer; GL: granulosa layer; GF: growing follicle. Yellow arrow: PCNA-positive cell. Nuclei were stained blue with DAPI. Scale bars: 50 μm and 100 μm. (<b>B</b>) PCNA positivity rate of SWF from aged laying chickens (<span class="html-italic">n</span> = 6). The proliferative level was determined by the PCNA positivity rate. (<b>C</b>) Western blotting showing the expression of PCNA, BAX, and Caspase-3 in SWF (<span class="html-italic">n</span> = 3). (<b>D</b>) The mRNA expression levels of cell cycle-related genes (<span class="html-italic">PCNA</span>, <span class="html-italic">CCND1</span>, <span class="html-italic">CDK2</span>, <span class="html-italic">Bcl-2</span>, <span class="html-italic">Bax</span>, <span class="html-italic">Caspase 3</span>, and <span class="html-italic">Caspase 9</span>) in SWF. (<b>E</b>) H&amp;E staining ovaries harvested from young and aged laying chickens. Asterisk: atretic ovarian follicle. (<b>F</b>) Relative mRNA expression of antioxidant-related genes (<span class="html-italic">Mgst</span>, <span class="html-italic">Gsta</span>, <span class="html-italic">Gsr</span>, <span class="html-italic">Cat</span>, and <span class="html-italic">Sod</span>) in SWF (<span class="html-italic">n</span> = 6). (<b>G</b>) Levels of oxidative- and antioxidant-related parameters (T-SOD, CAT, GSH-px, GSH-ST, T-AOC, GSH, MDA, and H<sub>2</sub>O<sub>2</sub>) in SWF (<span class="html-italic">n</span> = 6). Values are shown as mean ± SEM. Different letters represent statistically significant differences among the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>Effect of fisetin on ovarian glucose catabolism in the NA-OF model. (<b>A</b>) Immunofluorescence images of ovaries stained with GLUT1, PFKFB2, and LDHA antibodies (Red: positive cells; Blue: DAPI), and images in the white dotted boxes were enlarged on the right. Scale bar: 50 μm and 20 μm. Green arrow: cyst germ cell (CGC); white arrow: primordial follicle (PF); GF: growing follicle. (<b>B</b>) Relative protein expression of glucose metabolism-related enzymes (GLUT1, PFKFB2, HK2, SDHA) in SWF (<span class="html-italic">n</span> = 3). (<b>C</b>) Relative mRNA expressions of glycolysis-related genes (<span class="html-italic">GLUT1</span>, <span class="html-italic">HK1</span>, <span class="html-italic">LDHA</span>, <span class="html-italic">PFKP</span>, <span class="html-italic">SDHA</span>, <span class="html-italic">IDH1</span>, and <span class="html-italic">PKM</span>) in SWF. (<b>D</b>) Total contents of pyruvate, lactate, and ATP in SWF (<span class="html-italic">n</span> = 6). Values are shown as mean ± SEM. Different letters represent statistically significant differences among the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 4
<p>Effect of fisetin on the HA-OF model. (<b>A</b>) H&amp;E staining images of SWF, scale bar: 100 μm. The red arrowhead indicates granulosa cell layer. (<b>B</b>) Immunofluorescence images of SWF stained with BrdU (red), scale bar: 50 μm. (<b>C</b>) TUNEL-stained (green) images of SWF, scale bar: 20 μm. (<b>D</b>) BrdU positivity rates and mRNA levels of cell cycle-related genes (<span class="html-italic">CDK2</span>, <span class="html-italic">PCNA</span>) in SWF (<span class="html-italic">n</span> = 4). (<b>E</b>) TUNEL positivity rates and relative expression levels of apoptosis-related genes (<span class="html-italic">Caspase 3</span>, <span class="html-italic">Caspase 9</span>) in SWF (<span class="html-italic">n</span> = 4). (<b>F</b>) Western blot analysis of GLUT1, HK2, LDHA, Caspase-3, and PCNA in SWF (<span class="html-italic">n</span> = 3). Values are presented as mean ± SEM. Different letters represent statistically significant differences among the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 5
<p>Effect of fisetin on antioxidant- and glucose metabolism-related enzymes in the HA-OF model. (<b>A</b>) Levels of oxidation- and antioxidant-related parameters (CAT, GSH-ST, GSH-px, T-SOD, T-AOC, GSH, MDA, and H<sub>2</sub>O<sub>2</sub>) in SWF (<span class="html-italic">n</span> = 6). (<b>B</b>) Levels of lactate, pyruvate, and ATP in SWF (<span class="html-italic">n</span> = 6). (<b>C</b>) Relative mRNA expression of antioxidant enzyme genes (<span class="html-italic">Gsr</span>, <span class="html-italic">Mgst</span>, <span class="html-italic">Cat</span>, <span class="html-italic">Sod</span>, and <span class="html-italic">Trx</span>) and glucose metabolism-related enzyme genes (<span class="html-italic">HK1</span>, <span class="html-italic">HK2</span>, <span class="html-italic">PFKL</span>, <span class="html-italic">IDH1</span>, <span class="html-italic">PFKM</span>, <span class="html-italic">PKM</span>, <span class="html-italic">SDHA</span>, <span class="html-italic">SDHB</span>, <span class="html-italic">LDHA</span>, and <span class="html-italic">LDHB</span>) in SWF (<span class="html-italic">n</span> = 4). Values are shown as mean ± SEM. Different letters represent statistically significant differences among the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 6
<p>Effect of fisetin on the NF-κB signaling pathway in the HA-OF model. (<b>A</b>) Inhibition of NF-κB by fisetin in H<sub>2</sub>O<sub>2</sub>-induced SWF, as revealed by Western blot analysis (<span class="html-italic">n</span> = 3). Phosphorylated NF-κB p65, total NF-κB p65, SIRT-1, and COX-2 were analyzed. (<b>B</b>) Immunohistochemical staining for SIRT-1 and p-p65 in SWF. Black arrows indicate the localization of p-p65, primarily expressed in granulosa cells. Red arrows indicate the localization of SIRT-1, primarily expressed in theca cells. (<b>C</b>) Expression of p-p65 in ovaries (FITC, green). White arrows indicate the presence of phosphorylated p65 in ovarian follicles and its translocation to the nucleus. Scale bar: 20 μm. (<b>D</b>) Levels of oxidative and antioxidant-related parameters. (<b>E</b>) Western blot analysis of p-p65/p65, SIRT-1, and COX-2 in SWF (<span class="html-italic">n</span> = 3). (<b>F</b>) Levels of <span class="html-italic">Cat</span>, <span class="html-italic">Sod2</span>, and <span class="html-italic">Mgst</span> mRNAs in SWF (<span class="html-italic">n</span> = 3). Values are presented as mean ± SEM. Different letters represent statistically significant differences among the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 7
