Phytochrome A (phyA), the only far-red light (FRL) photoreceptor, initiates photomorphogenesis under FRL. Autophagy, an evolutionarily conserved degradation pathway, facilitates plant adaptation to nutrient stress. Recent studies revealed that elongated hypocotyl 5 (HY5) undergoes autophagic degradation during carbon and nitrogen starvation, a process antagonized by cryptochrome 1 (CRY1) through its binding to autophagy-related 8 (ATG8). The present study investigated how phyA engages with autophagy to mediate FRL signaling under nutrient starvation in Arabidopsis, a process whose mechanisms remain unclear. We combined protein-protein interaction, genetic, phenotypic, autophagic degradation, transcriptomic, and cellular localization assays to investigate this process. We demonstrate that autophagy-deficient mutants atg5, atg7, and atg8n exhibit enhanced photomorphogenesis under FRL. We further show that phyA physically interacts with ATG8 to suppress HY5 degradation via the autophagy pathway during combined FRL and nutrient starvation. Moreover, phyA restrains the nuclear export of ATG8e and inhibits autophagosome formation. Collectively, our results identify a phyA-ATG8-HY5 regulatory module that orchestrates photomorphogenesis under nutrient deficiency. These findings, together with earlier reports on CRY1, illustrate how distinct photoreceptors employ divergent strategies to converge on autophagy and fine-tune HY5 stability, thereby optimizing plant growth in fluctuating light and nutrient environments.
Pitahaya (Hylocereus spp.) is an emerging crop for tropical regions; however, its vegetative propagation is strongly influenced by environmental and management factors. Optimizing rooting and establishment under nursery conditions is essential to produce uniform and vigorous seedlings. This study evaluated the effects of root inducers and substrates on the vegetative propagation of three Hylocereus species under nursery conditions. A completely randomized design (CRD) with a 2 × 2 × 3 factorial arrangement was implemented, including two root inducers (NAA + IBA solution and seaweed extract), two substrates (substrate 1 and substrate 2), and three species (H. undatus, H. megalanthus, and H. costaricensis), resulting in 12 treatments with four replicates each. Seven variables were assessed: shoot emission time (SET), cutting viability (CV), number of shoots (NS), shoot length (SL), number of roots (NR), root length (RL), and vegetative vigor index (VVI). ANOVA revealed significant effects (p ≤ 0.05) in the three-factor interaction for SET, CV, NS, SL, RL, and VVI. Treatment T9 achieved the shortest SET (21.53 days) and highest CV (100%), T7 recorded the highest NS (1.62 shoots plant⁻¹), T3 showed greater SL (68.67 cm) and NR (5.30 roots plant⁻¹), and T12 obtained the highest RL (42.69 cm) and VVI (110.18). Effect size analysis based on partial eta squared (ηp²) identified species as the most influential factor, explaining 55-98% of the observed variance. Among the evaluated species, H. costaricensis showed the best overall performance. These findings demonstrate that combining H. costaricensis, substrate 1, and NAA + IBA enhances vegetative propagation efficiency. These findings provide valuable evidence for improving pitahaya propagation protocols and optimizing seedling production under nursery conditions.
