Search Results (4,389)

Melatonin plays a crucial role in modulating plant stress responses; however, its potential for mitigating salt–alkali stress remains incompletely understood. This study evaluates the efficacy of exogenous melatonin in alleviating moderate salt–alkali stress (120 mM) in poplar (Populus davidiana × P. bolleana ‘Baicheng Shanxinyang No. 1’) seedlings, investigating both pre- and post-stress treatments across a concentration range of 0–1000 μM. Physiological and morphological parameters, including chlorophyll content, antioxidant enzyme activities, and osmolyte accumulation, were analyzed to assess stress responses. Under salt–alkali stress, seedlings exhibited elevated stress markers and osmolyte levels, reflecting activated stress responses. Melatonin at concentrations of 200–400 μM was the most effective in mitigating stress, significantly enhancing antioxidant enzyme activities such as superoxide dismutase (SOD) and catalase (CAT), restoring chlorophyll content, and reducing oxidative damage markers such as malondialdehyde (MDA). It also regulated osmotic balance in leaves, indicating improved cellular stability under stress. Notably, post-stress application required slightly higher melatonin concentrations to achieve comparable recovery, highlighting the critical influence of application timing. These findings provide valuable insights for optimizing melatonin use to improve poplar growth in saline–alkali environments and support molecular breeding efforts aimed at developing salt–alkali-tolerant poplar varieties. Full article

Screening potato germplasm for low nitrogen (LN) tolerance is essential for improving nitrogen use efficiency and agricultural sustainability. A set of 156 potato genotypes from diverse sources—including the International Potato Center (CIP), the National Potato Germplasm Repository (CAAS), and varieties and lines bred by the Qinghai Academy of Agriculture and Forestry Sciences—was evaluated under optimal (60 mmol·L⁻¹) and low (3 mmol·L⁻¹) nitrogen conditions using tissue culture. Nine traits related to growth, nitrogen accumulation, and nitrogen use efficiency were measured. Under LN stress, nitrogen physiological efficiency (NPE), uptake efficiency (NUpE), and utilization efficiency (NUE) increased, while most growth-related traits declined. Considerable variation was observed in fresh weight (FW), dry weight (DW), nitrogen accumulation (NA), and NUE, with coefficients of variation ranging from 0.38 to 0.40 under LN and 0.17 to 0.42 under ON. Principal component analysis identified NA and NUpE as the primary contributors to phenotypic variation. Based on comprehensive D-values from cluster analysis, the genotypes were classified into five tolerance groups: Type I—(strong low-nitrogen tolerant (13 accessions); Type II—low-nitrogen tolerant (66 accessions); Type III—moderate low-nitrogen tolerant (36 accessions); Type IV—low-nitrogen sensitive (24 accessions); and Type V—highly low-nitrogen sensitive (17 accessions). Physiological validation revealed two distinct adaptive strategies: a nitrogen conservation strategy (Type I), characterized by high NA and nitrogen content (NC) alongside moderate physiological efficiency, and an efficiency-driven compensation strategy (Types II, IV, and V), marked by low NA and NC but high physiological efficiency. The congruence between multivariate clustering and subsequent physiological measurements confirms that this classification effectively captures genotypic differences in low nitrogen tolerance. Thirteen highly LN-tolerant genotypes—including PIMPERNEL, Favorita, and Spunta—were identified as promising genetic resources for breeding nitrogen-efficient potato cultivars. This tissue culture-based screening method provides a practical tool for evaluating nitrogen tolerance in plants and supports sustainable potato production under limited nitrogen availability. Full article

