Search Results (37,696)

This study developed a physics-informed neural network (PINN) surrogate model for a one-dimensional synthetic groundwater contaminant-transport problem with adsorption, using Crank–Nicolson numerical solutions as the reference data. The effects of observation density and noise on predictive accuracy, training uncertainty, and parameter inversion were systematically evaluated. To address the limited attention given to the temporal coverage of physical information and the high training cost of PINNs, this study further examined how the temporal extent of physical constraints affects extrapolation and whether transfer learning can improve training efficiency within and across adsorption mechanisms. The results show that PINNs accurately predict concentration values and reduce initialization-induced uncertainty when the physical constraints—including the ADE residual and the prescribed initial and boundary conditions—cover the target prediction period (0.3 <$t_D < 0.6$ ), outperforming purely data-driven neural networks in both accuracy and stability. For example, at $t_D$ = 0.6, PINN-T6 achieved an $R^2$ of 0.990, whereas the DNN yielded an $R^2$ of −1.122. When the target period lies outside the physically constrained interval, however, prediction errors and uncertainty increase with the extrapolation horizon, and the long-term performance of PINNs may approach that of data-driven models. At $t_D$ = 0.6, the $R^2$ of PINN-T3 decreased to 0.377 because its physical constraints were imposed only up to $t_D$ = 0.3. Transfer learning accelerated early-stage convergence when the source and target tasks shared the same linear-adsorption formulation, although the advantage decreased as the training budget increased. Cross-mechanism transfer from linear to Freundlich adsorption provided only temporary early-stage benefits and eventually resulted in negative transfer, indicating that its effectiveness depends on physical similarity and the available training budget. Full article

Objective: On 7 October 2023, Hamas-led militants attacked Israeli communities bordering the Gaza Strip, abducting 251 people, including 201 civilians. Wolfson Medical Center was chosen by Israel’s Ministry of Health to care for repatriated civilians following a hostage release deal. We describe the development and implementation of a multidisciplinary care framework and report the clinical characteristics and short-term hospital outcomes of the repatriated civilians treated within it. Methods: A multidisciplinary framework was developed and implemented to address acute and chronic medical conditions, physical trauma, sexual assault, psychosocial support, and nutritional rehabilitation. Clinical characteristics and short-term hospital outcomes were retrospectively extracted from the electronic medical records. Results: Between 24 November and 1 December 2023, we treated 10 repatriated female hostages (median age 67 years, range 29–85 years) held captive for 48–55 days. They suffered physical trauma and loss of family members during the 7 October events. Captivity was associated with inadequate nutrition, poor hygiene, physical injuries, psychological distress, and insufficient management of chronic medical conditions. Acute conditions on arrival included rapid atrial fibrillation, heart failure, deep vein thrombosis, endocrine abnormalities, gastrointestinal symptoms and dermatologic conditions. Care required individualized planning, flexibility, and close coordination with national authorities, community health providers, and social services. Conclusions: Our experience highlights the need for comprehensive, coordinated care frameworks that not only address the medical, psychological, and social complexities of early recovery and reintegration but also ensure effective oversight and training of the medical teams involved. Full article

Ambrosia artemisiifolia L. (common ragweed, Asteraceae), native to North America, is a highly invasive weed causing substantial agricultural losses and serious human health problems due to its allergenic pollen. In several countries, legal regulations require its active management. The North American leaf beetle Ophraella communa (Coleoptera: Chrysomelidae) is a biological control agent that can suppress ragweed growth and pollen production. This study evaluated the effectiveness of a synthetic herbicide, a bioherbicide, mowing, and O. communa releases at different densities and timings, applied alone or in combination under field conditions. Three field experiments were conducted in a major European hotspot of A. artemisiifolia in the Pannonian region of Hungary during the 2024 and 2025 growing seasons. Both herbicides, as well as mowing, reduced A. artemisiifolia growth and, to some extent, flower head formation, but not flowering intensity. Ophraella communa established at low densities regardless of the number and timing of released beetles, and despite the hot and dry summer climate of the region. The highest abundance and most damage were observed following medium-density early-season releases (4 beetles per m²) and high-density mid-season releases (32 beetles per m²). The results suggest that repeated or more intensive management may be required for an effective season-long suppression of A. artemisiifolia, and that early, higher-density O. communa releases under different local climates may warrant further investigation. Full article

