Search Results (3,456)

This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach combines edge-level monitoring, anomaly detection, and predictive modeling to analyze operational signals and estimate system health conditions from high-frequency industrial data. Empirical validation was conducted using operational datasets collected from two industrial production facilities between 2024 and 2025. The model evaluates patterns associated with operational instability and degradation-related anomalies and translates them into interpretable health indicators that can support proactive intervention. The empirical results show strong predictive performance, with R² reaching 0.989, a mean absolute percentage error of 3.67%, and a root mean square error of 0.79. In addition, the mitigation of early anomaly signals was associated with an observed improvement of approximately 3.99% in system stability. Unlike many existing studies that treat anomaly detection, predictive modeling, and prognostic analysis as separate tasks, the proposed framework connects these stages within a unified analytical structure designed for deployment in industrial environments. The findings indicate that edge-generated anomaly signals can provide meaningful early information about potential system deterioration and can assist in planning timely maintenance actions even when explicit failure labels are limited. The study contributes to the development of scalable predictive maintenance solutions that integrate artificial intelligence with edge-based industrial monitoring systems. Full article

Saline-alkaline (SA) soils pose a serious threat to soybean production worldwide. Although severe saline-alkaline stress can reduce yield by up to 30%, the mechanisms underlying saline-alkaline-induced inhibition of root growth remain unclear. In this study, two soybean cultivars with contrasting tolerance, Chang Nong 26 (CN26) and Jiyu 441 (JY441), were exposed to saline-alkaline stress induced by NaHCO₃ and Na₂CO₃ at Na⁺ concentrations of 0, 21, and 45 mmol·L⁻¹. The effects on seed germination, early seedling growth, antioxidant responses, and root DNA damage were systematically examined. High-level saline-alkaline stress significantly inhibited germination and root elongation in both cultivars. Superoxide dismutase (SOD) and peroxidase (POD) activities increased markedly under stress, indicating activation of antioxidant defenses. Catalase (CAT) and ascorbate peroxidase (APX) to scavenge ROS and maintain cellular redox balance. Nevertheless, oxygen-free radicals (OFRs) accumulated to a significantly greater extent in the root tips of CN 26 than in JY441, suggesting lower tolerance in CN 26. Random amplified polymorphic DNA (RAPD) analysis revealed pronounced DNA damage in root tips under saline-alkaline stress, with more polymorphic bands detected in CN 26 than in JY441. Furthermore, qRT-PCR analysis demonstrated that the expression of DNA damage repair-related genes (RAD51, OGG1, RAD4, and ATM) was downregulated in CN 26 roots under stress, whereas E2FA and WEE1 expression was upregulated. In contrast, these DNA repair genes in JY441 were significantly induced during the early stage of stress exposure and subsequently declined. Collectively, this study demonstrates that saline-alkaline stress inhibits soybean growth through the induction of oxidative DNA damage and cell cycle arrest in roots. The reduced capacity for DNA repair in CN 26 likely contributes to its greater sensitivity to saline-alkaline stress. This study provides mechanistic insights into saline-alkaline stress-induced growth inhibition in soybean and offers a theoretical basis for breeding stress-tolerant cultivars. Full article

Diabetic retinopathy is a prevalent condition and a major public health concern due to its detrimental impact on eyesight. Diabetes is a root cause of its development and damages small blood vessels caused by prolonged high blood sugar levels. The degenerative consequences of diabetic retinopathy are irrevocable if not diagnosed in the early stages of its progression. This ailment triggers the development of retinal lesions, which can be identified for diagnosis and prognosis. However, lesion detection is challenging due to their similarity in intensity profiles to other retinal features, inconsistent sizes, and random locations. This research evaluates a custom deep learning network for classifying retinal images and compares it with the state-of-the-art classifiers. The novel preprocessing method is introduced to reduce the complexity of the diagnostic process and to enhance classification performance by adaptively enhancing images. Despite being a shallow network, the proposed model yields competitive results with an accuracy of 87.66% and an F1-score of 0.78. The evaluation metrics indicate that class imbalance affects the performance of the proposed model despite using the weighted cross-entropy loss. The future contribution will be the inclusion of generative adversarial networks for generating synthetic images to balance the dataset. This research aims to develop a robust computer-aided diagnostic system as a second interpreter for ophthalmologists during the diagnosis and prognosis stages. Full article