We conducted a retrospective pilot feasibility study to evaluate whether tubeless mini-percutaneous nephrolithotomy (Mini-PCNL) performed under local anesthesia in the lateral position could be safely implemented in an ambulatory surgery setting for selected upper urinary tract calculi patients. Sixty patients who underwent Mini-PCNL at Lishui People's Hospital between 2021 and 2024 were divided into two groups based on treatment setting: 30 patients received local anesthesia in ambulatory surgery, while 30 received general anesthesia as inpatients. Groups were formed by patient and physician preference rather than randomization, introducing substantial selection bias. All procedures were completed successfully. The ambulatory group demonstrated shorter median operation time (57.9 versus 73.9 min, P < 0.001), shorter hospitalization (1.0 versus 7.4 days, P < 0.001), and lower costs (9041 versus 15186 yuan, P < 0.001) after Bonferroni correction for multiple comparisons. Stone clearance rates were similar between groups (93.3% versus 90.0%, P = 0.641). Secondary outcomes, which were exploratory and uncorrected, showed smaller increases in serum creatinine and urea nitrogen in the ambulatory group (P = 0.014 and P = 0.026), though these may represent Type I error. Anesthesia complications were less frequent (3.3% versus 20.0%, P = 0.017), but this comparison is clinically inappropriate as it includes complications specific to general anesthesia. Important limitations included the retrospective design, lack of randomization, exclusion of patients with hypertension or diabetes, single-center implementation, and potential for unmeasured confounding. While this integrated protocol appears feasible for highly selected patients, the profound selection bias precludes any conclusions about comparative effectiveness or safety. These preliminary findings require validation through prospective randomized controlled trials before clinical implementation can be recommended.
Gabapentin is effective for treating post-herpetic neuralgia and neuropathic pain by stabilizing nerve activity through blocking calcium channels and reducing neurotransmitter release. Gabapentin is available in immediate-release (IR) and extended-release (ER) formulations. A comparative bioavailability study was conducted between Gabapentin ER 600 mg tablets once-daily (OD) (Gabantin® GRS) [Test (T)] (manufactured by Sun Pharmaceuticals Industries Limited), and Gabapentin Tablets 600 mg OD (Gralise®) [Reference (R)] (distributed by Almatica Pharma LLC) in healthy human male adults under fed conditions. In this open-label, balanced, randomized, crossover study each subject received a 600 mg single dose of either T or R in Period 1, followed by crossover treatment in Period 2, with a washout period of 12 days in-between. Pharmacokinetic parameters, including Cmax, AUC0-t, and AUC0₋∞, were assessed. Safety was monitored through treatment-emergent adverse events (AEs). All 24 enrolled subjects completed the study. The test formulation demonstrated comparable pharmacokinetic profile to the reference product, meeting the criteria for bioequivalence within acceptable limits (0.80-1.25). The percentage ratio for T vs R product was 0.9171 (90% confidence interval [CI] 0.826-1.0183) for AUC0-t, 0.9191 (90%CI 0.8279-1.0203) for AUC0-∞ and 0.9135 (90%CI 0.8324-1.0026) for Cmax. Plasma concentration-time profiles were similar. One AE of fever was reported after administration of T; no serious adverse event was reported. Gabantin® GRS 600 Tablet ER OD of Sun Pharma had a similar pharmacokinetic profile and was bioequivalent to Gralise® in healthy subjects under fed conditions with good safety and tolerability profiles.
Measuring upper-limb movement using accessible motion capture (MoCap) technologies is of growing interest in rehabilitation and human motion analysis. While virtual reality (VR) systems offer an attractive alternative to traditional MoCap, their performance under realistic usage conditions remains insufficiently explored. In this pilot study, we provide a preliminary evaluation of the feasibility of the Meta Quest 2 as a motion tracking system by comparing wrist trajectories obtained from VR controllers with those measured using a Vicon optical MoCap system. Three healthy participants performed upper-limb movements under different head orientations, allowing the analysis of tracking performance when controllers were inside and outside the field of view (FOV) of the head-mounted display. Results show that trajectory similarity between systems is high when controllers remain within the FOV (correlation above [Formula: see text]) but decreases when controllers fall outside the visible area (down to [Formula: see text]). These findings highlight the strong dependence of inside-out VR tracking performance on FOV conditions. Overall, the Meta Quest 2 demonstrates feasible performance for capturing wrist trajectories during upper-limb movements, although tracking consistency is significantly affected by controller visibility. These preliminary results provide insight into the limitations of consumer VR systems for motion tracking in realistic scenarios and should be considered when applying such devices in applied contexts.