<p>Effect of fisetin on the AKT signaling and the JNK signaling in the HA-OF model. (<b>A</b>) Immunohistochemical staining of p-JNK and p-AKT in SWF. Scale bars: 20 μm and 50 μm. (<b>B</b>) Activation of AKT signaling by fisetin in the H<sub>2</sub>O<sub>2</sub>-induced SWF. Phosphorylated AKT (Ser473) and total AKT were examined by Western blot. Quantification of AKT and its downstream signaling (p-AKT/AKT, PCNA, CCND1, BAX, BCL-2, and Caspase 3) was plotted on the right. The apoptotic level was determined by the normalized ratio of cleaved to total Caspase 3 and the protein quantification of BAX (<span class="html-italic">n</span> = 3). (<b>C</b>) Activation of JNK and regulation of glucose metabolism-related enzyme expression by fisetin, revealed by Western blot. Quantification of p-JNK/JNK, GLUT1, HK2, PFKFB2, LDHA, and SDHA is plotted on the right (<span class="html-italic">n</span> = 3). Values are shown as mean ± SEM. Different letters represent statistically significant differences among the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 8
<p>Fisetin promoted ovarian energy metabolism of laying chickens via AKT and JNK signaling pathways. (<b>A</b>) Activation of AKT and JNK signaling was inhibited by AT (100 μM). Total AKT, phosphorylated AKT, total JNK, and phosphorylated JNK were assessed in SWF by Western blot analysis. (<b>B</b>) Western blot and quantitative analyses of total AKT, phosphorylated AKT, total JNK and phosphorylated JNK expression in SWF (<span class="html-italic">n</span> = 3) after SP treatment (50 μM). (<b>C</b>) Western blot and quantitative analyses of GLUT1, HK2, PFKFB2, LDHA, and SDHA after SP treatment (50 μM) in SWF (<span class="html-italic">n</span> = 3). (<b>D</b>) Immunofluorescence images of ovaries stained with GLUT1 (TRITC, red). Scale bar: 20 μm. (<b>E</b>) Levels of lactate and pyruvate in SWF (<span class="html-italic">n</span> = 6). Values are shown as mean ± SEM. Different letters represent statistically significant differences among the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 9
<p>Effect of fisetin on the growth and development, antioxidant properties, and energy metabolism of aged SWF in vitro. (<b>A</b>) Representative images of BrdU (red) and TUNEL (green) assay of SWF in each group. The red dots represent proliferation-positive cells, and green dots represent apoptosis-positive cells. Scale bar: 50 μm. (<b>B</b>) Quantification of BrdU and TUNEL assay (<span class="html-italic">n</span> = 6). (<b>C</b>) Representative H&amp;E staining images of SWF after 72 h culture in vitro, and images in the black dotted boxes were enlarged on the right. (<b>D</b>) Protein expression levels of BAX and BCL-2 in aged SWF (<span class="html-italic">n</span> = 3). (<b>E</b>) Relative mRNA levels of cell cycle-related genes (<span class="html-italic">PCNA</span>, <span class="html-italic">CCND1</span>, <span class="html-italic">CDK6</span>, <span class="html-italic">Bcl-2</span>, <span class="html-italic">Bax</span>, <span class="html-italic">Caspase 8</span>, and <span class="html-italic">Caspase 9</span>) in aged SWF (<span class="html-italic">n</span> = 3). (<b>F</b>) Levels of CAT, T-SOD, GSH-ST, GSH-Px, T-AOC, GSH, H<sub>2</sub>O<sub>2</sub>, and MDA in aged SWF (<span class="html-italic">n</span> = 6). (<b>G</b>) Relative mRNA levels of antioxidant-related genes (<span class="html-italic">Sod</span>, <span class="html-italic">Cat</span>, <span class="html-italic">Mgst</span>, <span class="html-italic">Gsr</span>, <span class="html-italic">Gsta</span>, <span class="html-italic">Trx</span>, and <span class="html-italic">Gclm</span>) in aged SWF (<span class="html-italic">n</span> = 4). (<b>H</b>) Transcription levels of glucose metabolism-related genes (<span class="html-italic">PKM</span>, <span class="html-italic">PFKM</span>, <span class="html-italic">LDHB</span>, <span class="html-italic">HK1</span>, <span class="html-italic">PFKL</span>, <span class="html-italic">LDHA</span>, and <span class="html-italic">SDHB</span>) in aged SWF (<span class="html-italic">n</span> = 4). (<b>I</b>) Contents of lactate, pyruvate and ATP in aged SWF (<span class="html-italic">n</span> = 6). (<b>J</b>) Relative protein levels of GLUT1, HK2, PFKFB2, and SDHA in aged SWF (<span class="html-italic">n</span> = 3). Values are shown as mean ± SEM. Different letters represent statistically significant differences among the groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 10
<p>Schematic diagram illustrating the mechanism by which fisetin alleviates ovarian aging in laying chickens.</p>
Full article ">
33 pages, 45495 KiB  
Article
Peplospheric Influences on Local Greenhouse Gas and Aerosol Variability at the Lamezia Terme WMO/GAW Regional Station in Calabria, Southern Italy: A Multiparameter Investigation
by Francesco D’Amico, Claudia Roberta Calidonna, Ivano Ammoscato, Daniel Gullì, Luana Malacaria, Salvatore Sinopoli, Giorgia De Benedetto and Teresa Lo Feudo
Sustainability 2024, 16(23), 10175; https://doi.org/10.3390/su162310175 - 21 Nov 2024
Abstract
One of the keys towards sustainable policies and advanced air quality monitoring is the detailed assessment of all factors that affect the surface concentrations of greenhouse gases (GHGs) and aerosols. While the development of new atmospheric tracers can pinpoint emission sources, the atmosphere [...] Read more.