The COVID-19 pandemic caused stark global mortality disparities, influenced by a complex interplay of demographic, economic, and health factors. This ecological study investigates associations between country macroscopic variables and COVID-19 accumulated mortality ratio (AMR) across 174 countries and may serve as a preparation for new pandemics. Methods: The study applies bidirectional stepwise multiple linear regression. To ensure statistical validity, we conducted diagnostic tests for multicollinearity and heteroscedasticity, applying robust M-estimation where necessary to minimize root mean squared error. The analysis covered six distinct stratifications based on development status (developed, developing, least developed, and combinations), and incorporated temporal analyses across three specific annual periods: 21 January 2020–20 January 2021; 21 January 2021–20 January 2022; and 21 January 2022–10 January 2023. Data: AMR per country values, accumulated between 21 January 2020 and 10 January 2023, and data on the prevalence of health conditions, and socioeconomic descriptive variables were extracted from Our World in Data (OWID) and other public data sites, like the World Bank. Results: The percentage of population aged over 65 years has the most consistent association with increased AMR globally. Obesity prevalence and income inequality (Gini index) were positively associated with AMR regardless of country development status. Conversely, the study finds a consistent negative correlation with diabetes prevalence, while the prevalence of respiratory diseases is a significant association only for developed nations. Socioeconomic factors were significantly associated with AMR, but this influence is stronger in developed countries than in the developing and least developed countries. Conclusions: While population aging is the primary association with increased AMR, the mortality impact of comorbidities and socioeconomic factors is heavily conditioned by a country’s development stage, pointing to the necessity of development-status-aware public health strategies for incoming pandemics. Full article

Cadmium (Cd) is widely dispersed in the environment and has emerged as a major environmental contaminant. Although Salix viminalis shows potential for phytoremediation of Cd pollution, the defence mechanism of its roots against heavy metals remains unclear. This study explores the adaptive response of S. viminalis roots to Cd stress from physiological, transcriptomic, and metabolomic perspectives. The results suggest that Cd stress exerts inhibitory effects on root growth and development. Compared with the control (Cd-free), the root volume and dry weight of S. viminalis exposed to Cd decreased by 26% and 29%, respectively. After exposure to Cd stress for 14 and 21 days, the Cd content in the roots increased by 117-fold and 134-fold, the hydrogen peroxide content increased by 89% and 110%, and the malondialdehyde content increased by 82% and 88%, respectively. This phenomenon can be attributed to the fact that the continuous accumulation of Cd in the roots may have aggravated the degree of lipid peroxidation. A total of 9171 differentially expressed genes (DEGs) and 169 differential metabolites (DIMs) were identified through transcriptomic and metabolomic analyses. Further combined analyses revealed the potential roles of several pathways in the defensive response of S. viminalis roots against Cd stress, including plant hormone signal transduction, thiamine metabolism, glycolysis, glycerophospholipid metabolism, and other pathways. Notably, the feedback regulatory effects formed by thiamine metabolism and hormone signal transduction related to auxin, jasmonic acid, and salicylic acid play a crucial role in the early stage when roots are exposed to Cd stress. These effects mobilized osmotic adjustment in roots by enhancing saccharide metabolism and activated the Cd detoxification process by altering lipid metabolism, thereby contributing positively to the defence of willow roots against Cd stress. These findings provide insights into the adaptive mechanism of S. viminalis roots in response to Cd and the application of fast-growing woody plants in heavy metal phytoremediation. Full article