This study presents a pure multi-criteria decision-making framework to select circular packaging alternatives for an e-commerce shipping pack that includes the outer box, void filler, sealing tape, and return handling. Packaging decisions in e-commerce are inherently multi-objective: they must protect products and reduce damage, keep total cost low, avoid dimensional-weight penalties, fit recovery infrastructure, and lower life-cycle emissions. These choices are often made with linguistic assessments, uncertain return rates, and heterogeneous customer behaviors across markets. To address this setting, we integrate Spherical Fuzzy Step-wise Weight Assessment Ratio Analysis (SF-SWARA) for criteria weighting and Spherical Fuzzy Combined Compromise Solution (SF-CoCoSo) for alternative ranking. A criteria system is defined to reflect operational and circularity priorities, including protective performance, total cost of ownership, compatibility with local collection and recycling systems, consumer acceptance and convenience, hygiene and contamination risk in returns, feasibility of implementing reverse logistics, and carbon footprint. An expert panel expresses relative importance and alternative performance using spherical fuzzy linguistic terms, and SF-SWARA yields a normalized weight vector. Five alternatives are assessed under the same spherical fuzzy environment: a single-use recyclable pack, a reusable pack, a compostable pack, a deposit-return pack, and a minimal-pack design. SF-CoCoSo generates compromise scores by combining additive and multiplicative principles, enabling balanced choices under conflicting objectives. Robustness is examined through sensitivity analysis that perturbs key weights and by benchmarking rankings against an alternative spherical fuzzy method. The framework provides a transparent, replicable decision aid for e-retailers, third-party logistics providers, and packaging designers, supporting context-specific adoption of circular packaging while safeguarding fulfillment performance and customer experience.
To clarify the effects of different microbial fertilizers on the photosynthetic characteristics, enzyme activities, and yield of oil sunflower under combined saline-alkali stress, and to screen suitable microbial fertilizer types and application concentrations for oil sunflower cultivation in saline-alkali soils, this study used the salt-tolerant oil sunflower cultivar NX53177 and the salt-sensitive cultivar NKY1502 as experimental materials. A randomized block design was adopted, with three microbial fertilizers (Qiaosengen, polylactic acid, and Aikesha) each applied at three concentrations (low, medium, and high) along with a control treatment. Photosynthetic parameters, stress-resistance-related enzyme activities, osmotic adjustment substances, and grain yield were systematically determined at different growth stages of oil sunflower. The results showed that under combined saline-alkali stress, the application of microbial fertilizers significantly increased leaf SPAD values, net photosynthetic rate (Pn), and stomatal conductance (Gs), thereby enhancing photosynthetic efficiency. Meanwhile, proline (Pro) content, superoxide dismutase (SOD) activity, and peroxidase (POD) activity were increased, whereas malondialdehyde (MDA) content was decreased, effectively alleviating oxidative damage caused by saline-alkali stress and enhancing plant stress resistance. Among all treatments, the medium concentration of Aikesha microbial fertilizer (T8 and T17) exhibited the optimal effect. At the flowering stage, Pn of the two cultivars increased by 22.82% and 19.49%, respectively, compared with the control. At the grain-filling stage, MDA content decreased by approximately 21% compared with the control. Grain yield reached 4879.65 kg/hm² and 4709.59 kg/hm², representing yield increases of 26.20% and 22.17%, respectively, relative to the control. The overall performance of the three microbial fertilizers followed the order: Aikesha > polylactic acid > Qiaosengen, and the medium concentration treatments were generally superior to both the low and high concentrations. The results of this study provide a scientific basis and technical support for the rational application of microbial fertilizers in oil sunflower cultivation on saline-alkali soils.
