Search Results (39,518)

Background/Objectives: Current hemoglobin management in neurocritical care relies on static transfusion thresholds, which fail to capture the dynamic nature of hemoglobin changes during ICU care. We aimed to determine whether distinct longitudinal hemoglobin trajectory phenotypes exist among neurosurgical ICU patients and whether specific trajectory patterns independently predict unfavorable neurological outcomes. Methods: In this retrospective observational cohort study, we analyzed 8517 patients admitted to the neurosurgical ICU of a tertiary academic medical center between January 2015 and December 2024. A feature-based Gaussian mixture model was applied to trajectory-derived hemoglobin features over the first 14 ICU days to identify distinct hemoglobin trajectory phenotypes. The association between trajectory class and neurological outcomes was evaluated using propensity score matching. Results: Six distinct hemoglobin trajectory phenotypes were identified. The “Rapid Dropper” phenotype (Class 2; n = 351, 4.1%), characterized by the steepest decline velocity and highest variability, showed dramatically worse outcomes: 36.5% unfavorable neurological outcome (Glasgow Outcome Scale 1–3) versus 3.1% in all other classes combined (odds ratio [OR], 18.10; 95% confidence interval [CI], 14.08–23.27). This association persisted after propensity score matching (OR, 2.40; 95% CI, 1.77–3.26; p < 0.001). Hemorrhagic diagnoses were disproportionately concentrated in this high-risk phenotype. A combined prediction model incorporating trajectory-derived features within 72 h achieved an area under the receiver operating characteristic curve of 0.850 (95% CI, 0.829–0.870). This value reflects retrospective full-trajectory phenotyping; in a 72 h landmark analysis, early-feature prediction achieved an AUC of approximately 0.72. Conclusions: Hemoglobin trajectory phenotyping identified a high-risk “Rapid Dropper” subgroup that was significantly associated with worse short-term neurological outcomes. The rate of hemoglobin decline, rather than any single threshold, was associated with prognostic separation; prospective and external validation is required before these associations can inform transfusion strategy. Full article

Planktothrix species are among the most widespread bloom-forming cyanobacteria in freshwater ecosystems and are of particular concern because of their ability to produce cyanotoxins and form persistent harmful algal blooms (HABs). Among them, Planktothrix agardhii and Planktothrix rubescens are the most extensively studied species and are responsible for a large proportion of bloom events reported in European lakes. This review synthesizes current knowledge on the taxonomy, ecophysiology, toxin production, environmental drivers, species interactions, and management of Planktothrix blooms, with a particular focus on European freshwater ecosystems. The available evidence highlights marked ecological differences between the two dominant species. P. agardhii is primarily associated with shallow, eutrophic, and well-mixed lakes, whereas P. rubescens is typically found in deep, stratified, and relatively transparent water bodies, where it forms persistent metalimnetic populations. These contrasting ecological strategies influence bloom development, toxin dynamics, detection, and management. Nutrient availability, light climate, temperature, water column stability, and biological interactions all contribute to bloom establishment and persistence, while climate change is expected to further modify bloom frequency, duration, and geographic distribution. The review also examines current monitoring and mitigation approaches, highlighting the limitations of conventional surface-based surveys for detecting deep P. rubescens populations and emphasizing the need for integrated monitoring strategies combining depth-resolved sampling, molecular tools, and toxin analyses. Overall, understanding the ecological and physiological diversity of Planktothrix species is essential for improving risk assessment, developing effective management measures, and mitigating the impacts of cyanobacterial blooms in European freshwaters. Full article

Simulation optimization has been used to analyze construction operations and support planning decisions under uncertainty. It enables the identification of effective planning strategies throughout a project’s lifecycle. However, the use of stochastic simulation to evaluate alternative strategies results in higher computational demands and the generation of inferior solutions within the resulting optimal solutions. This study examines the feasibility of overcoming these issues by implementing variance reduction techniques into a discrete-event simulation optimization framework. Three variance reduction techniques are evaluated in a case study: Common Random Numbers, Antithetic Variates, and a combined application of both. While these techniques are well established in simulation, their impact on the optimization performance of construction problems has not been fully explored. The results show that VRT not only reduces the computational effort required to evaluate planning strategies but also provides better planning strategies. Among the evaluated techniques, the combined approach demonstrates the best improvements. Overall, the study highlights that variance reduction techniques can make simulation optimization frameworks more practical and reliable for complex construction projects. Full article