As water resources are becoming increasingly scarce in the North China Plain, irrigation strategies that simultaneously improve grain yield and reduce irrigation water input are needed for winter wheat (Triticum aestivum L.) production. Current irrigation decision rules are based either on fixed soil moisture thresholds or on evapotranspiration (ET)-based ratios applied uniformly across the growing season, limiting their flexibility for growth stage-specific irrigation management. In this study, a multi-objective simulation optimization framework was developed to jointly optimize soil moisture lower control limits (irrigation trigger thresholds) and evapotranspiration-based irrigation replenishment ratios across key winter wheat growth stages. The framework integrated the AquaCrop-OSPy crop model with the PyFAO56 soil moisture balance, irrigation scheduling model and the NSGA-II evolutionary optimization algorithm. A field experiment was conducted during the 2024–2025 growing season in Laoling City, Shandong Province, China, employing a four-dense–one-sparse strip cropping pattern with two irrigation treatments: T1 (subsurface sprinkler irrigation) and T2 (shallow subsurface drip irrigation). The AquaCrop-OSPy model was calibrated and validated using measured canopy cover, aboveground biomass, grain yield, and soil moisture content in the 0–60 cm soil layer. Simulated canopy cover and grain yield showed good agreement with observations, with the coefficient of determination (R²) ranging from 0.87 to 0.94. For grain yield, the normalized root mean square error (NRMSE) ranged from 2.24% to 3.75%, and the root mean square error (RMSE) ranged from 0.29 to 0.54 t·ha⁻¹. For aboveground biomass, R² was 0.99, while RMSE ranged from 1.02 to 1.11 t·ha⁻¹, and NRMSE ranged from 14.25% to 15.49%. The PyFAO56 irrigation strategy model simulated average root-zone soil-moisture dynamics with satisfactory accuracy, with an R² of 0.86 and an RMSE of 5%. Multi-objective optimization (maximizing yield while minimizing irrigation volume) generated 23 Pareto-optimal irrigation strategies, with irrigation volumes ranging from 51 to 128 mm, corresponding yields ranging from 9.8 to 10.8 t·ha⁻¹, and irrigation water use efficiency (IWUE) ranging from 0.08 to 0.19 t·ha⁻¹·mm⁻¹. Correlation analysis within the Pareto set indicated that soil-moisture control lower limits during the regreening–jointing stage and higher soil-moisture control lower limits during the flowering–maturity stage were key controlling factors for achieving high yields and irrigation water use efficiency. The Entropy-Weighted Ranked Minimum Distance method identified an optimal irrigation scheme involving two irrigations (one at the end of the jointing stage and another at the beginning of the grain filling stage) involving an irrigation depth of 75 mm, achieving a simulated yield of 10.4 t·ha⁻¹ and an IWUE of 0.16 t·ha⁻¹·mm⁻¹. The proposed AquaCrop-PyFAO56-NSGA-II framework provides a flexible, process-based workflow for jointly optimizing irrigation control thresholds and evapotranspiration-based irrigation replenishment ratios across different winter wheat growth stages. Under the monitored conditions of the 2024–2025 wet season, the framework identified a two-irrigation strategy that balanced grain yield and irrigation input. This study should, therefore, be regarded as a proof-of-concept evaluation conducted in a well-instrumented single-site field setting rather than as a universally transferable recommendation. Because model calibration, within-season validation, and optimization were all based on one wet growing season at one site, the derived stage-specific thresholds, Pareto front, and S5 recommendation are most applicable to hydro-climatic conditions similar to the study year and should be further tested across contrasting year-types and locations before broader extrapolation. Full article

Most cycad seeds germinate under the parent plant, and seedlings die before recruitment to the juvenile stage. Decomposition of the senesced organs releases the nutrients to influence nutrient cycling. The aim of this study was to quantify the soil nitrogen that accumulates from seedling turnover. Soil cores were collected beneath male and female trees of four Cycas species in five Philippine habitats from 2019 through 2025, with matching cores collected 5 m from the trees. Five to nine replications were employed depending on the habitat. One seedling was excavated beneath each tree in one location. Total nitrogen concentration was determined by dry combustion in soil and plant tissues, and total nitrogen content was calculated for seedlings. The soils beneath female trees contained more nitrogen than beneath male trees or away from cycad trees in every habitat. The highest nitrogen concentration within seedlings occurred in coralloid roots, but leaflets contained the most nitrogen pool, indicating rapid release of nitrogen during decomposition of the senesced seedling. No differences in rhizosphere nitrogen occurred in a 2017–2025 ex situ experiment using Cycas edentata, where seeds were sown beneath female and male trees. A second 2018–2025 experiment revealed that female trees provisioned with self-seeds did not differ in rhizosphere nitrogen compared with non-kin seeds. Nitrogen fixed by cyanobacteria endosymbionts of cycad seedlings and programmed seedling mortality combine to influence nitrogen cycling in soils beneath female trees over time. Full article

