[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

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
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
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

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
remove_circle_outline

Search Results (1,929)

Search Parameters:
Keywords = symbiosis

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
21 pages, 738 KiB  
Article
Unpacking Sarcasm: A Contextual and Transformer-Based Approach for Improved Detection
by Parul Dubey, Pushkar Dubey and Pitshou N. Bokoro
Computers 2025, 14(3), 95; https://doi.org/10.3390/computers14030095 - 6 Mar 2025
Abstract
Sarcasm detection is a crucial task in natural language processing (NLP), particularly in sentiment analysis and opinion mining, where sarcasm can distort sentiment interpretation. Accurately identifying sarcasm remains challenging due to its context-dependent nature and linguistic complexity across informal text sources like social [...] Read more.
Sarcasm detection is a crucial task in natural language processing (NLP), particularly in sentiment analysis and opinion mining, where sarcasm can distort sentiment interpretation. Accurately identifying sarcasm remains challenging due to its context-dependent nature and linguistic complexity across informal text sources like social media and conversational dialogues. This study utilizes three benchmark datasets, namely, News Headlines, Mustard, and Reddit (SARC), which contain diverse sarcastic expressions from headlines, scripted dialogues, and online conversations. The proposed methodology leverages transformer-based models (RoBERTa and DistilBERT), integrating context summarization, metadata extraction, and conversational structure preservation to enhance sarcasm detection. The novelty of this research lies in combining contextual summarization with metadata-enhanced embeddings to improve model interpretability and efficiency. Performance evaluation is based on accuracy, F1 score, and the Jaccard coefficient, ensuring a comprehensive assessment. Experimental results demonstrate that RoBERTa achieves 98.5% accuracy with metadata, while DistilBERT offers a 1.74x speedup, highlighting the trade-off between accuracy and computational efficiency for real-world sarcasm detection applications. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling)
Show Figures

Figure 1

Figure 1
<p>Figure of Abstract.</p>
Full article ">Figure 2
<p>Preprocessing examples from different datasets.</p>
Full article ">Figure 3
<p>Architecture of the transform model.</p>
Full article ">Figure 4
<p>Conversation summarization with BART-Large.</p>
Full article ">Figure 5
<p>Accuracy percentages (%) across different conditions for sarcasm detection.</p>
Full article ">Figure 6
<p>Feature importance analysis for sarcasm detection.</p>
Full article ">Figure 7
<p>Computational efficiency analysis: RoBERTa vs. DistilBERT.</p>
Full article ">Figure 8
<p>A 3D comparison of Jaccard coefficients across models and conditions.</p>
Full article ">Figure 9
<p>Confusion matrices for RoBERTa, DistilBERT, and random forest models.</p>
Full article ">
13 pages, 12068 KiB  
Review
The Effect of Leisure-Time Exercise on Mental Health Among Adults: A Bibliometric Analysis of Randomized Controlled Trials
by Karuppasamy Govindasamy, Masilamani Elayaraja, Abderraouf Ben Abderrahman, Koulla Parpa, Borko Katanic and Urs Granacher
Healthcare 2025, 13(5), 575; https://doi.org/10.3390/healthcare13050575 - 6 Mar 2025
Viewed by 29
Abstract
Background: Adequate levels of leisure-time exercise (LTE) are associated with mental health benefits. Despite increased research in recent years through randomized controlled trials (RCTs), a systematic literature review aggregating these findings is lacking. Here, we examined publication trends, impact, and research gaps regarding [...] Read more.
Background: Adequate levels of leisure-time exercise (LTE) are associated with mental health benefits. Despite increased research in recent years through randomized controlled trials (RCTs), a systematic literature review aggregating these findings is lacking. Here, we examined publication trends, impact, and research gaps regarding LTE’s effects on mental health in the form of a bibliometric analysis. Methods: Five electronic databases (PubMed, EMBASE, Web of Science, Ovid Medline, and the Cumulative Index for Nursing and Allied Health Literature) were searched from their inception until 20 November 2024. Citations were independently screened by two authors and included based on pre-determined eligibility criteria. Bibliometric analysis was conducted using SciVal and VOSviewer under five themes: (1) descriptive analysis, (2) network analysis, (3) thematic mapping, (4) co-citation and co-occurrence analysis, and (5) bibliometric coupling. Results: The systematic search identified 5792 citations, of which 78 RCTs met the inclusion criteria. Only one study was conducted in a low- or middle-income country. Sixty-four percent of studies were published in quartile-one journals. Most studies were, conducted in the United States, followed by Australia, Canada, and the United Kingdom. National collaborations yielded the highest citation rates, reflecting the influence of cultural and social norms on exercise and mental health. Research gaps were identified with regards to the validity of mental health measures, the paucity of data from low- and middle-income countries, and emerging research sources. Conclusions: This bibliometric analysis highlights the existing evidence on LTE’s impact on mental health and identifies areas for future research and policy. Trials exploring valid mental health outcomes, biomarkers such as mood and oxidative stress, and collaborative research are needed, particularly in underrepresented regions of the world. Full article
(This article belongs to the Special Issue Physical Activity for Promoting Mental Health)
Show Figures

Figure 1

Figure 1
<p>PRISMA flowchart guiding the screening and inclusion of studies for bibliometric analysis.</p>
Full article ">Figure 2
<p>Descriptive analysis of the studies included for the systematic review: (<b>a</b>) shows year-wise number of publications; (<b>b</b>) shows citations across time; (<b>c</b>) shows the publications in different quartiles.</p>
Full article ">Figure 3
<p>Global trends in the number of publications (numbers depicted). United States and Australia dominates in exploring the effect of leisure-time physical activity on mental health.</p>
Full article ">Figure 4
<p>Pie chart shows the publication trends in different disciplines.</p>
Full article ">Figure 5
<p>Top 50 key phrases identified in the included studies.</p>
Full article ">Figure 6
<p>Co-occurrences of the keywords and the strength of interconnections.</p>
Full article ">Figure 7
<p>Highly cited authors and the connections [<a href="#B16-healthcare-13-00575" class="html-bibr">16</a>,<a href="#B17-healthcare-13-00575" class="html-bibr">17</a>,<a href="#B18-healthcare-13-00575" class="html-bibr">18</a>,<a href="#B19-healthcare-13-00575" class="html-bibr">19</a>,<a href="#B20-healthcare-13-00575" class="html-bibr">20</a>,<a href="#B21-healthcare-13-00575" class="html-bibr">21</a>].</p>
Full article ">Figure 8
<p>Affiliations of the most influential documents.</p>
Full article ">
23 pages, 55462 KiB  
Review
Lichens and Health—Trends and Perspectives for the Study of Biodiversity in the Antarctic Ecosystem
by Tatiana Prado, Wim Maurits Sylvain Degrave and Gabriela Frois Duarte
J. Fungi 2025, 11(3), 198; https://doi.org/10.3390/jof11030198 - 4 Mar 2025
Viewed by 199
Abstract
Lichens are an important vegetative component of the Antarctic terrestrial ecosystem and present a wide diversity. Recent advances in omics technologies have allowed for the identification of lichen microbiomes and the complex symbiotic relationships that contribute to their survival mechanisms under extreme conditions. [...] Read more.
Lichens are an important vegetative component of the Antarctic terrestrial ecosystem and present a wide diversity. Recent advances in omics technologies have allowed for the identification of lichen microbiomes and the complex symbiotic relationships that contribute to their survival mechanisms under extreme conditions. The preservation of biodiversity and genetic resources is fundamental for the balance of ecosystems and for human and animal health. In order to assess the current knowledge on Antarctic lichens, we carried out a systematic review of the international applied research published between January 2019 and February 2024, using the PRISMA model (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Articles that included the descriptors “lichen” and “Antarctic” were gathered from the web, and a total of 110 and 614 publications were retrieved from PubMed and ScienceDirect, respectively. From those, 109 publications were selected and grouped according to their main research characteristics, namely, (i) biodiversity, ecology and conservation; (ii) biomonitoring and environmental health; (iii) biotechnology and metabolism; (iv) climate change; (v) evolution and taxonomy; (vi) reviews; and (vii) symbiosis. Several topics were related to the discovery of secondary metabolites with potential for treating neurodegenerative, cancer and metabolic diseases, besides compounds with antimicrobial activity. Survival mechanisms under extreme environmental conditions were also addressed in many studies, as well as research that explored the lichen-associated microbiome, its biodiversity, and its use in biomonitoring and climate change, and reviews. The main findings of these studies are discussed, as well as common themes and perspectives. Full article
(This article belongs to the Section Fungal Evolution, Biodiversity and Systematics)
Show Figures