How sleep disturbances link to cognitive decline in Alzheimer’s disease (AD) via tau pathology is unclear. We explored whether sleep dysfunction worsens cognitive deficits by elevating plasma phosphorylated tau217 (p-Tau217). We combined Alzheimer’s Disease Neuroimaging Initiative (ADNI) human cohort mediation analysis with 6-week chronic sleep deprivation (SD) assays in P301L tau transgenic mice. Out of 5423 screened subjects, we included 230 participants with complete data. Clinical analyses revealed sleep disturbance independently linked to higher plasma p-Tau217 and worse cognition. Bootstrap mediation confirmed p-Tau217 partially mediated the sleep–cognition link, accounting for a 44.8% mediating effect. This pathway only existed in mild cognitive impairment (MCI) and AD patients. We also ruled out reverse causality. In mice, chronic SD triggered broad cognitive deficits without obvious stress elevation. Behavioral dysfunction was accompanied by hippocampal neuronal loss, glial overactivation, and widespread p-Tau217 hyperphosphorylation, whereas total tau showed only mild, sex-restricted upregulation. These animal observations corroborated our clinical results. In conclusion, we characterize a stage-specific correlational sleep-p-Tau217-cognition axis associated with early AD pathological changes. These findings provide preliminary translational clues linking sleep disruption to p-Tau217 dysregulation and subsequent cognitive impairment, laying a foundation for future exploration of sleep-targeted strategies for early AD intervention. Full article

Central nervous system (CNS) relapse remains a major cause of treatment failure in pediatric acute lymphoblastic leukemia (ALL). Repeated isolated CNS relapse is particularly rare and associated with poor prognosis, while optimal therapeutic strategies remain insufficiently defined. Intensified CNS-directed therapy may improve disease control but is also associated with substantial long-term neurotoxicity and survivorship burden. We present the case of a boy with favorable-risk B-cell ALL who developed two isolated CNS relapses despite a good initial response to frontline therapy and absence of classical CNS relapse risk factors. The second relapse was associated with extensive meningeal involvement and optic nerve infiltration. The patient underwent intensive multimodal CNS-directed therapy, including repeated intrathecal chemotherapy, liposomal cytarabine administered according to the IntReALL 2010 protocol, cranial irradiation, and allogeneic hematopoietic stem cell transplantation (alloHSCT) from a matched sibling donor. Durable long-term remission was achieved despite the extremely unfavorable prognosis that is associated with a second isolated CNS relapse. A twenty-year follow-up extending into early adulthood revealed substantial late complications, including epilepsy, transient ischemic attack, optic nerve injury, endocrinopathies, obesity, secondary thyroid malignancy, neurocognitive difficulties, and depression requiring long-term psychiatric and psychological support. The present case illustrates the complex balance between effective CNS disease control and cumulative treatment-related neurotoxicity in pediatric ALL survivors. It also highlights the cumulative CNS injury and long-term survivorship burden that is associated with repeated CNS relapse and multimodal CNS-directed therapy. Full article