Wheat is one of the staple foods, and its importance is considerable on a global scale. Drought stress significantly affects wheat seed germination. The absence of drought-resistant wheat varieties affects wheat cultivation, especially in arid regions of the world. This study examined seed germination and seedling growth of 147 wheat genotypes subjected to moisture stress induced by polyethylene glycol (PEG). All studied traits showed significant reduction in stress environment in comparison to control (PEG-0%), while MGT and RSR showed increase under stressful condition. The genotype (G), PEG-Treatments (TPEG), and G × TPEG(TPEG), and the G × TPEG interactions had significant effects on all studied traits (p < 0.01), implying considerable variation of genes among the RILs. PCA and cluster analysis revealed a good distinction between tolerant and susceptible genotypes. Elite RILs retained their high SVI, RL, and SHL even in severe osmotic stress conditions, which is a great opportunity for use of those lines in the development of drought tolerant varieties through breeding programs. Nevertheless, PEG osmotic stress is not similar to field drought conditions, and thus the results obtained are indicative of osmotic stress response only. The described genotypic variation represents a very good basis for further QTL and marker-assisted breeding work for improved drought resistance in bread wheat.
With increasing mining depth, the low permeability of coal seams has become a major constraint on efficient methane drainage and safe coal mining. To address this problem, this study focuses on three key issues related to hydraulic slotting/flushing in low-permeability coal seams. First, a dynamic porosity-permeability evolution equation for slotted coal is established. Second, the nonlinear relationship between permeability enhancement and coal removal volume is revealed. Third, borehole optimization criteria are proposed based on multi-physics coupling. A coupled model was developed to describe coal deformation, methane seepage, and pore structure evolution. The model links hydraulic slotting, permeability change, and the effective gas drainage radius. Field data from the Ji15-17-13070 working face of Pingmei No. 13 Mine were used for validation. Coal removal volumes of 0.5, 0.8, and 1.2 t/m were compared. The effective drainage radius was defined by a gas pressure reduction from 0.75 to 0.6 MPa. The results show that hydraulic slotting significantly improves coal permeability and methane drainage efficiency. At 0.8 t/m, the effective drainage radius reaches 4.02 m after 90 days, which is 1.56 times that of a conventional borehole. The drainage radius follows a decaying power-law growth trend with time, and its growth rate decreases markedly after 90 days. Increasing coal removal volume further enhances permeability, but the gain is nonlinear and gradually weakens. Based on the calibrated model, borehole spacing schemes were optimized for three-pattern and four-pattern layouts. Industrial tests show that the prediction error is less than 0.2 m. The model therefore provides a reliable tool for slotting parameter selection and borehole layout design. These findings support efficient methane drainage and borehole layout optimization in low-permeability coal seams.
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Non-contact injuries in professional football impose significant performance and economic burdens, yet the influence of workload feature representation on injury risk modelling remains insufficiently characterised. Traditional monitoring approaches, including the acute-to-chronic workload ratio (ACWR), may inadequately capture the temporal dynamics and instability of training load that underlie injury aetiology. This study systematically compared four complementary temporal feature engineering strategies-rolling workload aggregates, workload balance and exponential smoothing metrics, stability and stress indicators, and polynomial regression residuals-to evaluate their relative discriminative contribution to non-contact injury risk prediction in professional football. GPS-derived external load data from 69 professional male football players across two clubs were analysed over one full competitive season. A total of 23 non-contact injury events were recorded; under a 7-day pre-injury risk window labelling scheme, these generated 109 positive athlete-day observations across 10,134 total daily observations (1.08% positive prevalence). Decision Tree (DT), Random Forest (RF), and XGBoost models were evaluated using stratified group k-fold cross-validation with athlete-level grouping to prevent data leakage. Minimum redundancy-maximum relevance (mRMR) feature selection was applied independently within each fold. Model performance was assessed using Recall, F2-score, ROC-AUC, and Precision-Recall AUC (PR-AUC). Overall sensitivity remained limited across baseline configurations, reflecting the extreme class imbalance of injury data. Polynomial residual features, encoding deviations from expected workload trajectories, produced the most consistent gains in discriminative capacity across models (mean ΔROC-AUC + 0.078; largest absolute improvement: RF ΔROC-AUC =  + 0.131). Compact mRMR-selected subsets (34-42 variables) consistently outperformed full feature spaces. ACWR-based features degraded performance across all classifiers (mean ΔROC-AUC - 0.023). A supplementary optimisation analysis demonstrated that, under calibrated hyperparameters and SMOTE oversampling, RF achieved Recall = 0.667 and ROC-AUC = 0.676 on an independent held-out test set, confirming that the near-zero baseline Recall reflects deliberate methodological conservatism rather than fundamental feature inadequacy. Within this dataset, deviation-based workload representations provided greater discriminative value than traditional ratio-based indicators, suggesting that temporal instability and unexpected departures from established training patterns may carry more predictive information than absolute load magnitude. Given the limited sensitivity achieved under default model configurations, these findings should be interpreted as exploratory methodological evidence rather than a basis for immediate clinical deployment. Future work should integrate larger multi-season datasets, internal load markers, and prospective validation to improve clinical utility.