Ratoon rice cultivation is a significant practice for enhancing land productivity and food security. Ratooning ability is a key determinant of ratoon season crop (RC) yield and is influenced by genetic, agronomic, and hormonal factors. This study aimed to evaluate the effects of foliar-applied ratooning enhancers, formulated with plant hormones and botanical extracts, on the growth and regeneration of a japonica–indica hybrid rice cultivar, ‘Qingxiangyou 19 Xiang’. Treatments included gibberellin (GA), low, medium, and high concentrations of brassinosteroid (BR), each with or without jasmine extract (JE), alongside proline and zinc chloride (ZnCl₂) as supporting components. These solutions were applied twice at 5 and 15 days after flowering (DAF) of the main crop (MC). The results showed that GA treatment increased plant height and panicle length but reduced MC tiller number. BR treatments did not affect plant height but significantly increased the 1000-grain weight. Crucially, while BR alone had no significant effect on ratooning ability, the BR-JE combined application, particularly at medium (MBR-JE) and high (HBR-JE) concentrations, significantly increased ratoon tiller number and enhanced ratooning ability. However, the HBR-JE combination increased grain chalkiness. In conclusion, the foliar application of BR combined with JE during the flowering stage effectively promotes ratooning ability without compromising MC yield, offering a promising agronomic strategy for sustainable ratoon rice production. Full article

Accurate prediction of surface roughness and cutting forces in milling aluminum alloys remains challenging under data-scarce conditions, where limited experimental data restricts the application of conventional machine learning models. This study addresses this gap by developing a systematic machine learning framework using 108 milling experiments (repeated to 216 tests) on aluminum alloys AA2024-T351 and AA6061-T6. Five primary machining inputs—material type, spindle speed, feed rate, depth of cut, and tool coating—were used. Through feature engineering, 35 interaction features were generated to capture non-linear relationships. A two-step preprocessing strategy was applied: Winsorization at the 5th and 95th percentiles to handle outliers, followed by hybrid scaling combining RobustScaler and MinMaxScaler. Eight machine learning algorithms, including XGBoost, NGBoost, LightGBM, CatBoost, Random Forest, MLP, SVR, and Least Squares Boosting, were developed and hyperparameter-optimized using the Optuna framework with Tree-structured Parzen Estimator. Models were evaluated using R², MAE, and RMSE on a 70/15/15 train–validation–test split. Results demonstrate that XGBoost achieved the highest predictive accuracy for surface roughness (Ra) (R² = 0.99829) and for resultant cutting force (FN) (R² = 0.997). Feed rate was identified as the dominant machining parameter, accounting for 87.7% of the total importance in predicting surface roughness. SHAP analysis confirmed that engineered interaction features—particularly Feed_Coating and Material_Feed—carry strong physical relevance. Additionally, NGBoost enabled probabilistic regression, providing uncertainty estimates. The proposed framework proves highly effective for multi-output prediction in machining under limited data, offering a robust, interpretable, and industry-ready solution for quality control in aluminum alloy milling operations. Full article