Background and Clinical Significance: Vascular lesions of the face, particularly arteriovenous malformations (AVM) and mixed hemangiomas (MH), pose significant diagnostic and therapeutic challenges because of their complex anatomy, unpredictable behavior, and high risk of bleeding. Surgical planning should be individualized and often requires a staged approach with meticulous interdisciplinary coordination to ensure patient safety. The presence of a concomitant odontogenic infection further complicates management, as local inflammation may exacerbate vascular instability and increase the risk of life-threatening complications. Local inflammation and infection might cause some life-threatening conditions, especially when an abscess occurs in the area of any vascular lesion. Ensuring that the oral cavity is free from potential odontogenic infections is a particularly important issue in many complex cases, especially in patients treated for oral, head, and neck cancer or in those with other coexisting morbidities affecting the oral and facial regions. Case Presentation: A 72-year-old man was referred for management of a severe odontogenic infection associated with an extensive facial vascular lesion. The patient’s medical history was significant for arterial hypertension and chronic liver dysfunction (CLD) of unclear etiology. Complete blood testing, including coagulation assessment and liver ultrasonography, was performed, with no contraindication to surgery identified. The scope of odontogenic-related infections was scheduled for simultaneous removal during initial surgery. Preparation for surgery included the local application of sclerotherapy agents. Conclusions: Quite often, a routine panoramic radiograph can help in assessing the status of bone and dentition to undertake all necessary treatment. Severe odontogenic disease, including multiple retained roots, periapical infections, and odontogenic cystic lesions in the context of poor oral hygiene, may lead to the occurrence of possible inflammation. In case of any vascular lesion, a careful diagnostic and therapeutic strategy is needed. This case report highlights that maintaining an infection-free oral environment is a critical component of care in patients with complex facial MH and should be regarded as an essential element of overall treatment planning. Full article

This research evaluated the efficacy of using two types of biochar (non-modified and acidified) from date palm residues (fronds, leaves, pits) as soil amendments for enhancing soil fertility and maize growth. These biochars were produced through slow pyrolysis under oxygen-limited conditions at 500 °C. Our innovative approach was to minimize gas emissions by converting smoke into liquid fertilizer (LS), which was expected to improve seed germination and early plant growth stages. To assess this aim, a completely randomized experiment was conducted under lab conditions, in which 10 maize seeds were placed on double filter papers in Petri dishes and then exposed to seven concentrations of LS (0.0, 0.01, 0.10, 1.0, 10 and 100%, using distilled water for dilution v/v). The LS contains nutrients and bioactive compounds that may enhance seed germination and early plant growth at low concentrations, whereas higher concentrations may cause phytotoxic effects. Results showed that liquefied smoke at 0.1% increased the absolute percentage of maize germination from 75% (control) to 100% and achieved the highest root length of 9.80 cm. Acidified biochars at 5% reduced soil pH from 8.87 to 8.12 and enhanced potassium availability to 87.93 mg kg⁻¹. Conversely, the non-modified biochars contributed to further increases in soil organic matter (up to 1.02%), nitrogen, and phosphorus. In addition, the application of acidified leaf biochar (5%) enhanced maize shoot growth by 133%, chlorophyll content by 39%, and potassium uptake by 110%. This research establishes a scalable approach for converting agricultural waste into climate-resilient resources, effectively addressing soil degradation in arid environments, boosting crop resilience, and furthering the objectives of a circular bioeconomy. Full article