Figure 1

Figure 1
<p>Number of publications in the PubMed database from January 1980 to December 2023 that include the descriptor “lichen” (light blue, with scale on the left); number of publications on lichens related to Antarctic ecosystems in the PubMed database from January 1980 to December 2023, including the descriptors “lichen” and “Antarctic” (dark blue, with scale on the right).</p>
Full article ">Figure 2
<p>Flowchart of selection steps for studies related to lichen research in Antarctic ecosystem (January 2019 to February 2024).</p>
Full article ">Figure 3
<p>Number of studies included by thematic area (January 2019–February 2024).</p>
Full article ">Figure 4
<p>Heatmap showing the number of studies according to the country of the first author involved in lichen research in the Antarctic ecosystem by thematic area (January 2019–February 2024). Data spanned from white (low number of articles) to dark blue (higher number of articles), as illustrated by the color scale in the bar.</p>
Full article ">
15 pages, 1751 KiB  
Review
Maximizing Photosynthesis and Plant Growth in African Legumes Through Rhizobial Partnerships: The Road Behind and Ahead
by Sanjay K. Jaiswal and Felix D. Dakora
Microorganisms 2025, 13(3), 581; https://doi.org/10.3390/microorganisms13030581 - 4 Mar 2025
Viewed by 167
Abstract
The interplay between soil rhizobial bacteria and leguminous plants, particularly in Africa, has a profound impact on photosynthetic efficiency and overall crop productivity. This review explores the critical role of rhizobia in enhancing photosynthesis through nitrogen fixation, a process crucial for sustainable agriculture. [...] Read more.
The interplay between soil rhizobial bacteria and leguminous plants, particularly in Africa, has a profound impact on photosynthetic efficiency and overall crop productivity. This review explores the critical role of rhizobia in enhancing photosynthesis through nitrogen fixation, a process crucial for sustainable agriculture. Rhizobial bacteria residing in root nodules provide legumes with symbiotic nitrogen that significantly boosts plant growth and photosynthetic capacity. Recent advances in molecular genomics have elucidated the genetic frameworks underlying this symbiosis, identifying key genes involved in root nodule formation and nitrogen fixation. Comparative genomics of Bradyrhizobium species have revealed seven distinct lineages, with diverse traits linked to nodulation, nitrogen fixation, and photosynthesis. Field studies across Africa demonstrate that rhizobial inoculation can markedly increase nodulation, nitrogen fixation, and grain yields, though outcomes vary depending on local soil conditions and legume species. Notable findings include enhanced nutrient uptake and photosynthetic rates in inoculated legumes compared with nitrate-fed plants. This review highlights the potential of utilizing indigenous rhizobia to improve photosynthesis and crop resilience. Future prospects involve leveraging genomic insights to optimize rhizobial inoculants and enhance legume productivity in water-limited environments. As climate change intensifies, integrating these advancements into agricultural practices could play a crucial role in improving food security and sustainable soil health in Africa. Full article
Show Figures

Figure 1

Figure 1
<p>This diagram provides a schematic representation of how leguminous plants engage in a mutualistic relationship with <span class="html-italic">Rhizobium</span> to fix atmospheric nitrogen. The energy derived from photosynthesis drives the synthesis of photosynthates, which fuel root and nodule development and support the biological nitrogen fixation (BNF) process. Through this intricate biological system, the plant obtains essential nitrogen in a usable form ammonia, thereby enhancing its growth and productivity without reliance on external nitrogen fertilizers.</p>
Full article ">Figure 2
<p>This line graph presents a comparative analysis of physiological parameters across different crops and countries under two treatments: Inoculated and Uninoculated. The parameters considered in this analysis are Photosynthesis Rate (A) (μmol CO<sub>2</sub> m<sup>−2</sup> s<sup>−1</sup>), Stomatal Conductance (gs) (mol H<sub>2</sub>O m<sup>−2</sup> s<sup>−1</sup>), and Transpiration Rate (E) (mmol H<sub>2</sub>O m<sup>−2</sup> s<sup>−1</sup>). The trends in this graph shows that the inoculated treatment often shows higher values for the physiological parameters compared to the uninoculated treatment, indicating the possible benefits of inoculation on crop performance. Uninoculated Treatment shows lower values, suggesting a reduced physiological response in the absence of inoculation. Graph was constructed based on published data from Ibny et al. (2019) [<a href="#B13-microorganisms-13-00581" class="html-bibr">13</a>]; Gyogluu et al. (2018) [<a href="#B46-microorganisms-13-00581" class="html-bibr">46</a>]; Dlamini et al. (2021) [<a href="#B47-microorganisms-13-00581" class="html-bibr">47</a>]; Mbah et al. (2022) [<a href="#B5-microorganisms-13-00581" class="html-bibr">5</a>]; Simbine et al. (2021) [<a href="#B48-microorganisms-13-00581" class="html-bibr">48</a>]; Ngwenya et al. (2022) [<a href="#B8-microorganisms-13-00581" class="html-bibr">8</a>]; Gunununu et al. (2023) [<a href="#B49-microorganisms-13-00581" class="html-bibr">49</a>].</p>
Full article ">
22 pages, 2702 KiB  
Review
The Importance of the Glomus Genus as a Potential Candidate for Sustainable Agriculture Under Arid Environments: A Review
by Redouane Ouhaddou, Mohamed Anli, Raja Ben-Laouane, Abderrahim Boutasknit, Marouane Baslam and Abdelilah Meddich
Int. J. Plant Biol. 2025, 16(1), 32; https://doi.org/10.3390/ijpb16010032 - 3 Mar 2025
Viewed by 287
Abstract
Drought and salinity are major factors that hinder crop cultivation and significantly impair agricultural productivity, particularly in (semi)arid regions. These two abiotic constraints cause deterioration in soil structure and reduced fertility and hamper plant growth by limiting access to mineral elements and water, [...] Read more.
Drought and salinity are major factors that hinder crop cultivation and significantly impair agricultural productivity, particularly in (semi)arid regions. These two abiotic constraints cause deterioration in soil structure and reduced fertility and hamper plant growth by limiting access to mineral elements and water, thereby threatening global food security. What’s more, the excessive, long-term use of chemical fertilizers to boost crop productivity can disrupt the balance of agricultural ecosystems, particularly soil health. Faced with these challenges, the sustainable exploitation of natural resources, in particular rhizospheric microorganisms, is an environmentally friendly solution. Arbuscular mycorrhizal fungi play an important role as biofertilizers due to their symbiotic relationship with the roots of nearly 80% of plants. They promote not only the growth of host plants but also their resistance to abiotic stresses. Among these fungi, the Glomus genus stands out for its predominance in plants’ rhizosphere thanks to its richness in high-performance species and ecological adaptability. This review highlights the importance of species within this genus in soils, particularly in terrestrial ecosystems subject to (semi-)arid climates. Molecular mechanisms underlying plant tolerance to drought and salt stress in symbiosis with species of the Glomus genus are also explored. Full article
(This article belongs to the Section Plant–Microorganisms Interactions)
Show Figures