One of the keys towards sustainable policies and advanced air quality monitoring is the detailed assessment of all factors that affect the surface concentrations of greenhouse gases (GHGs) and aerosols. While the development of new atmospheric tracers can pinpoint emission sources, the atmosphere itself plays a relevant role even at local scales: Its dynamics can increase, or reduce, surface concentrations of pollutants harmful to human health and the environment. PBL (planetary boundary layer), or peplospheric, variability is known to affect such concentrations. In this study, an unprecedented characterization of PBL cycles and patterns is performed at the WMO/GAW regional coastal site of Lamezia Terme (code: LMT) in Calabria, Southern Italy, in conjunction with the analysis of key GHGs and aerosols. The analysis, accounting for five months of 2024 data, indicates that peplospheric variability and wind regimes influence the concentrations of key GHGs and aerosols. In particular, PBLH (PBL height) patterns have been tested to further influence the surface concentrations of carbon monoxide (CO), black carbon (BC), and particulate matter (PM). This research introduces four distinct wind regimes at LMT: breeze, not complete breeze, eastern synoptic, and western synoptic, each with its peculiar influences on the local transport of gases and aerosols. This research demonstrates that peplosphere monitoring needs to be considered when ensuring optimal air quality in urban and rural areas. Full article
(This article belongs to the Special Issue Sustainable Climate Action for Global Health)
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Figure 1

Figure 1
<p>(<b>A</b>) Modified Copernicus Digital Elevation Model [<a href="#B115-sustainability-16-10175" class="html-bibr">115</a>] of Europe, with a mark on LMT’s location. (<b>B</b>) Modified EMODnet [<a href="#B116-sustainability-16-10175" class="html-bibr">116</a>] highlighting LMT’s specific location in Southern Italy, within the region of Calabria. (<b>C</b>) Google Earth map, tilted by 70°, showing the observation site and key infrastructural/emission hotspots in the area. The “Highway” label indicates a point where the distance between LMT and the highway is ≈4.2 km. The “Lamezia Terme” label points to the town center. The “Station” label points to the busiest train station in the municipality of Lamezia Terme, the central one (<span class="html-italic">Lamezia Terme Centrale</span>).</p>
Full article ">Figure 1 Cont.
<p>(<b>A</b>) Modified Copernicus Digital Elevation Model [<a href="#B115-sustainability-16-10175" class="html-bibr">115</a>] of Europe, with a mark on LMT’s location. (<b>B</b>) Modified EMODnet [<a href="#B116-sustainability-16-10175" class="html-bibr">116</a>] highlighting LMT’s specific location in Southern Italy, within the region of Calabria. (<b>C</b>) Google Earth map, tilted by 70°, showing the observation site and key infrastructural/emission hotspots in the area. The “Highway” label indicates a point where the distance between LMT and the highway is ≈4.2 km. The “Lamezia Terme” label points to the town center. The “Station” label points to the busiest train station in the municipality of Lamezia Terme, the central one (<span class="html-italic">Lamezia Terme Centrale</span>).</p>
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<p>Wind rose based on hourly data gathered during the observation period (1 May–30 September 2024). Calm refers to the reported instances (0%) of a wind speed of 0 m/s.</p>
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<p>Daily averages of GHG and aerosol parameters evaluated in this research study: (<b>A</b>) carbon monoxide (CO), in ppm (parts per million); (<b>B</b>) carbon dioxide (CO<sub>2</sub>), in ppm; (<b>C</b>) methane (CH<sub>4</sub>), in ppm; (<b>D</b>) equivalent black carbon (eBC), in μg/m<sup>3</sup>; and (<b>E</b>) particulate matter (PM) in μg/m<sup>3</sup>, divided into the size ranges PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>4</sub>, and PM<sub>10</sub>. The gaps in CO, CO<sub>2</sub>, and CH<sub>4</sub> data shown in A-B-C are due to maintenance issues that affected the Picarro G2401. Similarly, Thermo Scientific 5012 MAAP data gathering was also affected by maintenance, as shown by a gap.</p>
Full article ">Figure 3 Cont.