The aim of this study was to investigate how delayed fertilization with individual macronutrients (N, P, K, Ca, Mg, and S) affects the growth, yield components, biomass, and spectrophotometric indicators of spring wheat grown under controlled hydroponic conditions. Nutrient deprivation was initiated at BBCH stage 23 and maintained for 21, 28, 35, or 133 days, corresponding to BBCH stages 30, 32, 37, and 99, respectively. In selected treatments, the complete nutrient solution was subsequently restored until harvest to evaluate recovery potential. N, P, and Ca deprivation exerted the strongest negative effects on biomass accumulation across all deprivation durations. Compared to the fully supplied control, biomass was reduced by 60% under N deprivation and by 44.5% under P deprivation after 21 days. After 35 days, calcium deprivation resulted in a 97.7% reduction in biomass. Following 133 days of deprivation, biomass was reduced by 98%, 96.8%, and 95.9% under N, calcium, and P deficiencies, respectively. Root mass followed a similar pattern: after 21 days, it decreased by 52.46% (N) and 36.44% (P); after 28 days—by 57.4% (N) and 52.7% (P); after 35 days—by 90.7% (Ca), 66% (N), and 59.1% (P); and after 133 days—by 95.1–90.1% (Ca, N, P). Magnesium deprivation caused substantial reductions in growth parameters, reflecting its central role in chlorophyll structure and photosynthetic efficiency. Sulfur deprivation resulted in moderate but consistent biomass suppression and spectral divergence, indicating its importance in protein synthesis and redox regulation. Short-term deficiencies allowed partial recovery of growth and productivity, whereas long-term deprivation induced pronounced morphological alterations linked to stress adaptation. These effects were further confirmed through in vivo spectral reflectance measurements compared to healthy control plants. Full article

Leaf Area Index (LAI) is critical for monitoring cassava growth and yield prediction, yet ground measurements are time-consuming and labor-intensive for large-scale applications. While satellite-based vegetation indices (VIs) offer a scalable alternative, their performance for cassava LAI estimation remains poorly documented, and optimal index selection for different growth stages is unclear. This study evaluated the predictive performance of 13 Sentinel-2-derived VIs for estimating ground-measured LAI across cassava growth stages. Ground-LAI was measured monthly using a SunScan Canopy Analyzer from January to June 2022 (2–7 months after planting; MAP) in 47 cassava plots in Nakhon Ratchasima Province, Thailand. Linear mixed-effects models and stage-specific regressions assessed VI predictive performance using Coefficient of determination (R²) and Root Mean Squared Error (RMSE). The Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Water Index (NDWI) demonstrated superior performance across all growth stages (R² = 0.524; RMSE = 0.350), followed by Sentinel-2 LAI Green Index (SeLI R² = 0.521, RMSE = 0.357). Stage-specific analysis revealed that Ratio Vegetation Index performed best during early growth (2 MAP, R² = 0.671; RMSE = 0.164) while GNDVI and NDWI excelled during mid-growth (3–5 MAP) and SeLI at late growth (7 MAP, R² = 0.393; RMSE = 0.422). While the presence of large trees altered the ranking of VI predictive performance, it did not substantially affect estimation errors, suggesting a relatively small impact of spatial heterogeneity on LAI estimation accuracy. These findings identify GNDVI and NDWI as the most operationally suitable Sentinel-2 indices for cassava LAI estimation and demonstrate that stage-specific index selection can improve monitoring accuracy, providing validated tools for regional-scale cassava crop monitoring using freely available satellite data. Full article

This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R²) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis. Full article

Solar greenhouses are indispensable infrastructure for winter vegetable production in high-latitude regions, maintaining favorable growing conditions without auxiliary heating through superior thermal insulation and solar radiation capture. However, extreme meteorological events disrupt this equilibrium, and soil temperature prediction remains challenging due to thermal hysteresis—time-lagged responses to environmental drivers that threaten crop viability during pre-dawn periods. Existing studies focus on air temperature or short-term horizons (3–6 h), leaving the critical 12 h prediction window inadequately addressed. This study develops a hybrid machine learning framework for 12 h soil temperature prediction in a solar greenhouse. High-resolution data were collected using soil temperature sensors at 200 mm depth (tomato root zone) during the winter growing season. Five algorithms were evaluated: RF and XGBoost (Bagging/Boosting for trend/residual capture), LSTM and GRU (temporal memory), and a novel RF-XGBoost hybrid with two-stage residual correction. The hybrid model achieved R² = 0.9927, improving standalone RF (R² = 0.9883) by 0.45% with MSE, RMSE, MAE reductions of 18.1%, 9.6%, 13.9%. Maximum 12 h error was 2.52 °C versus 3.12–4.60 °C for standalone models. The 12 h horizon enables preemptive heating activation, mitigating frost risk while avoiding unnecessary energy expenditure. Full article