Figure 1

Figure 1
<p>Spores of (<b>1</b>) <span class="html-italic">Glomus heterosporum</span>, (<b>2</b>) <span class="html-italic">G. microcarpum</span>, (<b>3</b>) <span class="html-italic">G.</span> sp., (<b>4</b>) <span class="html-italic">G. rubiforme</span>, (<b>5</b>) <span class="html-italic">G. multicaule</span>, (<b>6</b>) <span class="html-italic">G. globiferum</span>, and (<b>7</b>) <span class="html-italic">G. microcarpum</span> [<a href="#B47-ijpb-16-00032" class="html-bibr">47</a>].</p>
Full article ">Figure 2
<p>Stages in establishing symbiosis between <span class="html-italic">Glomus</span> sp. and plant root. SLs: strigolactone; AIA: indole-3-acetic acid; ABA: abscisic acid; GA: gebirilic acid; MycF: myc factors; RAM1: Required for arbuscular mycorrhiza1.</p>
Full article ">Figure 3
<p>Effects of drought and salinity on plant growth, physiology, biochemistry, and soil properties. ROS: reactive oxygen species; RWC: relative water content; ABA: abscisic acid, downward-curving arrow: assimilation, downward red arrow: low CO<sub>2</sub> assimilation.</p>
Full article ">Figure 4
<p>Impact of <span class="html-italic">Glomus</span> sp. on plant traits under drought and salt stress. AMF: arbuscular mycorrhizal fungi; PGPR: plant growth-promoting rhizobacteria; MHB: mycorrhiza helper bacteria; RWC: relative water content; WC: water content; MDA: malondialdehyde; H<sub>2</sub>O<sub>2</sub>: hydrogen peroxide; POX: peroxidase; PPO: polyphenoloxidase; CAT: catalase: SOD: superoxide dismutase; EPS: exopolysaccharides; downward-curving arrow: assimilation.</p>
Full article ">
28 pages, 5358 KiB  
Article
Mycorrhizal Symbiosis and Water Deficit: Morphophysiological and Gene Expression Responses in Caatinga Passion Fruit
by Luiz Victor de Almeida Dantas, Roberta Lane de Oliveira Silva, Welson Lima Simões, Adriana Mayumi Yano-Melo and Natoniel Franklin de Melo
Stresses 2025, 5(1), 18; https://doi.org/10.3390/stresses5010018 - 1 Mar 2025
Viewed by 160
Abstract
The advancement of global warming and climate change requires strategic actions in understanding and seeking interactions between plant species and microorganisms that are more tolerant to water deficit. This research assessed the morpho-agronomic, physiological, and gene expression responses of two Passiflora cincinnata accessions [...] Read more.
The advancement of global warming and climate change requires strategic actions in understanding and seeking interactions between plant species and microorganisms that are more tolerant to water deficit. This research assessed the morpho-agronomic, physiological, and gene expression responses of two Passiflora cincinnata accessions (tolerant and sensitive) to water deficit, focusing on their relationship with mycorrhization. A randomized design with two accessions, two field capacities, and four AMF inoculation treatments was used to compare drought and control conditions. Differential gene expression was analyzed under drought stress, and the effect of mycorrhization on stress tolerance was evaluated. The results showed that inoculation with native arbuscular mycorrhizal fungi (AMF) communities, especially those from water-deficit conditions (AMF25), resulted in greater increases in height, number of leaves, stem diameter, number of tendrils, leaf area, and fresh biomass of root and shoot, with increases ranging from 50% to 300% compared to the control (non-inoculated) and monospecific inoculation (Entrophospora etunicata). Higher photosynthetic rate and water use efficiency were observed in the tolerant accession. Mycorrhizal inoculation increased the total chlorophyll content in both accessions, especially when inoculated with native AMF communities. Overall, P. cincinnata showed higher mycorrhizal responsiveness when inoculated with native AMF communities compared to monospecific inoculation with E. etunicata. The tolerant accession showed overexpression of the genes PcbZIP, PcSIP, and PcSTK, which are associated with signal transduction, water deficit tolerance, osmoregulation, and water transport. In contrast, the water deficit-sensitive accession showed repression of the PcSIP and PcSTK genes, indicating their potential use for distinguishing tolerant and sensitive accessions of the species. The tolerance of P. cincinnata to water deficit is directly related to physiological responses, increased photosynthetic rate, efficient water use, and regulation of gene expression. Full article
(This article belongs to the Collection Feature Papers in Plant and Photoautotrophic Stresses)
Show Figures

Figure 1

Figure 1
<p>Growth of passion fruit plants cultivated under two irrigation regimes (up to 25% of field capacity and 75–100% of field capacity) in greenhouse. (<b>A</b>) Height, (<b>B</b>) stem diameter, and (<b>C</b>) number of tendrils in tolerant (A01—blue color) and sensitive (A48—red color) passion fruit accessions, either uninoculated (Control) or inoculated with <span class="html-italic">E. etunicata</span> (EE), native AMF communities from soil under water deficit (AMF25) or without water deficit (AMF75). In each boxplot, data points represent <span class="html-italic">n</span> = 10 samples from the two-way ANOVA interaction between accession and inoculation. Different letters denote significant differences between treatments, as determined by Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). Lowercase letters compare accessions within each treatment, and uppercase letters compare inoculation treatments within each accession.</p>
Full article ">Figure 2
<p>Comparison of morpho-agronomic variables in passion fruit accessions using a one-way ANOVA. (<b>A</b>–<b>D</b>) Growth of plants uninoculated (Control) or inoculated with <span class="html-italic">E. etunicata</span> (EE) or with native AMF communities from soil under water deficit (AMF25) or without water deficit (AMF75). (<b>E</b>–<b>G</b>) Differences between tolerant (A01) and sensitive (A48) passion fruit accessions. In each boxplot, data points represent <span class="html-italic">n</span> = 20 samples for inoculation and <span class="html-italic">n</span> = 40 for accession, based on one-way ANOVA. Different letters denote significant differences between treatments, as determined by Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>Graph showing total chlorophyll content in two passion fruit accessions: tolerant (A01) and sensitive (A48). Plants were inoculated with three different types of arbuscular mycorrhizal fungi (AMF): <span class="html-italic">E. etunicata</span> (EE), AMF communities from soil subjected to water deficit (AMF25), and AMF communities from soil without water deficit (AMF75). Irrigation conditions were applied to maintain either 25% field capacity (low) or 75–100% field capacity (high). Each boxplot represents data from <span class="html-italic">n</span> = 5 samples and illustrates the three-way interaction between accession, field capacity, and inoculation, as determined by ANOVA analysis. Different letters denote significant differences between treatments, as determined by Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). Lowercase letters compare field capacity within each access individually; uppercase letters compare the interaction between field capacity and inoculation within each access; italicized lowercase letters compare the interaction between field capacity and inoculation between contrasting accesses.</p>
Full article ">Figure 4
<p>Physiological status of passion fruit plants inoculated or non-inoculated with arbuscular mycorrhizal fungi (AMF), cultivated under two irrigation conditions: 25% field capacity (low) and 75–100% field capacity (high). (<b>A</b>,<b>C</b>,<b>E</b>) Water use efficiency, intrinsic water use efficiency, and photosynthetic rate in plants not inoculated (Control) or inoculated with <span class="html-italic">E. etunicata</span> (EE) or AMF communities from soil under water deficit (AMF25) or without water deficit (AMF75). (<b>B</b>,<b>D</b>,<b>F</b>) The same physiological parameters in tolerant (A01) and sensitive (A48) passion fruit accessions. In each boxplot, data points represent <span class="html-italic">n</span> = 20 samples for inoculation and <span class="html-italic">n</span> = 40 samples for accession, based on one-way ANOVA. Different letters indicate significant differences between treatments according to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 5
<p>PCA of morpho-agronomic and physiological descriptors across different inoculation treatments: Control (non-inoculated), <span class="html-italic">E. etunicata</span> inoculation (EE), AMF communities from soil under water deficit conditions (AMF25), and AMF communities from soil under irrigated conditions (AMF75). Photosynthesis rate (Pr), Stomatal conductance (gs), Transpiration rate (E), Leaf temperature (Lt), Height (H), Number of leaves (NL), Number of tendrils (NT), Stem diameter (SD), Chlorophyll a (ChloA), Chlorophyll b (ChloB), Total Chlorophyll (ChloAB), Shoot Fresh Weight (SFW), Root Fresh Weight (RFW), Leaf area (LA), Water-use efficiency (WUE), and Intrinsic water-use efficiency (iWUE).</p>
Full article ">Figure 6
<p>Mycorrhizal colonization of <span class="html-italic">P. cincinnata</span> accessions inoculated with AMF communities from the C25 water deficit condition under contrasting water availability conditions. Mycorrhizal frequency (<b>A</b>), mycorrhizal colonization intensity (<b>B</b>), intensity of arbuscules per root fragment (<b>C</b>), and intensity of arbuscules in the root system (<b>D</b>) are expressed as percentages. Lowercase letters indicate significant differences within accessions, while uppercase letters denote differences between accessions. In Figures (<b>C</b>,<b>D</b>), orange represents &lt; 25% field capacity, and ciano represents &gt;75% field capacity.</p>
Full article ">Figure 7
<p>PCA of morpho-agronomic and physiological descriptors among different <span class="html-italic">P. cincinnata</span> accessions: A01 (tolerant) and A48 (sensitive) to water deficit (<b>A</b>), and under two irrigation conditions (0–25% field capacity and 75–100% field capacity) (<b>B</b>), using samples from the treatment with native AMF communities from soil under water deficit (AMF25). This analysis includes treatments evaluated in the differential gene expression study.</p>
Full article ">Figure 8
<p>Experimental design diagram featuring two <span class="html-italic">P. cincinnata</span> accessions (A01 and A48), two irrigation conditions (&lt;25% and &gt;75% field capacity), and four AMF inoculation treatments: Control (no inoculation), EE (inoculation with <span class="html-italic">Entrophospora etunicata</span>), AMF25 (native AMF communities from drought-stressed plants), and AMF75 (native AMF communities from irrigated plants). The box outlined with dashed blue lines represents the morphophysiological analyses, while the box outlined with dashed red lines indicates the treatments used for gene expression analysis.</p>
Full article ">
22 pages, 6335 KiB  
Article
Redesigning Sustainable Rural Tourism: A Stakeholder-Centered Approach to Interest Symbiosis in Post-Planning Villages
by Pingping Fang, Yonghong Liu, Xiangtian Bai and Zhengbei Niu
Sustainability 2025, 17(5), 2064; https://doi.org/10.3390/su17052064 - 27 Feb 2025
Viewed by 193
Abstract
Rural tourism has become a crucial engine of economic growth in traditional villages, with numerous regions completing planning and development stages. However, along with the growth of tourism, challenges such as cultural conflicts, resource competition, and conflicting interests have emerged, threatening the long-term [...] Read more.
Rural tourism has become a crucial engine of economic growth in traditional villages, with numerous regions completing planning and development stages. However, along with the growth of tourism, challenges such as cultural conflicts, resource competition, and conflicting interests have emerged, threatening the long-term sustainability of tourism in these villages. Based on the unique characteristics of traditional villages, this study proposes a stakeholder-centered system design approach to address and improve these issues. This approach focuses on governing existing traditional village systems by constructing a stakeholder interest map through an analysis of the behavioral traits and interest demands of key stakeholders and identifying the main factors that hinder the flow of benefits. Furthermore, a large-scale symbiotic model is developed to explore the optimal path for the rebalancing of interests within traditional village systems. In terms of practical research, the study takes Gangtou Village in Guangzhou, Guangdong Province, China, as a case study. Through interviews, expert consultations, and tracking experiments, the research comprehensively analyzes the interests and flows of stakeholders within the system. A symbiotic interest model is collaboratively established, and based on this model, a redesigned planning scheme for Gangtou Village is proposed. In the design validation phase, expert ratings and the Wilcoxon non-parametric test were employed to compare the sustainability of the new and old plans. The results indicate that the new plan outperforms the old one, thereby validating the feasibility of the proposed holistic system design approach. Full article
(This article belongs to the Section Sustainable Urban and Rural Development)
Show Figures