Microalgae-based regulation is increasingly recognized as an eco-friendly strategy for improving water quality and nutrient management in intensive aquaculture systems. Although its effects on bacterial communities have been extensively investigated, its ecological impacts on higher trophic levels—particularly eukaryotic plankton communities across different size fractions—remain poorly understood. In this study, two indigenous microalgae species, Nannochloropsis oculata and Thalassiosira weissflogii, were inoculated into shrimp rearing water to elucidate the dynamics and interactions among microalgae, nutrient factors, and eukaryotic plankton communities across the small-sized (0.22–3 μm) and large-sized (>3 μm) fractions. The results revealed significant differences in the composition and diversity of both plankton size fractions under different microalgae treatments. Partial least squares path modeling indicated that microalgae influenced plankton communities both directly and indirectly through nutrient-mediated pathways. According to the neutral community model, microalgae inoculation was associated with an increased contribution of deterministic processes to community assembly. Variance partitioning further revealed that the large-sized community was primarily governed by microalgae, whereas the small-sized community was mainly shaped by rearing time, indicating size-dependent assembly mechanisms. The average variation degree and coefficient of variation, combined with effect-size analyses, indicated that N. oculata inoculation was associated with higher short-term community stability, an effect most pronounced in the large-sized fraction. Overall, these findings demonstrate that microalgal inoculation modulates the structure, assembly processes, and short-term stability of eukaryotic plankton communities, providing new insights into size-dependent, microalgae-driven assembly mechanisms and their potential to stabilize plankton communities for sustainable aquaculture management. Full article

Audio deepfake detection systems often achieve excellent internal validation performance but fail to generalize under real-world inference conditions involving synthetic speech generated with previously unseen AI tools. To address this limitation, this work proposes the Spectral Multi-Representation Fusion (SMRF) framework, which integrates multiple spectral representations and decision-level fusion strategies to improve robustness under cross-domain conditions. Additionally, a Stability-Aware Multi-Metric Selection (SAMMS) strategy is introduced to select architectures by jointly considering predictive performance and cross-representation stability. The proposed framework was evaluated using four spectral representations (log-magnitude spectrogram (LOG), Mel spectrogram (MEL), Discrete Wavelet Transform (DWT), and Constant-Q Transform (CQT)) combined with multiple convolutional architectures and complementary voting strategies. The experiments revealed that isolated models exhibiting validation metrics above 95% may still produce very poor synthetic-audio detection rates during external inference (even lower than 10%). In contrast, fusion-based strategies substantially improved robustness by exploiting complementary synthetic evidence across spectral domains. The results also demonstrated that both the voting strategy and the SAMMS stability parameter $\lambda$ strongly affect the final behavior of the system. In particular, hybrid fusion using One-Hard Voting with two architectures selected using $\lambda \ge 0.25$ achieved the best balance between synthetic-audio detection and real-audio preservation, outperforming individual models under cross-domain inference conditions, with detection rates close to 75% for both synthetic and real audio. These findings suggest that stability-aware fusion strategies constitute a promising direction for improving robustness in realistic audio deepfake detection scenarios. Full article

Background: Chronic atrophic gastritis (CAG) is a prevalent precancerous gastric disorder characterized by persistent inflammation, glandular atrophy, and progressive mucosal damage, for which effective multi-target therapeutic strategies remain insufficient. The Lian-Huo-Hua-Zhuo formula (LHHZ), a traditional Chinese herbal prescription, has demonstrated potential anti-inflammatory and gastrointestinal protective effects in clinical practice; however, its active constituents and mechanisms of action against CAG remain undefined. This study aimed to clarify the absorbed bioactive components of LHHZ and explore its therapeutic mechanism for CAG. Methods: Ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry was employed to identify the absorbed components of LHHZ in the gastric and intestinal tissues of mice. The therapeutic effects of LHHZ on CAG were assessed through histopathological staining, ultrastructural observation, and evaluation of serum and gastric functional indicators. Network pharmacology, molecular docking, and molecular dynamics simulations were integrated to predict the core targets and key signaling pathways, while the regulatory effects on the interleukin-17 (IL-17) signaling pathway were further validated by immunofluorescence staining, real-time quantitative polymerase chain reaction, and Western blotting. Additionally, 16S ribosomal RNA gene sequencing and targeted metabolomics were applied to investigate the effects of LHHZ on gut microbiota composition and short-chain fatty acid (SCFA) metabolism. Results: The results revealed that 55 and 48 absorbed components were identified in the gastric and intestinal tissues, respectively, predominantly derived from Coptis chinensis Franch. and Pogostemon cablin (Blanco) Benth. LHHZ significantly alleviated gastric mucosal lesions, reduced intestinal metaplasia, restored the ultrastructure of gastric mucosal cells, improved gastric functional indicators including pepsinogen I (PG I), pepsinogen II (PG II), and gastrin-17 (GAS-17), and decreased the levels of pro-inflammatory cytokines. Network pharmacology combined with in vitro and in vivo experiments demonstrated that the core bioactive components of LHHZ can target and regulate interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α), attenuate activation of the IL-17 signaling pathway, and suppress the secretion of downstream pro-inflammatory factors. Furthermore, LHHZ enhanced the alpha diversity of gut microbiota, reduced the Firmicutes to Bacteroidetes (F/B) ratio, restored the abundance of SCFA-producing bacteria such as Bacteroidales and Oscillospirales, and normalized the aberrant levels of eight SCFAs. Significant correlations were also observed between gut microbiota composition and SCFA metabolism. Conclusions: These findings suggest that LHHZ alleviates CAG by inhibiting inflammation via the IL-17 signaling pathway and by modulating the gut microbiota–SCFA axis, thereby providing preclinical evidence supporting its further investigation and development for multi-target therapeutic strategies against CAG. Full article