Background/Objectives: Medicinal plants are widely investigated due to their rich content of biologically active secondary metabolites with potential therapeutic applications. This study aimed to investigate the phytochemical profile and antioxidant activity of extracts with different polarities obtained from Justicia thunbergioides (Lindau) Leonard (Acanthaceae). Methods: Phytochemical screening was initially performed through qualitative analysis, followed by fractionation and characterization of dichloromethane and methanolic extracts using thin-layer chromatography (TLC) and gas chromatography–mass spectrometry (GC–MS). Antioxidant activity was evaluated using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay and electrochemical techniques. Results: GC–MS analysis of the dichloromethane extract revealed a chemically diverse composition, including compounds such as spathulenol, vitamin E, sesamin, squalene, and β-sitosterol, which are widely reported in the literature for their antioxidant and bioactive properties. The methanolic extract exhibited a distinct chemical profile, with a predominance of phenolic and redox-active compounds. DPPH assays demonstrated that the methanolic extract showed the highest radical scavenging capacity in a concentration-dependent manner, whereas the dichloromethane and hexane extracts required higher concentrations to achieve moderate antioxidant effects. Electrochemical analyses indicated that the methanolic extract is rich in electroactive metabolites capable of partially reversible electron transfer, consistent with its enhanced antioxidant performance. Conclusions: Collectively, these findings highlight the antioxidant efficacy of the polar extracts from J. thunbergioides and contribute to a better understanding of the bioactivity of their phytochemical constituents. Full article

The southern Junggar Basin in Xinjiang is rich in coalbed methane (CBM) resources. Large-scale development is underway in the Sigong River block (SGR block) of the Fukang West Block. Based on an integrated analysis of geological and hydrogeochemical characteristics, this study clarifies the key factors affecting CBM well productivity in the SGR block. Based on gas and water production performance, four distinct productivity types of CBM wells are identified, which are jointly controlled by burial depth, local structural and hydraulic disturbance, and also governed by synergistic interplay between gas content and permeability. The optimal geological combination—comprising the 700–1000 m burial depth, syncline core structure, stagnant hydrodynamic conditions, relatively high gas content, and favorable permeability—collectively contributes to the high-productivity Type I wells with low water production. In contrast, deep coal seams (>1400 m), characterized by reduced gas content and extremely low permeability, correspond to Type IV wells, which exhibit low gas and water production. Type II wells, located in the 1000–1400 m interval, exhibit moderate and variable productivity controlled by the interplay between high gas content and a wide range of permeability. Shallow margins (<700 m) affected by coal combustion and surface water influx produce high-water and low-gas wells (Type III). Geochemical signatures effectively differentiate between these types: closed, stagnant environments (Types I/II) are marked by a Na-Cl-HCO₃/Na-HCO₃-Cl water type, moderate total dissolved solids, and low sodium chloride coefficients, while open hydrodynamic conditions (Type III) are indicated by Na-SO₄-HCO₃ water with high sodium chloride coefficients. A δD-H₂O/δ¹⁸O-H₂O ratio of 7–9, combined with favorable TDS and water type, is identified as a key indicator of high productivity. Based on these relationships, a productivity response index model incorporating critical geological and geochemical parameters was developed. This model provides a practical tool for predicting CBM well performance and targeting sweet spots, offering significant value for exploring geologically and hydrologically complex basins. Full article

Detecting violent content in audio recordings is crucial for public safety, autonomous surveillance, and content moderation, particularly when visual cues are unreliable or unavailable. A resource-aware two-stage cascade system is proposed for acoustic violence detection that combines a lightweight Least Squares Linear Detector (LSLD) as a first-stage screener with a trimmed version of YAMNet as a second-stage classifier. A percentile-based forwarding rule controls the fraction of segments routed to the deep stage, turning the accuracy–cost trade-off into an explicit operating parameter for always-on deployment. The approach is evaluated on a publicly released dataset of real-world violent audio augmented with background noise and artificial reverberation. The results in the low-false-alarm regime show that the proposed cascade preserves performance close to a Stage 2-only baseline while substantially reducing average deep-inference workload. An ablation study validates the role of the LSLD as an inexpensive pre-filter, and robustness is assessed under clean, reverberant, and 12 dB noise conditions. Finally, an analytic energy consumption model is provided, which links computational workload to daily energy demand and photovoltaic sizing on ultra-low-power hardware, supporting sustainable off-grid deployment. Full article