<p>Daily averages of GHG and aerosol parameters evaluated in this research study: (<b>A</b>) carbon monoxide (CO), in ppm (parts per million); (<b>B</b>) carbon dioxide (CO<sub>2</sub>), in ppm; (<b>C</b>) methane (CH<sub>4</sub>), in ppm; (<b>D</b>) equivalent black carbon (eBC), in μg/m<sup>3</sup>; and (<b>E</b>) particulate matter (PM) in μg/m<sup>3</sup>, divided into the size ranges PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>4</sub>, and PM<sub>10</sub>. The gaps in CO, CO<sub>2</sub>, and CH<sub>4</sub> data shown in A-B-C are due to maintenance issues that affected the Picarro G2401. Similarly, Thermo Scientific 5012 MAAP data gathering was also affected by maintenance, as shown by a gap.</p>
Full article ">Figure 3 Cont.
<p>Daily averages of GHG and aerosol parameters evaluated in this research study: (<b>A</b>) carbon monoxide (CO), in ppm (parts per million); (<b>B</b>) carbon dioxide (CO<sub>2</sub>), in ppm; (<b>C</b>) methane (CH<sub>4</sub>), in ppm; (<b>D</b>) equivalent black carbon (eBC), in μg/m<sup>3</sup>; and (<b>E</b>) particulate matter (PM) in μg/m<sup>3</sup>, divided into the size ranges PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>4</sub>, and PM<sub>10</sub>. The gaps in CO, CO<sub>2</sub>, and CH<sub>4</sub> data shown in A-B-C are due to maintenance issues that affected the Picarro G2401. Similarly, Thermo Scientific 5012 MAAP data gathering was also affected by maintenance, as shown by a gap.</p>
Full article ">Figure 4
<p>Daily averages of environmental and meteorological data: (<b>A</b>) solar radiation in W/m<sup>2</sup>; (<b>B</b>) temperature in Celsius degrees, °C; (<b>C</b>) relative humidity, as a percentage (%); (<b>D</b>) scattering, as Mm<sup>−1</sup>.</p>
Full article ">Figure 4 Cont.
<p>Daily averages of environmental and meteorological data: (<b>A</b>) solar radiation in W/m<sup>2</sup>; (<b>B</b>) temperature in Celsius degrees, °C; (<b>C</b>) relative humidity, as a percentage (%); (<b>D</b>) scattering, as Mm<sup>−1</sup>.</p>
Full article ">Figure 5
<p>Hourly averages of GHG and aerosol parameters evaluated in this research study: (<b>A</b>) carbon monoxide (CO), in ppm (parts per million); (<b>B</b>) carbon dioxide (CO<sub>2</sub>), in ppm; (<b>C</b>) methane (CH<sub>4</sub>), in ppm; (<b>D</b>) equivalent black carbon (eBC), in μg/m<sup>3</sup>; and (<b>E</b>) particulate matter (PM) in μg/m<sup>3</sup>, divided into the size ranges PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>4</sub>, and PM<sub>10</sub>. 36 h moving averages of CO, CO<sub>2</sub>, CH<sub>4</sub> (pm), and eBC (μg/m<sup>3</sup>) are shown in cyan.</p>
Full article ">Figure 5 Cont.
<p>Hourly averages of GHG and aerosol parameters evaluated in this research study: (<b>A</b>) carbon monoxide (CO), in ppm (parts per million); (<b>B</b>) carbon dioxide (CO<sub>2</sub>), in ppm; (<b>C</b>) methane (CH<sub>4</sub>), in ppm; (<b>D</b>) equivalent black carbon (eBC), in μg/m<sup>3</sup>; and (<b>E</b>) particulate matter (PM) in μg/m<sup>3</sup>, divided into the size ranges PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>4</sub>, and PM<sub>10</sub>. 36 h moving averages of CO, CO<sub>2</sub>, CH<sub>4</sub> (pm), and eBC (μg/m<sup>3</sup>) are shown in cyan.</p>
Full article ">Figure 5 Cont.
<p>Hourly averages of GHG and aerosol parameters evaluated in this research study: (<b>A</b>) carbon monoxide (CO), in ppm (parts per million); (<b>B</b>) carbon dioxide (CO<sub>2</sub>), in ppm; (<b>C</b>) methane (CH<sub>4</sub>), in ppm; (<b>D</b>) equivalent black carbon (eBC), in μg/m<sup>3</sup>; and (<b>E</b>) particulate matter (PM) in μg/m<sup>3</sup>, divided into the size ranges PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>4</sub>, and PM<sub>10</sub>. 36 h moving averages of CO, CO<sub>2</sub>, CH<sub>4</sub> (pm), and eBC (μg/m<sup>3</sup>) are shown in cyan.</p>
Full article ">Figure 6
<p>Hourly averages of environmental and meteorological data: (<b>A</b>) temperature, in Celsius degrees, °C; (<b>B</b>) relative humidity, as a percentage (%); and (<b>C</b>) scattering, as Mm<sup>−1</sup>. 36 h moving averages of T (°C) and RH (%) are shown in dark red.</p>
Full article ">Figure 6 Cont.