Capsular contracture, a prevalent complication following breast augmentation, is characterized by pain, implant distortion, and often necessitates explantation. While its multifactorial pathogenesis involves bacterial biofilms and implant characteristics, the influence of immune activation, particularly in the context of COVID-19 infection and vaccination, remains underexplored. Growing evidence suggests COVID-19 vaccination can trigger immune-mediated reactions, including delayed inflammatory responses to dermal fillers. Given that capsular contracture is a fibro-inflammatory response, this study investigated whether COVID-19-related immune activation influenced the time from breast implantation to explantation due to severe capsular contracture. This retrospective cohort study analyzed patients from a single private practice who underwent breast implant removal due to Baker grade III or IV capsular contracture. Two historical cohorts were compared: Cohort 1 (pre-pandemic: January 2016 - December 2019) and Cohort 2 (post-pandemic: January 2021 - December 2024).The primary outcome, time from implantation to explantation, was analyzed using Kaplan-Meier survival curves, log-rank tests, and a multivariate Cox proportional hazards model, adjusting for confounders. Ethical approval was obtained (CAAE: 35154720.2.0000.5330). A total of 115 patients were included (47 in Cohort 1, 68 in Cohort 2). Baseline characteristics were comparable between groups. The time from breast implantation to explantation due to Baker grade III/IV capsular contracture was significantly shorter in the post-pandemic cohort (4.9 ± 1.3 years) compared to the pre-pandemic cohort (8.2 ± 1.5 years, p < 0.001). The Kaplan-Meier estimated median time to explantation was 5.0 years (95% CI: 4.6-5.3) for the post-pandemic cohort versus 8.0 years (95% CI: 7.6-8.4) for the pre-pandemic cohort (p < 0.001). Multivariate Cox analysis showed a hazard ratio of 2.3 (95% CI: 1.5-3.5, p < 0.001) for earlier explantation in the post-pandemic cohort, independent of confounders. No significant differences were found in the proportion of Baker IV contractures or primary complaints. While this study does not establish causation, the observed reduction in the time to explantation in the post-pandemic cohort compared to the pre-pandemic cohort suggests that external factors, including potential immunological responses to the COVID-19 vaccine or infection, may have influenced the progression of capsular contracture. This information is relevant for clinical practice and has implications for patient management, informed consent, and medicolegal assessment of implant-related complications. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Regime shifts often have profoundly negative ramifications, underscoring the need to better understand processes that influence their reversibility. We explored the interplay between trophic and competitive interactions in shaping resilience of seaweed states following coral-to-macroalgae shifts on tropical reefs. Herbivore feeding and competition assays indicated that interactions involving common seaweed species were consistent with the competition-palatability trade-off paradigm: the most consumed alga (Amansia rhodantha) ranked as best competitor, the least consumed (Turbinaria ornata) ranked as the worst, and a third (Sargassum pacificum) was intermediate in both aspects. A two-year experiment revealed that mixed-species seaweed assemblages became overgrown by Amansia when herbivores were excluded, whereas Turbinaria was the only seaweed that persisted under high herbivory. Additional experimentation showed that much less herbivory was needed to extirpate palatable seaweeds relative to unpalatable Turbinaria. This relationship between seaweed resistance and palatability supports predications that coral-seaweed bistability becomes more likely when algae are less vulnerable to consumers. As such, the trade-off paradigm provided valuable insight into the nature and reversibility of coral-to-macroalgae shifts. Because such trade-offs are likely to be common in plant communities, these insights may well be applicable to regime shifts in a broad range of ecosystems.