Background/Objectives: Breast cancer awareness is essential for early detection and timely help-seeking among women and represents a key component of multidisciplinary breast cancer prevention. The Breast Cancer Awareness Measure (Breast-CAM) is widely used to assess awareness of breast cancer symptoms, risk factors, and screening behaviors. Its measurement quality across populations has not yet been comprehensively evaluated. As Breast-CAM is a population-reported measurement instrument, evaluation using a standardized framework for measurement properties is required. This systematic review and meta-analysis aimed to assess the psychometric properties of the Breast-CAM across diverse populations and cultural adaptations, in accordance with COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) methodological standards. Methods: Major bibliographic databases and trial registries were systematically searched for peer-reviewed English-language studies published between 2010 and 2025 that evaluated at least one psychometric property of the Breast-CAM in adult women. Methodological quality was assessed using the COSMIN Risk of Bias checklist. Measurement properties were evaluated according to COSMIN criteria, and the certainty of evidence was graded using a modified GRADE approach. Meta-analysis was performed when data were sufficiently comparable. Results: Seventeen studies met the inclusion criteria for narrative synthesis, of which eleven were included in a meta-analysis, representing fourteen cultural adaptations of the instrument. A descriptive random-effects meta-analysis of reported Cronbach’s α yielded a pooled estimate of 0.89 (95% confidence interval 0.85–0.92). This value should be interpreted cautiously, as structural validity was frequently insufficient across cultural adaptations, limiting interpretation of internal consistency according to COSMIN guidance. Other measurement properties, including reliability and measurement error, were frequently inadequately assessed or unreported. The certainty of evidence ranged from very low to moderate. Conclusions: Content validity was generally rated as sufficient, although certainty of evidence was low. Despite the high pooled α estimate, the reliability of Breast-CAM cannot be firmly established because structural validity was frequently insufficient across cultural adaptations. In accordance with the COSMIN ceiling rule, internal consistency was not considered sufficient in the absence of adequate structural validity. Key measurement properties, including test–retest reliability, measurement error, and responsiveness, were rarely evaluated. Further high-quality psychometric studies, particularly in culturally diverse populations, are needed to address these gaps and support appropriate use of the instrument in research and public health practice. Full article

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

Selenium (Se) enrichment in yeast represents a promising strategy for producing organic Se with high bioavailability. However, a systematic understanding of how Se incorporation alters intact protein structure and function across diverse strains remains lacking. This study investigated four yeast species (Saccharomyces cerevisiae, Kluyveromyces marxianus, Kluyveromyces lactis, and Torulaspora delbrueckii) using multi-spectroscopic and radical scavenging assays. Despite moderate growth inhibition (10.4–27.7%), all strains accumulated substantial Se (1164–2858 µg/g). Structural analysis revealed that Se induced strain-dependent protein conformational perturbations. Specifically, in selenium-enriched Saccharomyces cerevisiae, where Se was predominantly incorporated as selenomethionine (SeMet, 85.80%), a significant structural relaxation occurred. This was characterized by decreased rigid β-sheet content, increased flexible random coils, and a substantial enhancement in surface hydrophobicity. Crucially, Pearson correlation analysis revealed that functional enhancements were synergistically governed by specific Se speciation and secondary structural remodeling. Enhanced DPPH^• scavenging activity was positively correlated with changes in β-sheet and random coil structures. Selenomethionine content was also significantly correlated with increased scavenging of ^•OH and ABTS^•+. Consequently, Saccharomyces cerevisiae uniquely achieved highly significant (p < 0.001) antioxidant improvements, whereas other strains showed moderate or non-significant responses despite high Se yields. Our findings demonstrate that the antioxidant efficacy of selenoproteins is not solely determined by total Se content but is fundamentally driven by the targeted bioconversion of SeMet and its associated structural relaxation. Full article