<p>Hourly averages of environmental and meteorological data: (<b>A</b>) temperature, in Celsius degrees, °C; (<b>B</b>) relative humidity, as a percentage (%); and (<b>C</b>) scattering, as Mm<sup>−1</sup>. 36 h moving averages of T (°C) and RH (%) are shown in dark red.</p>
Full article ">Figure 7
<p>Daily cycles of GHG and aerosol parameters analyzed in this research study, divided by wind regime: (<b>A</b>) carbon monoxide (CO), in ppm (parts per million); (<b>B</b>) carbon dioxide (CO<sub>2</sub>), in ppm; (<b>C</b>) methane (CH<sub>4</sub>), in ppm; (<b>D</b>) equivalent black carbon (eBC), in μg/m<sup>3</sup>; (<b>E</b>) particulate matter (PM) in μg/m<sup>3</sup>, divided into the size ranges PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>4</sub>, and PM<sub>10</sub> but not accounting for wind regime categories; (<b>F</b>) PM<sub>2.5</sub>, with wind regimes; and (<b>G</b>) PM<sub>10</sub>, with wind regimes. Where present, shaded areas refer to intervals within one standard deviation (±σ) from the reported values.</p>
Full article ">Figure 7 Cont.
<p>Daily cycles of GHG and aerosol parameters analyzed in this research study, divided by wind regime: (<b>A</b>) carbon monoxide (CO), in ppm (parts per million); (<b>B</b>) carbon dioxide (CO<sub>2</sub>), in ppm; (<b>C</b>) methane (CH<sub>4</sub>), in ppm; (<b>D</b>) equivalent black carbon (eBC), in μg/m<sup>3</sup>; (<b>E</b>) particulate matter (PM) in μg/m<sup>3</sup>, divided into the size ranges PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>4</sub>, and PM<sub>10</sub> but not accounting for wind regime categories; (<b>F</b>) PM<sub>2.5</sub>, with wind regimes; and (<b>G</b>) PM<sub>10</sub>, with wind regimes. Where present, shaded areas refer to intervals within one standard deviation (±σ) from the reported values.</p>
Full article ">Figure 7 Cont.
<p>Daily cycles of GHG and aerosol parameters analyzed in this research study, divided by wind regime: (<b>A</b>) carbon monoxide (CO), in ppm (parts per million); (<b>B</b>) carbon dioxide (CO<sub>2</sub>), in ppm; (<b>C</b>) methane (CH<sub>4</sub>), in ppm; (<b>D</b>) equivalent black carbon (eBC), in μg/m<sup>3</sup>; (<b>E</b>) particulate matter (PM) in μg/m<sup>3</sup>, divided into the size ranges PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>4</sub>, and PM<sub>10</sub> but not accounting for wind regime categories; (<b>F</b>) PM<sub>2.5</sub>, with wind regimes; and (<b>G</b>) PM<sub>10</sub>, with wind regimes. Where present, shaded areas refer to intervals within one standard deviation (±σ) from the reported values.</p>
Full article ">Figure 8
<p>Daily cycles of environmental and meteorological data: (<b>A</b>) solar radiation in W/m<sup>2</sup>; (<b>B</b>) temperature (°C); (<b>C</b>) relative humidity (%); and (<b>D</b>) scattering (Mm<sup>−1</sup>). Where present, shaded areas refer to intervals within one standard deviation (±σ) from the reported values.</p>
Full article ">Figure 8 Cont.
<p>Daily cycles of environmental and meteorological data: (<b>A</b>) solar radiation in W/m<sup>2</sup>; (<b>B</b>) temperature (°C); (<b>C</b>) relative humidity (%); and (<b>D</b>) scattering (Mm<sup>−1</sup>). Where present, shaded areas refer to intervals within one standard deviation (±σ) from the reported values.</p>
Full article ">Figure 9
<p>Percentile roses of GHGs and aerosols evaluated in this study. The radius of each rose shows concentrations, while the shaded areas represent the coverage rate by percentile range: (<b>A</b>) carbon monoxide (CO), (<b>B</b>) carbon dioxide (CO<sub>2</sub>), (<b>C</b>) methane (CH<sub>4</sub>), (<b>D</b>) equivalent black carbon (eBC), (<b>E</b>) total particulate matter (PM), (<b>F</b>) PM<sub>2.5</sub>, and (<b>G</b>) PM<sub>10</sub>.</p>
Full article ">Figure 9 Cont.