Against the backdrop of accelerated terrestrial hydrological cycling and the increasing concurrence of drought-heatwave compound extremes under global warming, regional land-atmosphere coupling has emerged as a central mechanism shaping climate feedbacks and trajectories of ecosystem carbon uptake. However, prior studies spanning climatic regimes, observational scales, and data sources have often yielded contradictory conclusions. Here, we challenge these fragmented perspectives by constructing an integrated LST-SM-ET-GPP chain that jointly represents land surface temperature, soil moisture, evapotranspiration, and gross primary productivity, thereby linking water availability, surface energy balance, and plant physiological processes within a unified framework. We synthesize a conceptual diagnostic roadmap for interpreting land-atmosphere coupling across observations and models. When ecosystems operate in humid, energy-limited environments, radiative and advective controls should be prioritized to diagnose system forcing. By contrast, as the system becomes water-depleted, attribution must shift to a nonlinear regime transition framework governed by a critical soil moisture threshold. This threshold mechanism implies that, once the system enters the moisture-limited regime, even modest declines in soil moisture can trigger a rapid weakening of evaporative cooling, substantially amplifying LST anomalies and strongly suppressing GPP. The competitive regulation of stomatal conductance by atmospheric demand (vapor pressure deficit, VPD) and terrestrial supply (rootzone soil moisture) further explains why the “dominant” controlling factor can dynamically reverse across hydrothermal states, timescales, and stages of extreme-event evolution. Notably, the steady-state coupling assumption may break down under flux “flooring” during extreme drought, or when structural buffering such as deep root water uptake is present, delineating strict applicability bounds for existing diagnostic frameworks. Finally, current assessments remain constrained by multiple uncertainties, particularly the lack of ET partitioning constraints, representativeness biases arising from clear-sky observations and sampling-depth limitations, and systematic errors in Earth system model simulations during the warm season. Full article

Perennial groundcover (PGC) systems integrate perennial grasses with annual crops such as corn (Zea mays L.) to provide continuous soil cover and enhance soil health. However, the proximity to groundcover vegetation can alter light quality perceived by developing seedlings, inducing shade avoidance response (SAR), a phytochrome-mediated developmental response that modifies plant architecture and may compromise yield. Identifying the distance at which SAR is initiated and the extent to which management practices modulate this response is critical for optimizing PGC systems. This growth chamber study aimed to (1) identify the distance at which SAR occurs in corn seedlings, (2) determine whether the thiamethoxam seed treatment mitigates SAR expression, and (3) compare hybrid physiological responses to PGC-induced SAR. The experiment was arranged in a randomized complete block design with four replications across three periods and included two corn hybrids (P1185, P1197), two seed treatments (untreated and thiamethoxam at 0.25 mg seed⁻¹), and four perennial ryegrass (Lolium perenne L.) distances [0, 6, 25 cm, and a control (no-grass)]. Reduced red to far-red light ratios associated with closer proximity to ryegrass induced SAR responses. Corn plants at 6 cm from PGC exhibited significant stem and height elongation beginning at 8 days after planting (DAP), followed by reduced growth by 14 DAP, confirming an early SAR response. Plants grown at 0 cm exhibited reduced height and growth compared to other distances at all growth stages. Hybrid responses differed, and Hybrid P1197 showed enhanced stem elongation, a characteristic SAR response. The thiamethoxam seed treatment did not mitigate SAR. These results indicate that SAR causes stem elongation without altering root or shoot biomass. Full article