Long-term time series forecasting (LTSF) underpins critical applications from energy management to weather prediction, yet achieving reliable multi-step-ahead accuracy remains challenging. Existing LTSF approaches, dominated by MLP- and Transformer-based architectures, either rely on simple linear mappings or introduce increasingly complex hand-crafted inductive biases, raising the question of whether a more expressive nonlinear modeling core could offer a useful alternative. In this work, we investigate whether Kolmogorov-Arnold Networks (KANs), which use learnable basis functions on network edges to model nonlinear relationships, can serve as effective modeling components for LTSF, and under which design choices they are most useful. Motivated by this question, we propose KANMixer, a compact KAN-centered architecture consisting of a multi-scale pooling frontend, KAN-based temporal mixing blocks, and KAN-based prediction heads. Unlike KAN-based forecasting models that combine KAN with decomposition-heavy or mixture-based pipelines, KANMixer is designed as a simple and controlled architecture for examining the role of KAN components in LTSF. Under a unified five-run reproduction protocol on seven standard benchmarks, KANMixer achieves competitive performance against representative LTSF baselines, especially on ETT-style datasets, while showing dataset-dependent limitations. Additional statistical tests, ablations, efficiency profiling, Gaussian-noise evaluation, and hyperparameter sensitivity analysis show that the practical value of KAN depends on basis-function choice, architectural placement, and computational constraints. These results suggest that KANs are promising but not plug-and-play components for LTSF, and that their benefits should be evaluated together with robustness and efficiency trade-offs.
Stalk rot and leaf spot, caused by Fusarium verticillioides and Curvularia lunata, respectively, are among the most destructive seed-borne fungal diseases of sorghum, resulting in substantial yield losses. In the present study, two novel endophytic strains, Trichoderma asperellum SEPA11A and Trichoderma harzianum SEPA11B, were isolated from healthy sorghum seeds and evaluated for their biocontrol potential and growth-promoting effects. Out of 39 Trichoderma isolates screened, the isolates 35 and 3 exhibited the highest antagonistic activity in dual culture assays, inhibiting the mycelial growth of F. verticillioides by 68.88% and 66.67%, and C. lunata by 57.77% and 59.30%, respectively. Both strains were identified based on morphological characteristics and internal transcribed spacer (ITS) rDNA sequencing and have been deposited in GenBank under accession numbers LC866760.1 and LC866759.1, and coded as SEPA11B and SEPA11A respectively. Gas chromatography-mass spectrometry (GC-MS) analysis of culture filtrates revealed 55 bioactive metabolites, while scanning electron microscopy demonstrated severe ultrastructural damage to pathogen hyphae. A granular formulation of SEPA11B and SEPA11A maintained high viability for up to 14 months under storage. These results collectively indicate that SEPA11B and SEPA11A represent promising, sustainable, and cost-effective alternatives to chemical fungicides for integrated management of stalk rot and leaf spot diseases, with the added as a potential benefit of enhancing sorghum growth and productivity. This unique trait makes seed-derived endophytic Trichoderma isolates promising for effective, persistent biofungicide development. Adapted to the seed-soil interface, they can colonize the rhizosphere, outcompete pathogens, and remain active under field conditions. Thus, they are strong candidates for sustainable biofungicides, pending greenhouse/field validation and large-scale production optimization.