<p>Percentile roses of GHGs and aerosols evaluated in this study. The radius of each rose shows concentrations, while the shaded areas represent the coverage rate by percentile range: (<b>A</b>) carbon monoxide (CO), (<b>B</b>) carbon dioxide (CO<sub>2</sub>), (<b>C</b>) methane (CH<sub>4</sub>), (<b>D</b>) equivalent black carbon (eBC), (<b>E</b>) total particulate matter (PM), (<b>F</b>) PM<sub>2.5</sub>, and (<b>G</b>) PM<sub>10</sub>.</p>
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<p>Daily (<b>A</b>) and hourly (<b>B</b>) averages of PBLH at LMT. Daily cycle (<b>C</b>) divided by the four wind regime categories described in <a href="#sec2dot2-sustainability-16-10175" class="html-sec">Section 2.2</a>. Where present, shaded areas refer to intervals within one standard deviation (±σ) from the reported values.</p>
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<p>Daily (<b>A</b>) and hourly (<b>B</b>) averages of PBLH at LMT. Daily cycle (<b>C</b>) divided by the four wind regime categories described in <a href="#sec2dot2-sustainability-16-10175" class="html-sec">Section 2.2</a>. Where present, shaded areas refer to intervals within one standard deviation (±σ) from the reported values.</p>
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<p>Temporal variation in ceilometer backscattered profiles, aggregated on a 5 min basis, during select days with synoptic flows from west (1, 2 May) and east (14, 15 May), well-developed breeze (11, 12 August), and not complete breeze (17, 18 July). Yellow contours underline PBL boundaries, while turquoise and green contours indicate cloudy layers.</p>
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<p>Temporal variation in ceilometer backscattered profiles, aggregated on a 5 min basis, during select days with synoptic flows from west (1, 2 May) and east (14, 15 May), well-developed breeze (11, 12 August), and not complete breeze (17, 18 July). Yellow contours underline PBL boundaries, while turquoise and green contours indicate cloudy layers.</p>
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<p>Scatter plots testing the correlation between PBLH and carbon monoxide (CO) under the four observed wind regimes (top: breeze and not complete breeze; bottom: western and eastern synoptic flows).</p>
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<p>Scatter plots testing the correlation between PBLH and carbon dioxide (CO<sub>2</sub>) under the four observed wind regimes (top: breeze and not complete breeze; bottom: western and eastern synoptic flows).</p>
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<p>Scatter plots testing the correlation between PBLH and methane (CH<sub>4</sub>) under the four observed wind regimes (top: breeze and not complete breeze; bottom: western and eastern synoptic flows).</p>
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<p>Scatter plots testing the correlation between PBLH and equivalent black carbon (eBC) under the four observed wind regimes (top: breeze and not complete breeze; bottom: western and eastern synoptic flows).</p>
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<p>Scatter plots testing the correlation between PBLH and PM<sub>2.5</sub> under the four observed wind regimes (top: breeze and not complete breeze; bottom: western and eastern synoptic flows).</p>
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<p>Scatter plots testing the correlation between PBLH and PM<sub>10</sub> under the four observed wind regimes (top: breeze and not complete breeze; bottom: western and eastern synoptic flows).</p>
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13 pages, 5493 KiB  
Article
Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology
by Hanting Zou, Tianmeng Lan, Yongwen Jiang, Xiao-Lan Yu and Haibo Yuan
Foods 2024, 13(23), 3718; https://doi.org/10.3390/foods13233718 - 21 Nov 2024
Abstract
As a crucial stage in the processing of black tea, rolling plays a significant role in both the color transformation and the quality development of the tea. In this process, the production of theaflavins, thearubigins, and theabrownins is a primary factor contributing to [...] Read more.
As a crucial stage in the processing of black tea, rolling plays a significant role in both the color transformation and the quality development of the tea. In this process, the production of theaflavins, thearubigins, and theabrownins is a primary factor contributing to the alteration in color of rolled leaves. Herein, tea pigments are selected as the key quality indicators during rolling of black tea, aiming to establish rapid detection methods for them. A machine vision system is employed to extract nine color feature variables from the images of samples subjected to varying rolling times. Then, the tea pigment content in the corresponding samples is determined using a UV-visible spectrophotometer. In the meantime, the correlation between color variables and tea pigments is discussed. Additionally, Z-score and PCA are used to eliminate the magnitude difference and redundant information in original data. Finally, the quantitative prediction models of tea pigments based on the images’ color features are established by using PLSR, SVR, and ELM. The data show that the Z-score–PCA–ELM model has the best prediction effect for tea pigments. The Rp values for the model prediction sets are all over 0.96, and the RPD values are all greater than 3.50. In this study, rapid determination methods for tea pigments during rolling of black tea are established. These methods offer significant technical support for the digital production of black tea. Full article
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<p>Flow chart of the experiment.</p>
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<p>Image color feature variables: (<b>a</b>) R, (<b>b</b>) G, (<b>c</b>) B, (<b>d</b>) H, (<b>e</b>) S, (<b>f</b>) V, (<b>g</b>) L, (<b>h</b>) a*, (<b>i</b>) b*.</p>
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<p>Image color feature variables: (<b>a</b>) R, (<b>b</b>) G, (<b>c</b>) B, (<b>d</b>) H, (<b>e</b>) S, (<b>f</b>) V, (<b>g</b>) L, (<b>h</b>) a*, (<b>i</b>) b*.</p>
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<p>Correlation analysis diagram of tea pigments and image color feature variables.</p>
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<p>Explanatory variance in principal component analysis.</p>
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<p>(<b>a</b>) Regression prediction scatter plot based on Z-score–ELM; (<b>b</b>) regression prediction scatter plot based on Z-score–PCA–ELM; (<b>c</b>) line chart of prediction results based on Z-score–ELM; (<b>d</b>) relative error chart of prediction results based on Z-score–PCA–ELM.</p>
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<p>(<b>a</b>) Regression prediction scatter plot based on Z-score–ELM; (<b>b</b>) regression prediction scatter plot based on Z-score–PCA–ELM; (<b>c</b>) line chart of prediction results based on Z-score–ELM; (<b>d</b>) relative error chart of prediction results based on Z-score-PCA–ELM.</p>
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<p>(<b>a</b>) Regression prediction scatter plot based on Z-score–ELM; (<b>b</b>) regression prediction scatter plot based on Z-score–PCA–ELM; (<b>c</b>) line chart of prediction results based on Z-score–ELM; (<b>d</b>) relative error chart of prediction results based on Z-score–PCA–ELM.</p>
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17 pages, 5520 KiB  
Review
Development, Prevention, and Detection of Pineapple Translucency: A Review
by Chuanling Li, Mingwei Li, Miaolin Zhang, Linpan Chen, Qingsong Wu, Junjun He, Zhong Xue, Xiumei Zhang and Yanli Yao
Agronomy 2024, 14(12), 2755; https://doi.org/10.3390/agronomy14122755 - 21 Nov 2024
Abstract
Pineapple is one of the most important crops in tropical and subtropical areas. However, its production has been seriously impacted by the issue of fruit translucency in the past several decades. Fruit translucency is a physiological disorder of pineapple flesh with water-soaked core [...] Read more.