Figure 1

Figure 1
<p>Explicit and Invisible Restrictive Factors on Tourism Development in Traditional Villages.</p>
Full article ">Figure 2
<p>Schematic Representation of the Traditional Village Tourism Development System.</p>
Full article ">Figure 3
<p>Map of the Regional Structure of Gangtou Village (The area enclosed by the thick red line represents the geographical location of Gangtou Village, as well as the scope of the tourism redesign for the village. The area enclosed by the thin red line highlights several key attractions within Gangtou Village that have already been planned).</p>
Full article ">Figure 4
<p>Architectural Appearance of Traditional Lingnan Buildings in Gangtou Village.</p>
Full article ">Figure 5
<p>Stakeholder-Centered System Design Approach.</p>
Full article ">Figure 6
<p>Changes in Old and New Stakeholders in Tourism Services. (<b>a</b>) Traditional Stakeholder Relationship (<b>b</b>) Modern Stakeholder Relationship.</p>
Full article ">Figure 7
<p>The Field Research Process in Gangtou Village.</p>
Full article ">Figure 8
<p>Mapping the Flow of Interest Among Stakeholders (The different colored lines in the image represent the flow of material, experiential, ecological, cultural, and informational interests among the four stakeholders: enterprises, management, tourists, and residents).</p>
Full article ">Figure 9
<p>Rebalancing Path of Interests.</p>
Full article ">Figure 10
<p>Symbiotic System of Interest Model (The arrows in the image represent the flow and sequence of the system, starting from the input (Step 1), followed by reflection/forecasts (Step 2), and ending with the output (Step 3). The arrows also illustrate the progressive relationships between the various components).</p>
Full article ">Figure 11
<p>Distribution of Tourist Attractions in Gangtou Village.</p>
Full article ">Figure 12
<p>Design Scheme for Tourism Services in Gangtou Village.</p>
Full article ">
7 pages, 407 KiB  
Data Descriptor
Draft Genome Sequence Data of the Ensifer sp. P24N7, a Symbiotic Bacteria Isolated from Nodules of Phaseolus vulgaris Grown in Mining Tailings from Huautla, Morelos, Mexico
by José Augusto Ramírez-Trujillo, Maria Guadalupe Castillo-Texta, Mario Ramírez-Yáñez and Ramón Suárez-Rodríguez
Data 2025, 10(3), 34; https://doi.org/10.3390/data10030034 - 27 Feb 2025
Viewed by 207
Abstract
In this work, we report the draft genome sequence of Ensifer sp. P24N7, a symbiotic nitrogen-fixing bacterium isolated from nodules of Phaseolus vulgaris var. Negro Jamapa was planted in pots that contained mining tailings from Huautla, Morelos, México. The genomic DNA was sequenced [...] Read more.
In this work, we report the draft genome sequence of Ensifer sp. P24N7, a symbiotic nitrogen-fixing bacterium isolated from nodules of Phaseolus vulgaris var. Negro Jamapa was planted in pots that contained mining tailings from Huautla, Morelos, México. The genomic DNA was sequenced by an Illumina NovaSeq 6000 using the 250 bp paired-end protocol obtaining 1,188,899 reads. An assembly generated with SPAdes v. 3.15.4 resulted in a genome length of 7,165,722 bp composed of 181 contigs with a N50 of 323,467 bp, a coverage of 76X, and a GC content of 61.96%. The genome was annotated with the NCBI Prokaryotic Genome Annotation Pipeline and contains 6631 protein-coding sequences, 3 complete rRNAs, 52 tRNAs, and 4 non-coding RNAs. The Ensifer sp. P24N7 genome has 59 genes related to heavy metal tolerance predicted by RAST server. These data may be useful to the scientific community because they can be used as a reference for other works related to heavy metals, including works in Huautla, Morelos. Full article
(This article belongs to the Special Issue Benchmarking Datasets in Bioinformatics, 2nd Edition)
Show Figures

Figure 1

Figure 1
<p>Tree inferred with FastME 2.1.6.1 from GBDP distances calculated from genome sequences. The branch lengths are scaled in terms of GBDP distance formula <span class="html-italic">d</span><sub>5</sub>. The numbers above branches are GBDP pseudo-bootstrap support values &gt;60% from 100 replications, with an average branch support of 61.2%. The tree shows the placement of the <span class="html-italic">Ensifer</span> sp. P24N7 with <span class="html-italic">E. meliloti</span> NBRC 14782 and <span class="html-italic">Ensifer kummerowiae</span> CCBAU 71714. Red asterisk indicates isolate <span class="html-italic">Ensifer</span> sp. P24N7, object of the present study.</p>
Full article ">
15 pages, 3171 KiB  
Article
Genome-Wide Identification, Expression, and Protein Interaction of GRAS Family Genes During Arbuscular Mycorrhizal Symbiosis in Poncirus trifoliata
by Fang Song, Chuanya Ji, Tingting Wang, Zelu Zhang, Yaoyuan Duan, Miao Yu, Xin Song, Yingchun Jiang, Ligang He, Zhijing Wang, Xiaofang Ma, Yu Zhang, Zhiyong Pan and Liming Wu
Int. J. Mol. Sci. 2025, 26(5), 2082; https://doi.org/10.3390/ijms26052082 - 27 Feb 2025
Viewed by 213
Abstract
Arbuscular mycorrhizal (AM) fungi establish mutualistic symbiosis with most land plants, facilitating mineral nutrient uptake in exchange for photosynthates. As one of the most commercially used rootstocks in citrus, Poncirus trifoliata heavily depends on AM fungi for nutrient absorption. The GRAS gene family [...] Read more.
Arbuscular mycorrhizal (AM) fungi establish mutualistic symbiosis with most land plants, facilitating mineral nutrient uptake in exchange for photosynthates. As one of the most commercially used rootstocks in citrus, Poncirus trifoliata heavily depends on AM fungi for nutrient absorption. The GRAS gene family plays essential roles in plant growth and development, signaling transduction, and responses to biotic and abiotic stresses. However, the identification and functional characterization of GRAS family genes in P. trifoliata remains largely unexplored. In this study, a comprehensive genome-wide analysis of PtGRAS family genes was conducted, including their identification, physicochemical properties, phylogenetic relationships, gene structures, conserved domains, chromosome localization, and collinear relationships. Additionally, the expression profiles and protein interaction of these genes under AM symbiosis were systematically investigated. As a result, 41 GRAS genes were identified in the P. trifoliata genome, and classified into nine distinct clades. Collinearity analysis revealed seven segmental duplications but no tandem duplications, suggesting that segmental duplication played a more important role in the expansion of the PtGRAS gene family compared to tandem duplication. Additionally, 18 PtGRAS genes were differentially expressed in response to AM symbiosis, including orthologs of RAD1, RAM1, and DELLA3 in P. trifoliata. Yeast two-hybrid (Y2H) screening further revealed that PtGRAS6 and PtGRAS20 interacted with both PtGRAS12 and PtGRAS18, respectively. The interactions were subsequently validated through bimolecular fluorescence complementation (BiFC) assays. These findings underscored the crucial role of GRAS genes in AM symbiosis in P. trifoliata, and provided valuable candidate genes for improving nutrient uptake and stress resistance in citrus rootstocks through molecular breeding approaches. Full article
(This article belongs to the Special Issue Molecular Research of Tropical Fruit (2nd Edition))
Show Figures