Neuromorphic computing based on artificial synapses requires devices capable of gradual, repeatable, and energy-efficient conductance modulation. Ionically gated transistors are promising candidates because their ion dynamics naturally produce synaptic behavior under low-voltage operation. However, how key device characteristics-conductance range, number of accessible conductance states N, and weight-update nonlinearity β-jointly influence neural network performance remains insufficiently understood. Here, we investigate MoS2-based ionically gated synaptic transistors using a combined experimental and modeling framework that links device physics to hardware-aware artificial neural network (ANN) simulations across image-classification tasks of varying complexity. We show that under fixed-amplitude pulsing, increasing N introduces a fundamental trade-off: finer weight resolution is accompanied by stronger update nonlinearity. ANN simulations further reveal that, within the nonlinearity range studied here, classification accuracy is governed by a task-dependent optimal weight resolution rather than a simply maximized number of states. To overcome the nonlinear weight updates, we employ a physics-informed transient model to develop a predictive pulse-engineering algorithm and experimentally demonstrate that it can linearize synaptic weight evolution in the same device. These linearized updates improve ANN accuracy by 1.5%-5.2% for MNIST, 4.0%-5.2% for FMNIST, and 1.2%-12% for KMNIST across the tested state numbers, establishing a quantitative link between device-level dynamics and neural network performance in ionically gated synaptic transistors.
This single-center retrospective study developed and internally validated a two-dimensional deep learning model based on cone-beam computed tomography (CBCT) panoramic reconstruction images for the preliminary differentiation of operationally defined cystic and neoplastic jaw lesions from osteomyelitis of the jaw. A total of 400 patients were included, comprising 200 patients with jaw mass lesions and 200 patients with osteomyelitis. The jaw mass lesion cohort included cystic, benign neoplastic, and malignant neoplastic lesions and was analyzed as a single positive class for preliminary screening rather than lesion subtype classification. In this study, the term "jaw mass lesions" was used as an operational imaging-based umbrella term for jaw lesions presenting as clinically relevant expansile, destructive, or mass-like osseous abnormalities on CBCT panoramic reconstruction images. This term does not imply that all included lesions were tumors or soft-tissue masses; in particular, odontogenic keratocyst was classified as a cystic lesion. Images were randomly divided at the patient level into training, validation, and independent test sets in a 70:10:20 ratio. ResNet-50 was used as the baseline model and was compared with EfficientNet-B4, Swin Transformer, and ConvNeXt-Tiny under the same evaluation protocol. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPRC), accuracy, sensitivity, specificity, precision, and F1 score. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for post hoc visualization, and an exploratory reader study was conducted to preliminarily assess the potential influence of artificial intelligence (AI) assistance on image interpretation. In the independent test set, ResNet-50 achieved an AUC of 0.9781 and an AUPRC of 0.9714. The proposed model is intended as a preliminary screening and triage-support tool rather than a replacement for contrast-enhanced MDCT, magnetic resonance imaging (MRI), histopathological examination, or multidisciplinary assessment.
Drought represents one of the most pervasive and intensifying abiotic stresses under changing climate regimes severely constraining agricultural productivity, ecosystem stability, and global food security. Water deficit disrupts cellular homeostasis, reduces photosynthetic efficiency, and induces excessive accumulation of reactive oxygen species (ROS), resulting in oxidative damage. To survive under such conditions, plants employ a diverse array of adaptive responses, including osmotic adjustment, antioxidant defense, hormonal signalling, and stress-responsive gene regulation. Among the key signalling molecules involved in drought tolerance, nitric oxide (NO) and abscisic acid (ABA) have emerged as pivotal signalling molecules orchestrating a wide spectrum of physiological and molecular responses under drought. NO functions as a versatile signalling molecule that regulates redox homeostasis, enhances antioxidant activity, and promotes the accumulation of osmoprotectant. ABA maintains drought perception by inducing stomatal closure, and activating stress-responsive pathways. Co-application of NO and ABA regulates seed germination, root-shoot growth, and stomatal movement, thereby improving relative water content (RWC), membrane stability index (MSI), and photosynthetic efficiency while reducing oxidative stress markers such as malondialdehyde (MDA) and hydrogen peroxide (H2O2). This comprehensive review navigates through a clear and integrative overview of the mechanistic role of NO and ABA, and at the molecular level, NO and ABA modulate drought tolerance through transcriptional regulation, mRNA-level control, and translational modification of stress-responsive genes. Additionally, emerging strategies, including plant-growth promoting rhizobacteria (PGPR), marker-assisted selection (MAS) with QTL mapping, and genome editing tools such as CRISPR/Cas systems, offer promising approaches for enhancing drought tolerance and developing climate-resilient crop varieties.