Unmanned aerial vehicles (UAVs) have been widely used in surveying and mapping, inspection, emergency rescue, and environmental monitoring. However, effective path planning remains a key challenge in complex three-dimensional terrain, where UAVs must simultaneously cope with terrain undulations, no-fly zones, safety-clearance requirements, and trajectory-smoothness constraints. In addition, conventional intelligent optimization algorithms often suffer from search instability and premature convergence. To address these challenges, this study proposes a reinforcement-learning-driven multi-strategy spherical-vector grey wolf optimizer, termed TLQ-SGWO, where TLQ denotes the combined use of Tent–Logistic hybrid initialization and Q-learning search-strategy scheduling. In the proposed method, candidate trajectories are encoded using spherical-vector increments; Tent–Logistic hybrid initialization is introduced to enhance population diversity; and Q-learning is incorporated to adaptively select search strategies, thereby dynamically balancing exploration and exploitation. A comprehensive cost function integrating path length, threat avoidance, terrain clearance, and trajectory smoothness is further constructed to improve the feasibility and safety of the planned trajectories. Experiments are conducted on the CEC2017 benchmark functions and artificially generated complex mountainous terrain scenarios. On the CEC2017 benchmark suite, TLQ-SGWO achieves the best average rankings in both mean error and standard deviation among the seven compared algorithms, indicating a stronger balance between optimization accuracy and robustness. In artificial mountainous scenarios, TLQ-SGWO obtains the lowest mean path cost in three of the four scenarios and remains statistically comparable to the strongest hybrid baseline in the remaining scenario, while maintaining stable feasible 3D trajectories under increasing no-fly-zone complexity. Full article

Biomass allocation patterns affect plant functions across all levels, ranging from plant growth and reproduction to the quality and energy flow of entire communities. Revealing the biomass allocation and allometric growth relationships among the dominant plant formations in alpine peat swamp wetlands not only can help elucidate the life history strategies of swamp plants, but also plays a crucial role in understanding the uncertainty of plant carbon sinks in peat swamp wetlands. Based on community surveys, this study employed analysis of variance (ANOVA) and standardized major axis estimation (SMA) to analyze the species composition, biomass allocation of different organs, and allometric growth relationships of the dominant plant formation in the alpine peat swamp wetlands of the Yellow River on the Gannon Plateau, Gansu Province, China. The results showed the following: (1) Peat swamp plants can be classified into six formations dominated by Carex muliensis, Blysmus sinocompressus, Carex atrofusca, Kobresia tibetica, Kobresia kansuensis, and Carex kansuensis. Environmental filtering was identified as the primary factor influencing the distribution of formations in this region. (2) The biomass allocation ratios of the dominant plant formations were ordered as follows: root mass ratio > leaf mass ratio > stem mass ratio. There were also significant differences in the biomass allocation of roots, stems, and leaves among different plant formations. (3) Isometric growth was observed between the leaf and stem biomass of the dominant plant formations (p > 0.05), while allometric growth relationships existed between root/leaf biomass and root/stem biomass (p < 0.05), with the growth rate of root biomass (RB) being higher than that of leaf biomass (LB) and stem biomass (SB). The biomass allocation patterns and allometric growth relationships among the roots, stems, and leaves of the dominant plant formations in peat swamp wetlands reflect the environmental plasticity mechanism of functional plant traits in heterogeneous habitats. Moreover, combining optimal allocation theory and allometric growth theory can better explain the biomass variation and adaptation mechanisms of dominant plant formations in peat swamp wetlands, providing a theoretical basis for understanding the habitat adaptation patterns of plants in alpine peat swamp wetlands. Full article