Figure 1

Figure 1
<p>Chromosome localization of <span class="html-italic">PtGRAS</span> family genes. The text on the left represents the number of chromosomes, and the scale on the left represents the chromosome size.</p>
Full article ">Figure 2
<p>Phylogenetic relationships of GRAS proteins between <span class="html-italic">Poncirus trifoliata</span> (Pt), <span class="html-italic">Arabidopsis thaliana</span> (At), and <span class="html-italic">Medicago trunctula</span> (Mt). Red squares represent PtGRAS proteins, blue stars represent AtGRAS proteins, and yellow circles represent MtGRAS proteins. The phylogenetic tree was constructed by MEGA X using the Maximum Likelihood Method (1000 bootstrap). The different colors of backgrounds indicated eight clades of <span class="html-italic">GRAS</span> family genes.</p>
Full article ">Figure 3
<p>Conserved motifs and gene structures of <span class="html-italic">PtGRAS</span> family genes. (<b>A</b>) Conserved motifs of <span class="html-italic">PtGRAS</span> family genes using MEME algorithm. The different colors indicated 12 identified motifs. (<b>B</b>) The gene structures are based on the sequences of <span class="html-italic">PtGRAS</span> family genes. The yellow color and green color indicated CDS and UTR, and the lines indicated Intron.</p>
Full article ">Figure 4
<p>Collinearity analysis of <span class="html-italic">PtGRAS</span> genes. (<b>A</b>) Gray lines indicated all duplicated genes, dark lines indicated segmentally duplicated genes, and the heatmap and line graph were gene densities. (<b>B</b>) Collinearity analysis of <span class="html-italic">PtGRAS</span> genes with <span class="html-italic">A. thaliana</span> and <span class="html-italic">M. truncatula</span>. The gray lines represented all collinear pairs of <span class="html-italic">P. trifoliata</span> with <span class="html-italic">A. thaliana</span> and <span class="html-italic">M. truncatula</span> at the genome level. The black lines represented collinearity gene pairs.</p>
Full article ">Figure 5
<p>The expression profiles of <span class="html-italic">PtGRAS</span> genes in response to arbuscular mycorrhizal symbiosis. (<b>A</b>). Heatmap analysis of RNA-seq data. (<b>B</b>). Relative expression of qRT-PCR analysis. AM, arbuscular mycorrhizal inoculated <span class="html-italic">P. trifoliata</span> roots; NM, non-mycorrhizal control roots. The asterisks indicated significant differences of student’s <span class="html-italic">t</span>-test (** <span class="html-italic">p</span> &lt; 0.01).</p>
Full article ">Figure 6
<p>Identification of the interaction of AM symbiosis-related PtGRAS proteins. (<b>A</b>) Y2H analyses screening the interaction among PtGRAS proteins using SD/–LW and SD/–LWHA selective medium. AD-RecT + BD-53 and AD + BD were utilized as positive control and negative control, respectively. The experimental controls (positive and negative controls) are shown in <a href="#app1-ijms-26-02082" class="html-app">Figure S3</a>. (<b>B</b>) BiFC assay validation of the interaction between PtGRAS6, PtGRAS12, and PtGRAS18 proteins in <span class="html-italic">N. benthamiana</span> leaves. Scale bars, 30 µm. The overlapping of GFP and mCherry fluorescence is marked with red arrow. FIB2:mCherry was utilized as a nucleus marker, YFPn + YFPc was provided as the negative control.</p>
Full article ">
27 pages, 4855 KiB  
Article
Metagenomic Characterization of the Maerua crassifolia Soil Rhizosphere: Uncovering Microbial Networks for Nutrient Acquisition and Plant Resilience in Arid Ecosystems
by Sumayah M. Alharbi, Nadiah Al-Sulami, Hadba Al-Amrah, Yasir Anwar, Ola A. Gadah, Lina Ahmed Bahamdain, Mohammed Al-Matary, Amnah M. Alamri and Ahmed Bahieldin
Genes 2025, 16(3), 285; https://doi.org/10.3390/genes16030285 - 26 Feb 2025
Viewed by 257
Abstract
Background/Objectives:Maerua crassifolia, a threatened medicinal species endemic to drylands, exhibits a pronounced drought sensitivity. Despite the critical role of microorganisms, particularly bacteria and fungi, the microbial consortia in M. crassifolia’s rhizosphere remain underexplored. Methods: Metagenomic whole genome shotgun sequencing (WGS) was [...] Read more.
Background/Objectives:Maerua crassifolia, a threatened medicinal species endemic to drylands, exhibits a pronounced drought sensitivity. Despite the critical role of microorganisms, particularly bacteria and fungi, the microbial consortia in M. crassifolia’s rhizosphere remain underexplored. Methods: Metagenomic whole genome shotgun sequencing (WGS) was employed to elucidate the taxonomic composition of bacterial and fungal communities inhabiting the soil rhizosphere of M. crassifolia. Results: The data revealed a marked predominance of bacterial genomes relative to fungal communities, as evidenced by non-redundant gene analysis. Notably, arbuscular mycorrhizal fungi (AMF), specifically Rhizophagus clarus, Rhizophagus irregularis and Funneliformis geosporum, are key rhizosphere colonizers. This study confirmed the presence of phosphate-solubilizing bacteria (PSB), such as Sphingomonas spp., Cyanobacteria and Pseudomonadota, underscoring the critical role of these microorganisms in the phosphorus cycle. Additionally, the study uncovered the presence of previously uncharacterized species within the phylum Actinobacteria, as well as unidentified taxa from the Betaproteobacteria, Gemmatimonadota and Chloroflexota phyla, which may represent novel microbial taxa with potential plant growth-promoting properties. Conclusions: Findings suggest a complex, symbiotic network where AMF facilitate phosphorus uptake through plant–root interactions. In a tripartite symbiosis, PSB enhance inorganic phosphorus solubilization, increasing bioavailability, which AMF assimilate and deliver to plant roots, optimizing nutrition. This bacterial–fungal interplay is essential for plant resilience in arid environments. Future investigations should prioritize the isolation and characterization of underexplored microbial taxa residing in the rhizosphere of M. crassifolia, with particular emphasis on members of the Actinobacteria, Betaproteobacteria, Gemmatimonadota and Chloroflexota phyla to uncover their roles in nutrient acquisition and sustainability. Full article
(This article belongs to the Section Genes & Environments)
Show Figures