Light is essential for photosynthesis and plant growth, and although physiological changes in response to light acclimation have been the subject of much research, the ecophysiology of Amazonian trees under light acclimation is still under investigation. The aim of this study was to evaluate how seedlings of andiroba (Carapa guianensis Aubl.) and Brazil nut (Bertholletia excelsa Bonpl.) acclimate to full sunlight. We exposed andiroba and Brazil nut seedlings to six full sunlight acclimation times for 30 days: 0 h (control kept under greenhouse), 1.5, 3.0, 6.0, 9.0 and 12.0 h. Then, the seedlings were transferred to an open area to receive 12 h (the whole day) of full sunlight for 120 days, for a total experimental period of 150 days. We evaluated 30 plant trait parameters, including biomass, leaf gas-exchange, leaf mass per area (LMA), stomatal density (StoD) and leaf nutrient concentrations. Light and CO2 saturated photosynthesis increased in sunlight-exposed seedlings. Biomass accumulation, LMA and StoD were responsive to sunlight acclimation, but the size of the effect varied between species. StoD slightly rose in Brazil nut, but it greatly increased in andiroba. Conversely, LMA moderately changed in andiroba, but it substantially rose in Brazil nut. Andiroba significantly increased biomass accumulation relative to Brazil nut, but the intrinsic water use efficiency was greater in the latter. This study highlights the remarkable ability of Brazil nut to increase LMA and WUEi, as well as the significant phenotypic plasticity of andiroba to increase stomatal density and biomass production in response to full sunlight acclimation.
This study introduces a novel, sustainable method for biodiesel production using bifunctional heterogeneous catalysts derived from black tea waste. Catalysts were synthesized via sulfonation with H2SO4 followed by alkaline treatment with NaOH and KOH to produce NaS.T and KS.T, respectively. Characterization showed enhanced surface areas of 59.7 m2/g (NaS.T) and 47.6 m2/g (KS.T), supporting improved catalytic activity. Transesterification of waste cooking oil using these catalysts achieved maximum biodiesel yields of 98.7% (NaS.T) and 97.8% (KS.T) under optimal conditions (3 wt% catalyst, 10:1 methanol/oil ratio, 40 °C, 45 min). Kinetic modeling confirmed pseudo-first-order behavior (R2 > 0.90), with activation energies of 11.71 kJ/mol (NaS.T) and 24.56 kJ/mol (KS.T)-significantly lower than conventional ranges-indicating a diffusion-controlled reaction. The higher pre-exponential factor for KS.T (741.5 min-1 vs. 106.5 min-1 for NaS.T) suggests a greater frequency of effective molecular collisions. Thermodynamic analysis confirmed the endothermic nature of the process (ΔH* = 15.01-22.04 kJ/mol) and negative entropy values (- 25.81 to - 41.9 J/mol·K), reflecting the formation of an ordered transition state. Gibbs free energy values (ΔG* = 27.29-30.12 kJ/mol) affirmed the feasibility and spontaneity of the reactions under studied conditions. Optimization using Response Surface Methodology yielded models with high predictive accuracy (R2 = 0.979 for KS.T; 0.944 for NaS.T), enabling predicted yields of up to 99.6%. These results demonstrate the potential of black tea waste-derived catalysts for low-cost, energy-efficient biodiesel production, aligning with sustainable fuel and waste valorization goals.