Postharvest dehydration is a major determinant of cucumber freshness and marketability, yet early reductions in internal water status are difficult to detect using conventional quality assessment methods. This study presents a non-destructive, physiology-informed deep learning approach that links cucumber surface color and texture patterns to internal hydration level for automated freshness classification. A time-resolved dataset comprising 4160 RGB images of cucumber fruits was paired with gravimetrically determined relative water content (RWC), used as an objective indicator of internal hydration status. Based on RWC, fruits were classified into four freshness categories: Very Fresh (≥98%), Moderately Fresh (95–98%), Low Freshness (90–95%), and Spoiled (<90%). A custom convolutional neural network (CNN) was trained using standardized RGB images and evaluated on an independent test set. The model achieved an overall classification accuracy of 91.35% and a Cohen’s Kappa coefficient of 0.875, indicating strong agreement between predicted and actual freshness classes. Classification performance was highest for the extreme freshness states, with F1-scores exceeding 0.94 for Very Fresh and Spoiled fruits, while intermediate classes showed greater overlap, reflecting the gradual nature of postharvest water loss. Model interpretability analyses revealed that the CNN consistently focused on physiologically meaningful surface color and texture features associated with dehydration. Overall, these findings highlight the potential of physiology-informed deep learning to advance non-destructive freshness assessment in cucumbers, offering a realistic pathway toward hydration-based sorting, improved shelf-life management, and intelligent quality monitoring in modern postharvest supply chains. Full article

The application of leguminous green manure (GM) can enhance the soil inorganic phosphorus (Pi) pool, offering considerable benefits for crop cultivation in slightly and moderately saline-alkali soils. To optimize its agronomic potential, systematic and science-based fertilization strategies are required. In this study, we researched the changes in the content, movement distance, and accumulation of Pi fractions at the GM microsites in coastal saline-alkali soils of differing salinity levels (slightly vs. moderately) following the application of Sesbania GM at two rates (30 and 60 t ha⁻¹) over 14- and 28-day incubation periods. The results indicated that GM application significantly (p < 0.05) increased the accumulation of all Pi fractions—including aluminum-bound phosphorus (Al-P), iron-bound phosphorus (Fe-P), occluded phosphorus (O-P), and forms of calcium-bound Pi (Ca-P: Ca₂-P, Ca₈-P, and Ca₁₀-P)—at the manure microsite, with the magnitude of increase declining with distance from the manure site. Further analysis revealed positive correlations between GM rate, two incubation periods and Pi-fraction movement distance, indicating that the observed effects were significantly influenced by incubation period, GM rate, and soil salinity-alkalinity. While temporal dynamics governed the rates of Pi movement and transformation, elevated salinity-alkalinity partially inhibited these processes. This study provides practical insights for improving GM utilization efficiency on saline-alkali soils. These results support optimized GM application to enhance P efficiency and reduce fertilizer reliance in saline systems. Full article

Light quality is a crucial factor influencing plant growth and physiological quality in controlled-environment agriculture (CEA). This study examined how different LED light spectra affect the growth and internal quality of Ligularia stenocephala cultivated in a plant factory. The plants were grown under five types of LED light: monochromatic red, monochromatic blue, a combination of blue and red, white LEDs, and quantum dot (QD) LEDs. We evaluated various growth parameters, biomass accumulation, chlorophyll indices, and antioxidant capacity. Monochromatic red LEDs promoted rapid early growth and stem elongation but led to lower chlorophyll accumulation and antioxidant capacity. In contrast, monochromatic blue LEDs increased chlorophyll content, leaf thickness, dry matter accumulation, and antioxidant capacity, although they limited leaf expansion and shoot biomass. Composite-spectrum LEDs displayed distinct trade-offs between growth and quality parameters. QD LEDs maximized shoot biomass accumulation while maintaining moderate internal quality, whereas Blue+Red LEDs provided a balanced combination of significant biomass and enhanced phytochemical content. Principal component analysis indicated a fundamental trade-off between quality-related (PC1: 57.6%) and growth-related (PC2: 22.7%) parameters, showing that no single LED spectrum could optimize all cultivation factors simultaneously. Therefore, LED selection should align strategically with specific cultivation goals: use QD LEDs for volume-based production, Blue+Red LEDs for balanced premium markets, and blue LEDs for specialty functional vegetables. These findings underscore the importance of context-dependent lighting optimization strategies in plant factory systems and offer a framework for selecting the most effective LED spectra to enhance crop performance in CEA. Full article