Figure 1

Figure 1
<p>The figure depicts the location where the samples were collected.</p>
Full article ">Figure 2
<p>ANOSIM results of soil metagenomes associated with <span class="html-italic">M. crassifolia</span> plants. ANOSIM was used to compare microbial community structure between groups A and B at the (<b>A</b>) phylum, (<b>B</b>) genus and (<b>C</b>) species levels.</p>
Full article ">Figure 3
<p>PCA results of soil metagenomes associated with <span class="html-italic">M. crassifolia</span> plants. PCA was performed based on gene abundances at the (<b>A</b>) phylum, (<b>B</b>) genus and (<b>C</b>) species levels. The <span class="html-italic">x</span>-axis and <span class="html-italic">y</span>-axis represent the first and second principal components (PC1 and PC2), respectively. The percentage of variation explained by each principal component is indicated in parentheses.</p>
Full article ">Figure 4
<p>Microbial abundance at the domain level, representing bacteria, Eukaryota and others. The abundance values are based on nRG identified in metagenomic data of <span class="html-italic">M. crassifolia</span> plant across soil types. “Others” include archaea, viruses and unidentified taxa.</p>
Full article ">Figure 5
<p>The microbial abundance of the top ten bacterial (purple columns) and eukaryotic (pink columns) phyla, based on nRG identified in the metagenomic data of the <span class="html-italic">M. crassifolia</span> plant across different soil types, including the NOVO_MIX samples.</p>
Full article ">Figure 6
<p>The microbial abundance of the top ten bacterial (purple columns) and eukaryotic (pink columns) genera, based on nRG identified in the metagenomic data of the <span class="html-italic">M. crassifolia</span> plant across different soil types, including the NOVO_MIX samples.</p>
Full article ">Figure 7
<p>The microbial abundance of the top ten bacterial (purple columns) and eukaryotic (pink columns) species, based on nRG identified in the metagenomic data of the <span class="html-italic">M. crassifolia</span> plant across different soil types, including the NOVO_MIX samples.</p>
Full article ">Figure 8
<p>The relative microbial abundance of the top ten bacterial phyla, based on nRG identified in the metagenomic data of the <span class="html-italic">M. crassifolia</span> plant across different soil types (e.g., rhizosphere and bulk soil).</p>
Full article ">Figure 9
<p>The relative microbial abundance of the top ten bacterial genera, based on nRG identified in the metagenomic data of the <span class="html-italic">M. crassifolia</span> plant across different soil types (e.g., rhizosphere and bulk soil).</p>
Full article ">Figure 10
<p>The relative microbial abundance of the top ten bacterial species, based on nRG identified in the metagenomic data of the <span class="html-italic">M. crassifolia</span> plant across different soil types (e.g., rhizosphere and bulk soil).</p>
Full article ">Figure 11
<p>The relative microbial abundance of the top ten eukaryotic phyla, based on nRG identified in the metagenomic data of the <span class="html-italic">M. crassifolia</span> plant across different soil types (e.g., rhizosphere and bulk soil).</p>
Full article ">Figure 12
<p>The relative microbial abundance of the top ten eukaryotic genera, based on nRG identified in the metagenomic data of the <span class="html-italic">M. crassifolia</span> plant across different soil types (e.g., rhizosphere and bulk soil).</p>
Full article ">Figure 13
<p>The relative microbial abundance of the top ten eukaryotic species, based on nRG identified in the metagenomic data of the <span class="html-italic">M. crassifolia</span> plant across different soil types (e.g., rhizosphere and bulk soil).</p>
Full article ">Figure 14
<p>(<b>A</b>) The LEFSe line graph represents species that show a significant difference in abundance, with an LDA threshold of 4.0. The length of each bar in the graph is proportional to the size of the effect (LDA score), with longer bars indicating greater differences in abundance. The <span class="html-italic">x</span>-axis represents the LDA score for the bulk soil ‘B’ group. (<b>B</b>) The phylogenetic tree illustrates the relative abundance and evolutionary links between taxa. The nodes on the tree denote taxonomic classifications, while the branching patterns indicate putative ancestral relationships. The size of each node correlates with the observed abundance of that taxon. The highlighted red node labeled “a” identifies “Unclassified Acidimicrobiales” as a biomarker taxon distinct from the indistinguishable yellow nodes. The red marker “B” means that this biomarker taxon is associated with the bulk soil habitat.</p>
Full article ">Figure 14 Cont.
<p>(<b>A</b>) The LEFSe line graph represents species that show a significant difference in abundance, with an LDA threshold of 4.0. The length of each bar in the graph is proportional to the size of the effect (LDA score), with longer bars indicating greater differences in abundance. The <span class="html-italic">x</span>-axis represents the LDA score for the bulk soil ‘B’ group. (<b>B</b>) The phylogenetic tree illustrates the relative abundance and evolutionary links between taxa. The nodes on the tree denote taxonomic classifications, while the branching patterns indicate putative ancestral relationships. The size of each node correlates with the observed abundance of that taxon. The highlighted red node labeled “a” identifies “Unclassified Acidimicrobiales” as a biomarker taxon distinct from the indistinguishable yellow nodes. The red marker “B” means that this biomarker taxon is associated with the bulk soil habitat.</p>
Full article ">Figure 15
<p>The microbiome of the rhizosphere is mainly structured by chemotaxis, i.e., the controlled movement of microbes in response to chemicals. This behavior can be described as either negative chemotaxis, in which microbes move away from a chemical repellent, or positive chemotaxis, in which they move toward a chemical attractant. Under the guidance of chemical cues from root exudates, PGPMs show positive chemotaxis towards plant roots. These microbes can colonize the rhizosphere more easily thanks to this controlled migration, which improves plant development. On the other hand, certain chemicals contained in root exudates or released by PGPMs, such as <span class="html-italic">Nonomuraea</span> spp. in the rhizosphere, can induce negative chemotaxis on phytopathogens, including <span class="html-italic">Aspergillus</span> spp., <span class="html-italic">Fusarium</span> spp. and <span class="html-italic">R. arrhizus</span>. This repellent effect restricts their access to the rhizosphere, confining them to the bulk soil and protecting the plant.</p>
Full article ">
27 pages, 1890 KiB  
Review
Non-Rhizobial Endophytes (NREs) of the Nodule Microbiome Have Synergistic Roles in Beneficial Tripartite Plant–Microbe Interactions
by Ahmed Idris Hassen, Esther K. Muema, Mamonokane O. Diale, Tiisetso Mpai and Francina L. Bopape
Microorganisms 2025, 13(3), 518; https://doi.org/10.3390/microorganisms13030518 - 26 Feb 2025
Viewed by 163
Abstract
Microbial symbioses deal with the symbiotic interactions between a given microorganism and another host. The most widely known and investigated microbial symbiosis is the association between leguminous plants and nitrogen-fixing rhizobia. It is one of the best-studied plant–microbe interactions that occur in the [...] Read more.
Microbial symbioses deal with the symbiotic interactions between a given microorganism and another host. The most widely known and investigated microbial symbiosis is the association between leguminous plants and nitrogen-fixing rhizobia. It is one of the best-studied plant–microbe interactions that occur in the soil rhizosphere and one of the oldest plant–microbe interactions extensively studied for the past several decades globally. Until recently, it used to be a common understanding among scientists in the field of rhizobia and microbial ecology that the root nodules of thousands of leguminous species only contain nitrogen-fixing symbiotic rhizobia. With the advancement of molecular microbiology and the coming into being of state-of-the-art biotechnology innovations, including next-generation sequencing, it has now been revealed that rhizobia living in the root nodules of legumes are not alone. Microbiome studies such as metagenomics of the root nodule microbial community showed that, in addition to symbiotic rhizobia, other bacteria referred to as non-rhizobial endophytes (NREs) exist in the nodules. This review provides an insight into the occurrence of non-rhizobial endophytes in the root nodules of several legume species and the beneficial roles of the tripartite interactions between the legumes, the rhizobia and the non-rhizobial endophytes (NREs). Full article
(This article belongs to the Section Plant Microbe Interactions)
Show Figures

Figure 1

Figure 1
<p>Signaling among microorganisms and inter-kingdom signaling between microorganisms and plants in the rhizosphere. Source [<a href="#B18-microorganisms-13-00518" class="html-bibr">18</a>] with permission.</p>
Full article ">Figure 2
<p>One of the proposed mechanisms of how non-rhizobial endophytes enter the root nodules and become part of the nodule microbiome that includes both the symbiotic rhizobia and the once free-living rhizobacteria, which were part of the rhizosphere soil. Rhizobium species are colored in purple, while free-living NRE are colored in red, blue and green. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> [<a href="#B32-microorganisms-13-00518" class="html-bibr">32</a>].</p>
Full article ">Figure 3
<p>Microbial diversity of the nodule microbiome of Actinorhizal plants determined using shotgun metagenomics. The core microbiome of the nodules retrieved from greenhouse (<b>A</b>) and field (<b>B</b>) samples, comprising a total of 27 and 41 families, respectively. Source: [<a href="#B37-microorganisms-13-00518" class="html-bibr">37</a>] with permission.</p>
Full article ">Figure 4
<p>A schematic representation of how PGPR are involved in the mitigation of various abiotic stresses, including drought, salinity, and nutrient deficiency (fertility) stress, and elicit induced systemic tolerance (IST) in plants. Source: [<a href="#B65-microorganisms-13-00518" class="html-bibr">65</a>] with permission.</p>
Full article ">Figure 5
<p>(<b>A</b>) In vitro siderophore production by non-rhizobial endophytes <span class="html-italic">Herbaspirillum seropedicae</span> and <span class="html-italic">Burkholderia</span> sp. isolated from the nodules of <span class="html-italic">Aspalathus linearis</span> (rooibos). (<b>B</b>) <span class="html-italic">Aspalathus linearis</span> inoculated with the non-rhizobial endophytic strain <span class="html-italic">Herbaspirillum lucitanum</span> (<b>left</b>) as compared to the non-inoculated control (<b>right</b>). Source: [<a href="#B96-microorganisms-13-00518" class="html-bibr">96</a>].</p>
Full article ">
17 pages, 6682 KiB  
Article
Untangling the Characteristics and Ecological Processes of Microbial Community Assembly in the Source Area of the East Route of the South-to-North Water Diversion Project in China Under Different Water Periods
by Wei Cai, Xin Wen, Yueru Zhao, Xiusen Wu, Haoran Zheng, Jiangtao Chen, Zhengyang Hu, Qin Zhong and Jun Wu
Water 2025, 17(5), 649; https://doi.org/10.3390/w17050649 - 23 Feb 2025
Viewed by 274
Abstract
This study presented a comprehensive analysis of the microbial ecology in water diversion rivers (WDRs) in the source area of the East Route of the South-to-North Water Diversion Project (ER-SNWDP) in China across various water periods. Proteobacteria, Chloroflexi, Acidobacteriota, and [...] Read more.
This study presented a comprehensive analysis of the microbial ecology in water diversion rivers (WDRs) in the source area of the East Route of the South-to-North Water Diversion Project (ER-SNWDP) in China across various water periods. Proteobacteria, Chloroflexi, Acidobacteriota, and Bacteroidota were identified as the dominant microbial phyla in river sediment. During the wet period, microbial communities exhibited the highest richness, biodiversity, and the most intense antagonistic relationships compared to those in the dry and normal water periods. Generally, the microbial network predominantly existed in symbiotic models characterized by mutual benefit and symbiosis throughout all periods. During the dry period, the microbial co-occurrence network was found to be the most complex, with microbial OTUs showing the closest interconnections. The dominant mechanisms governing community diversity, succession, and biogeography were spatial turnover of species and stochastic processes. A more pronounced impact of stochastic processes on microbial community assemblages was observed during normal or wet periods than the dry period. Functional prediction of metabolic pathways indicated that the main ecological functions of microbial communities encompassed carbohydrate metabolism, amino acid metabolism, energy metabolism, etc. This study could provide essential scientific data for ecological regulation, ecological protection, and water resources management in WDRs. Full article
(This article belongs to the Special Issue Freshwater Ecosystems—Biodiversity and Protection)
Show Figures