Detecting weak radar targets in complex sea conditions is inherently challenging due to non-stationary sea clutter and sea spikes. Furthermore, traditional dictionary learning algorithms for clutter suppression suffer from high computational complexity. To address these issues, this paper proposes an efficient sea clutter suppression method cascading Block Coordinate Descent (BCD)-accelerated dictionary learning with Tunable Q-factor Wavelet Transform (TQWT) denoising. During dictionary learning, a BCD strategy replaces global Singular Value Decomposition (SVD) with analytical optimization. Combined with an adaptive soft-thresholding operator, this enables low-complexity joint optimization of dictionary atoms and sparse coefficients, drastically reducing training time. Subsequently, a batch-adaptive Orthogonal Matching Pursuit (OMP) algorithm featuring Gram matrix precomputation and a dual-stop mechanism achieves efficient reconstruction and preliminary cancellation of clutter components. Finally, TQWT is applied to filter out residual non-stationary clutter and noise by leveraging its narrowband feature representation and shift invariance. Experiments on measured radar data from the IPIX database and datasets published by the Journal of Radars demonstrate that the proposed method significantly outperforms traditional K-SVD-based algorithms. Specifically, it improves the average signal-to-clutter-plus-noise ratio (SCNR) by 17.48 dB and requires a total execution time of only 7.99 s, achieving a highly favorable trade-off between suppression performance and computational efficiency. Full article

Global warming is associated with an increased frequency and intensity of heat waves, which severely threaten crop production and sustainable agriculture. As sessile organisms, plants evolved complex heat stress memory mechanisms to cope with recurring heat waves. Heat shock transcription factors (HSFs) are at the core of plant heat stress responses and memory. They regulate basal thermotolerance, acquired thermotolerance, and the maintenance of acquired thermotolerance. These processes involve multiple mechanisms, including temperature perception, activation of heat shock protein expression, and integration of hormonal and epigenetic signals. Here, we review the pivotal role HSFs play in the formation of heat stress memory, their structural characteristics, functional differentiation, and signal perception and transcriptional regulatory mechanisms. We further discuss the functional conservation and the diversity of HSFs across multiple species—for instance, HSFA2 acts as a conserved regulator of heat stress memory in Arabidopsis, tomato, wheat, and barley—and outline future research directions, including the functional characterization of heat shock transcription factor (HSF) subfamilies, investigation of their roles under stress combination, and strategies to balance stress tolerance with growth and development. We hope that our review will provide a theoretical foundation for the genetic improvement of crop thermotolerance as well as contribute to efforts directed at ensuring food security in the face of climate change. Full article

Water scarcity in Chile has been exacerbated by a decline in precipitation and an increase in water demand. This has prompted a search for strategies to increase water supply, whether through aquifer recharge or reservoir construction. In this study, aquifer recharge was evaluated through rainwater harvesting systems (RWHS) and direct injection into rural wells in the Ñuble Region. Three wells were selected in the Ñuble Region (Ñiquén, San Carlos, and Coihueco) using hydrogeological and operational criteria. To characterize the hydrogeology of the area, local piezometric data, geophysical surveys using electrical resistivity tomography (ERT), and seismoelectric tests were considered. This enabled the identification of aquifers with water levels between 2.6 and 23 m depth across the different geological units of the territory. The hydrological design was based on a frequency analysis of annual precipitation (1991–2020), which yielded design rainfall values between 442 and 694 mm. The implemented RWHS demonstrated injection capacities between 0.9 and 1.4 L·s⁻¹. The results show that rainwater harvesting combined with direct aquifer recharge represents a viable alternative for improving water security, with potential for territorial scaling through regional public policies. Full article