Pineapple is one of the most important crops in tropical and subtropical areas. However, its production has been seriously impacted by the issue of fruit translucency in the past several decades. Fruit translucency is a physiological disorder of pineapple flesh with water-soaked core which results in a decline in pineapple quality. It has become a significant challenge for the sustainability of pineapple industry. Currently, the cause and pathophysiological development of pineapple translucency still have not been fully understood. The preventative and remedial measurements on the disease have yet to be effectively implemented in the production process. This review provides comprehensive information and the latest research progress on the possible pathogenesis, initiating factors, preventive and control practices, and detection techniques for pineapple translucency. Furthermore, the progress of research on apple and pear fruit translucency in recent years is reviewed and compared with pineapple translucency. The review offers theoretical guidance and insightful knowledge for the investigation of pineapple translucency disease. Full article
(This article belongs to the Special Issue Green Control of Pests and Pathogens in Tropical Plants)
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<p>Pineapple is a highly valuable crop with a high production, application, and economic value.</p>
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<p>Health benefits of pineapple and bromelain.</p>
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<p>Characteristics of different grades of pineapple translucency.</p>
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<p>Fruit sugar metabolism disorder associated with fruit translucency. (<b>A</b>): schematic diagram of the metabolism of photosynthates in sinks. The sucrose transported by vascular tissue enters the sink tissue through the symplastic and exoplasmic pathways. Sucrose in the exosomal space can enter the cell via sucrose transporters (ST), or it can be broken down into glucose and fructose by the cell-wall invertase (CWI) and then enter the cell via hexose transporters (HT). Suc: sucrose, Fru: fructose, Glu: glucose. (<b>B</b>): proposed causes of fruit translucency. As fruit ripens, the exoplasmic pathway becomes dominant. Due to unknown factors, the CWI activity of the translucent tissue increased sharply. More sucrose was broken down into glucose and fructose and released into the extracytosomal space, resulting in a higher osmotic potential in the water-centered regions. The accumulation of water in the extracytosomal space and the reduction in cellular void ratio leads to a lower refractive index of light and translucent flesh.</p>
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19 pages, 5930 KiB  
Article
Pistachio Phenology and Yield in a Cold-Winter Region of Spain: The Status of the Cultivation and Performance of Three Cultivars
by Lidia Núñez, Hugo Martín, José Manuel Mirás-Avalos and Sara Álvarez
Horticulturae 2024, 10(12), 1235; https://doi.org/10.3390/horticulturae10121235 - 21 Nov 2024
Viewed by 46
Abstract
In recent years, pistachio (Pistacia vera L.) cultivation is undergoing a great expansion in Spain, which is promising for regions where water and winter chilling are not limiting. Many areas of Castilla y León (Spain) provide suitable conditions for pistachio production, but [...] Read more.
In recent years, pistachio (Pistacia vera L.) cultivation is undergoing a great expansion in Spain, which is promising for regions where water and winter chilling are not limiting. Many areas of Castilla y León (Spain) provide suitable conditions for pistachio production, but heat requirement could be a limiting factor. The aims of this study were (i) to investigate the status of pistachios in Castilla y León and the relationships between phenology and agroclimatic conditions and (ii) to assess the performance of three pistachio cultivars (‘Kerman’, ‘Lost Hills’, and ‘Golden Hills’) in a plantation within this region. This work describes the phenological and productive behavior of three pistachio varieties in seven orchards over three years. The chilling requirements were exceeded, and heat accumulation was sufficient to complete the cycle in all seasons. Bloom and harvest occurred later in ‘Kerman’ than in ‘Golden Hills’ and ‘Lost Hills’. In general, ‘Kerman’ had higher nut yield than the other two cultivars but also had more non-split and blank nuts, aspects that should be considered for future plantations. Despite the interannual variability in yield, a trend to increase the production with water received was observed, but this also affected the quality and modified the splitting percentage. Full article
(This article belongs to the Section Fruit Production Systems)
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<p>The location of the pistachio orchards in ‘Castilla y León’ region, Spain. P1–P7 represent the surveyed sites: P1 = Perales, P2 = Toro, P3 = Pozal de Gallinas, P4 = Carpio, P5 = Fombellida, P6 = Madrigal de las Altas Torres, and P7 = Villafuerte.</p>
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<p>The boxplot for the cumulated growing degree days (GDDs, base temperature = 7.2 °C) between April and September in the six weather stations considered (2007–2023 period). Different letters on the boxes indicate significant differences between weather stations according to Duncan’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Occurrence of different phenological stages over the growing season of pistachio ‘Kerman’ and ‘Lost Hills’ cultivars in Carpio during 2019. C = beginning of pollination reception; E = separated clusters; F0 = end of blooming; F1 = beginning of mesocarp yellowing; F2 = yellow mesocarp.</p>
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<p>The principal component analysis (PCA) of ‘Kerman’ pistachio yield: the biplot for the first two components (PC) for bioclimatic variables and yield. P1–P4 represent the surveyed sites: P1 = Perales, P2 = Toro, P3 = Pozal, P4 = Carpio.</p>