Figure 1

Figure 1
<p>Map of the study area and sampling sites.</p>
Full article ">Figure 2
<p>Microbial community composition at phylum level (A1–A15 represent samples collected in the dry period, B1–B15 represent samples collected in the normal period, and C1–C15 represent samples collected in the wet period).</p>
Full article ">Figure 3
<p>Microbial community composition at genus level (A1–A15 represent samples collected in the dry period, B1–B15 represent samples collected in the normal period, and C1–C15 represent samples collected in the wet period).</p>
Full article ">Figure 4
<p>(<b>a</b>) PCoA and (<b>b</b>) NMDS analysis of the microbial communities (red squares represent samples collected in the dry period, blue circles represent samples collected in the normal period, and green triangles represent samples collected in the wet period).</p>
Full article ">Figure 5
<p>Co-occurrence network analysis of microbial communities in different water periods. The co-occurrence network of microbial communities in the dry period (<b>a</b>), normal period (<b>b</b>), and wet period (<b>c</b>).</p>
Full article ">Figure 6
<p>Fit of the neutral community model (NCM) of microbial community assembly. The predicted frequencies of occurrence for all periods, dry period, normal period, and wet period groups representing microbial communities from all periods (<b>a</b>), dry period (<b>b</b>), normal period (<b>c</b>), and wet period (<b>d</b>), respectively. Nm indicates the estimates of the metacommunity size times immigration rate, N demonstrates the metacommunity size, m is the immigration rate, and the coefficient of determination (R<sup>2</sup>) is the goodness of fit of the neutral model.</p>
Full article ">Figure 7
<p>Relative abundance heatmap of predicted metagenomes using KEGG genes (A1–A15 represent samples collected in the dry period, B1–B15 represent samples collected in the normal period and C1–C15 represent samples collected in the wet period).</p>
Full article ">
23 pages, 3202 KiB  
Article
Exploring Trigeneration in MSW Gasification: An Energy Recovery Potential Study Using Monte Carlo Simulation
by Katarina Pegg, Grant Wilson and Bushra Al-Duri
Energies 2025, 18(5), 1034; https://doi.org/10.3390/en18051034 - 20 Feb 2025
Viewed by 333
Abstract
This study evaluates the potential of gasification-based energy-from-waste (EfW) as a sustainable alternative to the current incineration facility in an industrial zone of a major UK city. The city generates approximately 475,000 tonnes of municipal solid waste (MSW) annually, with around 285,000 tonnes [...] Read more.
This study evaluates the potential of gasification-based energy-from-waste (EfW) as a sustainable alternative to the current incineration facility in an industrial zone of a major UK city. The city generates approximately 475,000 tonnes of municipal solid waste (MSW) annually, with around 285,000 tonnes suitable for gasification-based energy recovery. Using a Monte Carlo type approach, we assess energy outputs across three scenarios: electricity-focused; balanced; hydrogen-focused. Results show that the industrial zone’s annual demand for heat and electricity are covered by all three scenarios, although the analysis does not seek to balance supply and demand over sub-annual timeframes. This suggests that energy-from-waste can support local energy demand and enable industrial symbiosis. At the city scale, however, only 7% of annual electricity demand is covered by the electricity-focused scenario with the balanced scenario only covering 4%. The hydrogen-focused scenario yields enough hydrogen annually to power up to 3400 buses, well beyond the current fleet of 144 and the target fleet of 480 by 2035, positioning the area as a potential hydrogen hub. The balanced scenario offers adaptable energy outputs, supporting diverse energy needs and reducing dependency on conventional incineration. Full article
(This article belongs to the Section B: Energy and Environment)
Show Figures

Figure 1

Figure 1
<p>Map location of Birmingham and Tyseley in the UK.</p>
Full article ">Figure 2
<p>Flow diagram of the MSW gasification system. MSW is converted to syngas in the gasifier, followed by cleaning. The syngas feeds into a CHP engine for electricity and heat generation, while a hydrogen separator extracts hydrogen for storage, enabling flexible trigeneration. The process also generates a solid residue (10–20% of feedstock weight), consisting of ash and unconverted carbon which may be repurposed in construction (e.g., cement additives) or require treatment for safe disposal. Residues with high carbon content can be processed into activated carbon or further oxidised.</p>
Full article ">Figure 3
<p>Average annual totals and composition of local authority-collected waste for 21 UK cities between 2016–2022.</p>
Full article ">Figure 4
<p>Material breakdown by percentage from 2016 to 2022.</p>
Full article ">Figure 5
<p>Average percentages of gasifiable MSW from 2016 to 2022.</p>
Full article ">Figure 6
<p>Birmingham’s waste composition 2015–2022.</p>
Full article ">Figure 7
<p>Seasonal/quarterly variation in the quantity of MSW in Birmingham 2015 to 2023.</p>
Full article ">Figure 8
<p>Average energy output in kWh per tonne of MSW for each scenario.</p>
Full article ">Figure 9
<p>Optimal energy output in kWh per tonne of MSW for each scenario.</p>
Full article ">Figure 10
<p>Supply vs. demand—average energy output for each scenario.</p>
Full article ">Figure 11
<p>Supply vs. demand—optimised energy output for each scenario.</p>
Full article ">
26 pages, 5315 KiB  
Article
Biomimicry-Based Design of Underground Cold Storage Facilities: Energy Efficiency and Sustainability
by Mugdha Kshirsagar, Sanjay Kulkarni, Ankush Kumar Meena, Danby Caetano D’costa, Aroushi Bhagwat, Md Irfanul Haque Siddiqui and Dan Dobrotă
Biomimetics 2025, 10(2), 122; https://doi.org/10.3390/biomimetics10020122 - 18 Feb 2025
Viewed by 374
Abstract
Underground cold storage gives rise to special challenges that require innovative solutions to ensure maximum energy efficiency. Conventional energy systems tend to be based on high energy use, so sustainable solutions are crucial. This study explores the novel idea of biomimetics and how [...] Read more.
Underground cold storage gives rise to special challenges that require innovative solutions to ensure maximum energy efficiency. Conventional energy systems tend to be based on high energy use, so sustainable solutions are crucial. This study explores the novel idea of biomimetics and how it might be used in the planning and building of underground cold storage facilities as well as other infrastructure projects. Biomimetic strategies, inspired by termite mounds, gentoo penguin feathers, and beehive structures, are applied to minimize reliance on energy-intensive cooling systems. These natural models offer efficient thermal regulation, airflow optimization, and passive cooling mechanisms such as geothermal energy harvesting. The integration of naturally driven convection and ventilation ensures stable internal temperatures under varying conditions. Biomimicry was employed in Revit Architecture, coupled with structural optimization, to eliminate urban space’s limitations and further increase energy efficiency. The analytical work for this paper utilized a set of formulas that represent heat flow, thermal resistance, R-value, thermal transmittance, U-value, solar absorption, and G-value. The results pointed to very good insulation, with exterior walls having an R-value of 10.2 m2K/W and U-value of 0.98 W/m2K. Among the chosen 3-layer ETFE cushion with a U-value of 1.96 W/m2K, with a G-value of 0.50, showed good heat regulation and daylight management. Furthermore, bagasse-cement composites with a very low thermal conductivity of 0.10–0.30 W/m·K provided good insulation. This research proposes a scalable and sustainable approach in the design of underground cold storage by merging modelling based on Revit with thermal simulations. Biomimicry has been demonstrated to have the potential for changing subterranean infrastructure, conserving energy consumption, and creating eco-friendly construction practices. Full article
Show Figures