Background: Post-stroke spasticity is a frequent and disabling consequence of stroke, including when affecting the lower limbs, where it may impair stance, gait, balance, postural control, functional independence and quality of life. Botulinum toxin type A (BoNT-A) is widely used as a focal neuromodulatory treatment for post-stroke spasticity. However, the relationship between BoNT-A-induced reduction in muscle hypertonia, objective changes in spastic muscle’s biomechanical properties, and functional outcomes such as mobility and balance remains insufficiently clarified. This systematic review aimed to synthesize recent evidence regarding the non-invasive assessment of spastic muscle properties following BoNT-A administration in post-stroke patients, with emphasis on mobility and balance outcomes. Methods: A systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search was performed in international electronic databases and included studies published between 1 January 2023 and 31 December 2025. The search strategy used specific keywords and keyword combinations/syntaxes, contextually, related to the topic of interest. Results: A total of 32 studies met the eligibility criteria and were included in the final data analysis and synthesis, comprising 13 primary clinical studies—6 randomized or controlled interventional studies and 7 observational studies—together with 12 reviews or evidence syntheses, 3 technical or clinical framework papers, and 4 survey, epidemiological, health-services or health-economic studies. Overall, the included articles addressed BoNT-A treatment in post-stroke spasticity, with partial focus on muscle properties, gait, mobility, and functional outcomes. However, only a limited number of studies investigated objective non-invasive assessment methods, and few directly related muscle-property changes in balance and mobility outcomes. Formal risk-of-bias assessment and quantitative synthesis were not performed because of the substantial heterogeneity of the included evidence, with only two studies being potentially suitable for pooling and these addressing different muscle groups, interventions, and outcome domains. Discussion and Conclusions: The reviewed literature confirms the clinical relevance of BoNT-A in the management of post-stroke spasticity. However, most studies assess treatment effects mainly through clinical scales, while objective evaluation of muscle stiffness, elasticity, viscoelastic properties, and their relationship with mobility and balance remains limited. Although some studies address gait, functional recovery, or muscle-related changes, the combined use of BoNT-A treatment, myotonometric assessment, and proprioceptive–stabilometric evaluation is largely absent. Therefore, current evidence highlights an important research gap and supports the need for future longitudinal studies integrating non-invasive biomechanical and balance assessment tools to better monitor treatment response and guide individualized neurorehabilitation in post-stroke patients. Full article

Cytoreductive surgery remains one of the key treatment modalities in advanced ovarian cancer. Complete cytoreduction is the main surgical goal. PARP inhibitors are currently established mainly as maintenance therapy after response to platinum-based chemotherapy, particularly in patients with BRCA-mutated or homologous recombination-deficient tumors. Their use before cytoreductive surgery remains investigational. This review evaluates preoperative PARP inhibition from a surgical perspective. This narrative review summarizes current evidence, ongoing clinical trials, and perioperative considerations related to preoperative or neoadjuvant PARP inhibitor strategies in advanced ovarian cancer. Particular attention was given to the review of current clinical trials’ strategies, resectability, complete cytoreduction, patient selection, perioperative safety, treatment timing, and surgery-specific endpoints. Current studies explore several preoperative approaches, including short window-of-opportunity treatment before primary debulking surgery, PARP inhibitor monotherapy as potential conversion therapy in homologous recombination-deficient disease, PARP inhibitor-based strategies before interval debulking surgery, combination regimens with immunotherapy or antiangiogenic therapy, and preoperative PARP inhibitor use before secondary cytoreduction in recurrent disease. These studies suggest that preoperative PARP inhibition may provide biological and surgical insights, but available evidence remains preliminary. Key concerns include hematologic toxicity, surgical postponement, perioperative complications, wound healing, postoperative recovery, and the risk of delaying standard chemotherapy or surgery. Preoperative PARP inhibitor therapy is theoretically promising but an unproven strategy in ovarian cancer. Its future value will depend on prospective trials showing that it can safely improve resectability and complete cytoreduction without compromising treatment timing. Future studies should include surgery-specific endpoints in addition to conventional oncologic outcomes. Full article