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<p>The evolution of the fresh (FW) (<b>a</b>) and dry (DW) weight of fruits per tree (<b>b</b>) in three pistachio cultivars (K = ‘Kerman’, G = ‘Golden Hills’, L = ‘Lost Hills’) in the Carpio orchard during the experimental period (2016–2022). Different letters indicate significant differences among cultivars according to Duncan’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The pistachio sizes (the number of nuts per ounce) in three pistachio cultivars (K = ‘Kerman’, G = ‘Golden Hills‘, L = ‘Lost Hills’) grown in the Carpio orchard during the experimental period (2016–2022). Different letters indicate significant differences among cultivars according to Duncan’s test (<span class="html-italic">p</span> &lt; 0.05), ns = not significant. No nuts were harvested in 2017 due to spring frost damage.</p>
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<p>The evolution of the percentage of split (<b>a</b>), non-split (<b>b</b>), and blank nuts (<b>c</b>) in three pistachio cultivars (K = ‘Kerman’, G = ‘Golden Hills’, L = ‘Lost Hills’) grown in the Carpio orchard during the experimental period (2016–2022). Different letters indicate significant differences among cultivars according to Duncan’s test (<span class="html-italic">p</span> &lt; 0.05), ns = not significant. No nuts were harvested in 2017 due to spring frost damage.</p>
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<p>The accumulated yield (<b>a</b>) and dry weight of split (<b>b</b>), non-split (<b>c</b>), and blank nuts (<b>d</b>) in three pistachio cultivars (K = ‘Kerman’, G = ‘Golden Hills’, L = ‘Lost Hills’) grown in the Carpio orchard during the experimental period (2016–2022). Different letters indicate significant differences among cultivars according to Duncan’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The evolution of fresh weight yield in three pistachio cultivars (K = ‘Kerman’, G = ‘Golden Hills’, L = ‘Lost Hills’) grown in the Carpio orchard and water supply (irrigation + rainfall) (<b>a</b>) during the experimental period (2016–2022) and the relationship between average yield of the three cultivars and water supply (<b>b</b>).</p>
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<p>The relationship between growing degree days using the base temperature of 7.2 °C (GDDs) and yield in three pistachio cultivars (K = ‘Kerman’, G = ‘Golden Hills’, L = ‘Lost Hills’) grown in the Carpio orchard (<b>a</b>) and relationship between GDDs and the average yield of the three cultivars (<b>b</b>) during the experimental period (2016–2022).</p>
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<p>The general aspect of the selected trees from three pistachio cultivars (‘Kerman’, ‘Golden Hills‘ and ‘Lost Hills’) grown in the Carpio orchard during the experimental period. In the beginning of the vegetative period, May 2018 (<b>a</b>–<b>c</b>) and in August 2018 (<b>d</b>–<b>f</b>).</p>
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<p>The detail of the flowers, leaves, and fruits in three pistachio cultivars (‘Kerman’, ‘Golden Hills‘, and ‘Lost Hills’) grown in the Carpio orchard during the experimental period: at the beginning of the vegetative period, May 2018 (<b>a</b>–<b>c</b>), June 2018 (<b>d</b>–<b>f</b>), and in August 2018 (<b>g</b>–<b>i</b>).</p>
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12 pages, 427 KiB  
Article
An Investigation of the Risk Factors Related to Frailty in Older Adults Receiving Home Care Services
by Eirini Stratidaki, Enkeleint A. Mechili, Christina Ouzouni, Athina E. Patelarou, Ioannis Savvakis, Konstantinos Giakoumidakis, Aggelos Laliotis and Evridiki Patelarou
Nutrients 2024, 16(23), 3982; https://doi.org/10.3390/nu16233982 - 21 Nov 2024
Viewed by 44
Abstract
(1) Background: Frailty in older adults is a condition that involves an interaction of psychological, biological, and social factors. This study aimed to assess the frailty status of older adults (65 years old and above) who receive home care services. Additionally, this work [...] Read more.
(1) Background: Frailty in older adults is a condition that involves an interaction of psychological, biological, and social factors. This study aimed to assess the frailty status of older adults (65 years old and above) who receive home care services. Additionally, this work aimed to explore the key factors that have a statistically significant impact on the frailty of this vulnerable population. (2) Methods: This study represents the first phase of an intervention trial involving individuals aged 65 and over who received primary healthcare services and resided in the municipality of Archanes-Asterousia in Crete, Greece. Frailty was assessed using the SHARE-Frailty Instrument, while nutritional status was evaluated with the Mini Nutritional Assessment. Diet-related factors were analyzed, including health factors (oral hygiene, depression, cognitive decline, impaired functioning, quality of life), social factors (educational attainment, marital status, type of work before the age of 60), and lifestyle factors (smoking, alcohol consumption, diet). (3) Results: A total of 730 older adults were evaluated (31.5% male), with an average age (±SD) of 76.83 (±6.68) years. The frailty status analysis revealed 108 (14.8%) to be frail, 249 (34.1%) to be pre-frail, and 373 (51.1%) to be non-frail. Statistically significant associations were found between the MNA and Barthel scores (rs = 0.822, p < 0.001). Higher nutritional evaluations (MNA) were revealed in non-frail adults (mean (±SD); 26.97 ± 1.96) compared to pre-fail (mean (±SD); 19.37 ± 3.36) and frail adults (mean (±SD); 13.08 ± 3.16), as well as in pre-fail compared to frail adults (F = 1338.08, p < 0.001). Functional independence (Barthel) significantly differed with the frailty status of older adults (H = 521.98, p < 0.001; median for non-frail: 20.00, pre-fail: 19.00, frail adults: 15.00). (4) Conclusions: This study demonstrated that good nutritional status, good oral health, functional independence, and good quality of life are strongly correlated with lower frailty. Additionally, having chronic conditions is positively associated with one’s frailty status. Educational programs for both healthcare personnel and older adults are recommended. Full article
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<p>Age according to frailty status.</p>
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