Figure 1

Figure 1
<p>Methodology Flowchart.</p>
Full article ">Figure 2
<p>Dimensions of the proposed storage boxes.</p>
Full article ">Figure 3
<p>Iceberg concept implemented for the cold storage facility with (90:10) underground-to-above-ground ratio (made using Revit).</p>
Full article ">Figure 4
<p>ETFE roofing in a research building in Braunschweig, Germany. Source: <a href="https://specialtyfabricsreview.com/wp-content/uploads/sites/28/2018/03/6907_20171222_1N4V9587_1_Hanno-Keppel.jpg" target="_blank">https://specialtyfabricsreview.com/wp-content/uploads/sites/28/2018/03/6907_20171222_1N4V9587_1_Hanno-Keppel.jpg</a> (accessed on 1 December 2024). Photos: Hanno Keppel.</p>
Full article ">Figure 5
<p>3-layer ETFE cushion system implemented in the surface warehouse facility (made using Revit).</p>
Full article ">Figure 6
<p>Body structure of a Gentoo penguin.</p>
Full article ">Figure 7
<p>Cross-section of the exterior wall of subsurface cold storage facility showing the layers of insulation.</p>
Full article ">Figure 8
<p>Induced flow model for termite mound ventilation.</p>
Full article ">Figure 9
<p>Schematic Drawing of ventilator hoods.</p>
Full article ">Figure 10
<p>Structure A: Bagasse-Cement Composite Wall.</p>
Full article ">Figure 11
<p>Structure B: Common insulating wall.</p>
Full article ">Figure 12
<p>Structure C: Regular masonry wall.</p>
Full article ">Figure 13
<p>Spread of forces on a hexagon cell.</p>
Full article ">Figure 14
<p>Difference between regular and non-regular tessellating patterns.</p>
Full article ">
17 pages, 3574 KiB  
Article
Genome-Wide Identification and Expression Analyses of Glycoside Hydrolase Family 18 Genes During Nodule Symbiosis in Glycine max
by Rujie Li, Chuanjie Gou, Ke Zhang, Milan He, Lanxin Li, Fanjiang Kong, Zhihui Sun and Huan Liu
Int. J. Mol. Sci. 2025, 26(4), 1649; https://doi.org/10.3390/ijms26041649 - 14 Feb 2025
Viewed by 359
Abstract
Glycoside hydrolase family 18 (GH18) proteins can hydrolyze the β-1,4-glycosidic bonds of chitin, which is a common structure component of insect exoskeletons and fungal cell walls. In this study, 36 GH18 genes were identified and subjected to bioinformatic analysis based on the genomic [...] Read more.
Glycoside hydrolase family 18 (GH18) proteins can hydrolyze the β-1,4-glycosidic bonds of chitin, which is a common structure component of insect exoskeletons and fungal cell walls. In this study, 36 GH18 genes were identified and subjected to bioinformatic analysis based on the genomic data of Glycine max. They were distributed in 16 out of 20 tested soybean chromosomes. According to the amino acid sequences, they can be further divided into five subclades. Class III chitinases (22 members) and class V chitinases (6 members) are the major two subclades. The amino acid size of soybean GH18 proteins ranges from 173 amino acids (aa) to 820 aa and the molecular weight ranges from 19.46 kDa to 91.01 kDa. From an evolutionary perspective, soybean GH18 genes are closely related to Medicago (17 collinear loci with soybean) and Lotus (23 collinear loci with soybean). Promoter analysis revealed that GH18 genes could be induced by environmental stress, hormones, and embryo development. GmGH18-15, GmGH18-24, and GmGH18-33 were screened out due to their nodulation specific expression and further verified by RT-qPCR. These results provide an elaborate reference for the further characterization of specific GH18 genes, especially during nodule formation in soybean. Full article
(This article belongs to the Special Issue Genetics and Novel Techniques for Soybean Pivotal Characters)
Show Figures

Figure 1

Figure 1
<p>Chromosome mapping of the <span class="html-italic">GH18</span> genes in <span class="html-italic">G. max</span>. Here, 20 soybean chromosomes were displayed in scale with a ruler beside. The position of each <span class="html-italic">GH18</span> gene was clearly marked on the chromosomes. The yellow to blue gradient represents different gene density, with yellow as high gene density region and blue as low gene density region.</p>
Full article ">Figure 2
<p>Phylogenetic analysis and protein structure identification of <span class="html-italic">GH18</span> genes in soybean. (<b>a</b>) A phylogenetic tree was constructed by the Maximum Likelihood method. Bootstrap tests with 1000 replicates were performed. (<b>b</b>) Here, 10 conserved motifs and 10 conserved domains were identified in soybean GH18 proteins, respectively.</p>
Full article ">Figure 3
<p>Phylogenetic analysis of GH18 proteins in soybean, Medicago, and Lotus. Based on the HMM search result of PF00704, 36, 47, and 18 putative GH18 proteins were identified in soybean, Medicago, and Lotus, respectively. They were further divided into 6 groups using the Maximum Likelihood method implemented on MEGA7.0. There were 50 genes in the class III chitinase clade, 23 genes in the class V chitinase clade, 3 genes in the stabilin-1 interacting chitinase-like protein clade (SI-CLP), 3 genes in the chitinase-like superfamily, 6 genes in the narbonin clade, and 16 genes in the Medicago-specific GH18 clade, respectively. Due to the reported biological function in legume–rhizobium symbiosis, MtGH18-20 (MtNFH1), MtGH18-21 (MtCHIT5b), and LjGH18-12 (LjChit5) are highlighted in red.</p>
Full article ">Figure 4
<p>Collinearity analysis of <span class="html-italic">GH18</span> genes in soybean. Here, 20 soybean chromosomes were arranged in circle. Gray lines represent the gene pairs in the whole genome. Red lines highlight the collinear relationship within the <span class="html-italic">GH18</span> gene family.</p>
Full article ">Figure 5
<p>Collinearity analysis of <span class="html-italic">GH18</span> genes between two species of soybean, Medicago, and Lotus. Collinearity analysis was performed between two given species: soybean–Medicago (<b>a</b>), soybean–Lotus (<b>b</b>), and Medicago–Lotus (<b>c</b>). Gray lines from the background show all collinear gene pairs between two genomes, while red lines highlight the collinear relationship within the GH18 family.</p>
Full article ">Figure 6
<p>Cis-acting element analysis of <span class="html-italic">GH18</span> gene promoter regions in <span class="html-italic">G. max</span>. 41 <span class="html-italic">GH18</span> gene promoter sequences (−2000 bp upstream of ATG) are obtained from <span class="html-italic">G. max</span> Wm82 genome (a2.v1). The gene symbols are listed on the left and the nucleotide positions are labeled at the bottom. In total, 13 cis-acting elements are identified, whose potential biological functions are related to drought responsiveness, anaerobic responsiveness, light responsiveness, gibberellin responsiveness, abscisic acid responsiveness, defense/stress responsiveness, low-temperature responsiveness, endosperm expression, zein metabolism regulation, meristem expression, Methyl jasmonate (MeJA) responsiveness, salicylic acid responsiveness, and auxin responsiveness.</p>
Full article ">Figure 7
<p>Expression profile of soybean <span class="html-italic">GH18</span> genes and rhizobial responsiveness validation by RT-qPCR. (<b>a</b>) The <span class="html-italic">GH18</span> gene expression pattern was extracted from the soybean RNA sequencing data [<a href="#B32-ijms-26-01649" class="html-bibr">32</a>] and displayed in the heatmap. According to their expression levels in pods, root hairs, leaves, roots, nodules, seeds, shoot apical meristems (SAM), stems, and flowers, 36 soybean <span class="html-italic">GH18</span> genes were grouped into 5 categories. Group 4 was highlighted, because members from this group are nodulation-specific (N.S.). (<b>b</b>–<b>d</b>) RT-qPCR validation of the expression behavior of group 4 members under symbiotic conditions. <span class="html-italic">G. max</span> Wm82 plants were inoculated with <span class="html-italic">Bradyrhizobium japonicum</span> USDA110; the transcript levels of <span class="html-italic">GmGH18-15</span>, <span class="html-italic">GmGH18-24</span>, and <span class="html-italic">GmGH18-33</span> in the roots (within 7 days post inoculation) and nodules (14 and 21 dpi) were measured (<span class="html-italic">n</span> = 3). The <span class="html-italic">Actin</span> gene served as a reference. Data indicate means ± SE of normalized expression values (mean value of control set to one). The asterisks indicate significantly increased expression compared to control roots without rhizobial inoculation (Student’s <span class="html-italic">t</span>-test; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">
Back to TopTop