Sorghum (Sorghum bicolor) is a key cereal for food and forage security in arid and semi-arid regions, where climate change is intensifying drought stress and threatening sustainable crop production. Intercropping cereals with legumes is widely promoted as a nature-based solution to improve resource-use efficiency, nitrogen (N) cycling, and drylands’ resilience. We evaluated the performance and interactions of a novel sorghum–legume combination by intercropping sorghum with the drought-tolerant legume serradella (Ornithopus sativus) in a 10-week mesocosm experiment. Cropping systems (sorghum monocrop, serradella monocrop, and strip intercropping) were subjected to moderate or severe water stress, with or without frequent cutting. We investigated how intercropping influenced individual crop growth, N accumulation, and survival, and whether benefits at the plant level translated to the system level. Under severe water stress, sorghum maintained higher biomass and survival than serradella. Intercropping did not increase aboveground biomass or N content at the mesocosm level. However, individual sorghum plants in intercrops accumulated up to 80% more biomass and 100% more aboveground N than in monocropping. In contrast, serradella experienced reduced growth, N accumulation, and survival in intercrops. Our results reveal trade-offs in this intercrop under dryland stress, where individual crop benefits do not translate into system-level gains. Although limited to early growth and controlled conditions, the results provide valuable insights for designing resilient sorghum–legume systems, including optimizing species density, intercrop configuration, and cutting regimes in drought-prone agroecosystems. Full article

To explore the patterns of biomass accumulation and sediment carbon burial indicators in mangrove forests under different thinning intensities, a study was conducted on an 8-year-old Kandelia obovata Sheue & al. plantation on Shupaisha Island, Longwan District, Wenzhou City, Zhejiang Province. Three treatments were designed: no thinning (CK), 20% thinning, and 40% thinning. Stand growth and plant carbon density were evaluated for all three treatments at the initial thinning stage and two years later. Sediment carbon density and organic carbon burial rate were assessed only for CK and 20% thinning. Thinning significantly enhanced mangrove growth and plant carbon storage. Compared with unthinned stands, 20% and 40% thinning treatments significantly increased branch diameter and biomass (p < 0.05). The order of mangrove height was 20% thinning > 40% thinning > CK. The plant carbon densities in the 20% and 40% thinned stands were 16.31 Mg C·ha⁻¹ and 15.30 Mg C·ha⁻¹, respectively, far exceeding that of the control (4.80 Mg C·ha⁻¹). In contrast, sediment carbon responses were negative in the short term. After thinning, the sedimentation rate and organic carbon content in mangrove sediments decreased. Sediment carbon density decreased from 88.10 Mg C·ha⁻¹ in unthinned stands to 85.02 Mg C·ha⁻¹ under 20% thinning, accompanied by a reduction in carbon burial rate. Overall, these two-year results indicate increased plant carbon storage under thinning, whereas measured sediment carbon indicators under moderate thinning declined over the same period. Longer-term monitoring is needed to assess whether these short-term responses translate into net ecosystem carbon consequences. Full article

Terminalia arjuna (Arjun) is a tropical deciduous tree species significantly valued for its pharmaceutical properties for various heart diseases, as well as its economic role in the sericulture industry. However, the growth performance and physiological responses of T. arjuna under water stress conditions remain largely unexplored, particularly in the context of increasing climate variability and the growing challenges posed by climate change. Therefore, this study aimed to examine the morpho-physio-biochemical alterations, nutrient uptake changes, and adaptive strategies under different degrees of water stress with respect to field capacity ($F_{wc}$), maintained at 100% $F_{wc}$ (control), 75% $F_{wc}$ (mild), 50% $F_{wc}$ (moderate), and 25% $F_{wc}$ (severe). Key growth parameters, including shoot and root length, leaf traits and shoot dry biomass, were significantly (p < 0.05) reduced under the given water stresses. Root dry biomass showed a distinct response, increasing under mild to moderate water stress but failing to sustain its levels under severe stress. Increasing drought severity resulted in a substantial reduction in stomatal density (15–37%), while stomatal size increased (18–49%) under mild to moderate stress but decreased under severe stress. These responses were associated with significant reductions in gas exchange traits (45–75%), whereas water use efficiency increased by 59–99%, reflecting a survival-focused adaptive mechanism. Moderate water stress triggered the stress responses in T. arjuna through high proline accumulation and increased oxidative stress markers. The most critical impact was found under the severe stress with a substantial reduction in leaf relative water content and membrane stability index (MSI), although MSI was sustained above the critical threshold, reflecting cellular protection. Increased stress intensity also altered mineral uptake, decreased major nutrients, and increased potassium and calcium content, indicating an adaptive strategy. These findings suggest a threshold effect, where T. arjuna tolerates mild stress well and activates adaptive morpho-physiological mechanisms under moderate stress but shifts to survival-focused strategies under severe stress. The demonstrated tolerance of Terminalia arjuna to mild–moderate drought suggests that climate-resilient forestry policies and conservation programs should prioritize its cultivation and restoration in drought-prone landscapes while ensuring adequate water management to prevent severe stress and sustain its medicinal and economic benefits. Full article

Artemisia maritima holds potential applications in the rehabilitation of degraded environments, particularly in salt-affected areas, for biosaline agriculture aimed at biomass production for further valorization and green biotechnology. The aim of the present study was to investigate the response of A. maritima to alterations in soil chemical composition, including differences in mineral supply, the addition of various sodium salts, and contamination with several heavy metals (cadmium, lead, copper, manganese, zinc), in order to establish a scientific basis for further applied research. Under standard fertilization conditions, the growth of A. maritima plants was restrained by nitrogen deficiency. Surplus nitrogen enhanced mineral uptake and growth, especially for shoots, and stimulated clonal development. Low to moderate (50 and 100 mmol L⁻¹) NaNO₃ treatment significantly stimulated shoot growth, while Na₂HPO₄ and NaHCO₃ treatments exhibited the most adverse effects at 200 and 400 mmol L⁻¹, resulting in reduced growth and biomass, and even the deterioration of the aboveground parts. Chlorophyll fluorescence parameters served as reliable early indicators of the detrimental effects of salinity associated with individual anions. Shoot macronutrient levels remained unchanged for phosphorus and calcium, while nitrogen increased in nitrate treatments. Root mineral nutrient content was more susceptible to salinity, with significant changes observed for all macro- and micronutrients, varying depending on the specific element and anion type. The alterations in mineral nutrition observed for each anion treatment exhibited distinct characteristics. A. maritima plants demonstrated high tolerance to all heavy metals, with roots being more susceptible compared to shoots. At the shoot level, statistically significant growth inhibition was evident only for 1000 mg L⁻¹ lead and 1000 mg L⁻¹ zinc treatments. A. maritima plants can be characterized as high accumulators of cadmium, lead, manganese, and zinc, and as extreme accumulators of copper in shoots. Nitrophily, clonal expansion with a help of bud-bearing roots, and the ability to accumulate relatively high concentrations of mineral elements in shoots are among the important physiological characteristics of A. maritima plants, enabling them to exhibit high resilience in environmentally heterogeneous habitats. Full article