White mustard (Sinapis alba L.) is a valuable crop for dryland agroecosystems, yet its productivity is strongly influenced by crop spatial configuration and hydrothermal variability, particularly under dry steppe conditions. Nevertheless, systematic evidence for optimizing cultivation parameters for white mustard in the dry steppe of northern Kazakhstan remains limited, while substantial interannual yield fluctuations continue to challenge production stability. This study evaluated the effects of seeding rate and sowing method on crop growth, biomass accumulation, and yield formation on southern carbonate chernozem soils. Field experiments were conducted during 2023–2025 using a two-factor design with three seeding rates (1.5, 2.5, and 3.5 million viable seeds ha⁻¹) and two sowing methods (25 cm row spacing and 50 cm wide-row spacing). Seed yield was significantly affected by seeding rate, with 2.5 million viable seeds ha⁻¹ producing the highest overall mean yield. Both lower and higher seeding rates were associated with reduced productivity, particularly under moisture-limited conditions. Sowing method also influenced yield, with row sowing (25 cm) generally producing higher yields than wide-row sowing. Yield declined progressively from 2023 to 2025, reflecting substantial interannual environmental variability, while the significant interaction (p < 0.05) among sowing method, seeding rate, and year indicated that treatment responses varied across growing seasons. Spearman’s correlation analysis indicated that seed yield was more closely associated with plant height at flowering and pod formation than with early-stage biomass accumulation. Although biomass variables were strongly interrelated across growth stages, their direct association with seed yield was limited, indicating that reproductive-stage plant development was more closely associated with yield variation under moisture-limited conditions. The combination of 2.5 million viable seeds ha⁻¹ and 25 cm row spacing produced the highest mean yield (558 kg ha⁻¹), representing a 51.2% increase in yield and a 476.9% increase in the stability index compared with the lowest-yielding treatment (1.5 million viable seeds ha⁻¹ with 50 cm wide-row spacing). This study provides field-based evidence of the combined effects of seeding rate and sowing method under contrasting hydrothermal conditions and identifies 2.5 million viable seeds ha⁻¹ combined with 25 cm row spacing as the most favorable crop spatial configuration for improving white mustard productivity under the dry steppe conditions in Northern Kazakhstan. Full article

Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide. Among its defining hallmarks, metabolic reprogramming, particularly aerobic glycolysis (the Warburg effect), emerged as a central driver of tumor progression, therapeutic resistance, and immune evasion. Over the past decades, substantial efforts have delineated the molecular architecture of metabolic reprogramming in GC, identifying key effector enzymes, including hexokinase 2 (HK2), pyruvate kinase M2 (PKM2), and lactate dehydrogenase A (LDHA), as well as upstream oncogenic signaling axes, such as the PI3K/AKT/mTOR pathway and hypoxia-inducible factor 1-alpha (HIF-1α), along with their interconnected regulatory networks. Despite extensive preclinical validation of these nodes, clinical translation remains elusive, with glycolysis-targeted monotherapies showing limited efficacy in early-phase trials. Yet, we contend that this persistent translational failure does not stem from invalid targets, but rather from a systemic underestimation of three fundamental roadblocks: (1) temporal metabolic plasticity that enables rapid compensatory adaptation and pathway switching; (2) spatial inter- and intra-tumoral metabolic heterogeneity that undermines uniform treatment strategies; and (3) a critical void in predictive and pharmacodynamic biomarkers essential for patient stratification and treatment monitoring. To overcome these barriers, we propose an integrated, forward-looking strategic framework that converges advanced diagnostics with next-generation therapeutic modalities. On the diagnostic front, we highlight spatial multi-omics for high-resolution metabolic cartography and artificial intelligence-driven integrative patient stratification to map heterogeneity and predict treatment response. On the therapeutic front, we examine strategies designed to circumvent metabolic plasticity, including dual-pathway inhibition, nodal targeting, exploitation of non-catalytic vulnerabilities, and tumor-penetrating nanocarriers for targeted metabolic intervention. Particular emphasis is placed on rational, mechanism-driven combination regimens, especially those synergizing glycolysis-targeted therapies with immunotherapy to remodel the suppressive tumor microenvironment—as well as hierarchical and parallel pathway combinations and the emerging metabolism–epigenetics axis, exemplified by lactate-mediated histone lactylation and α-ketoglutarate-dependent DNA demethylation. By shifting the therapeutic paradigm from static inhibition of single metabolic nodes toward dynamic, network-level intervention, this review provides a strategic roadmap for translating the vulnerabilities inherent in the glycolytic network into durable clinical benefit for patients with GC. This paradigm shift, we argue, is essential for advancing precision metabolic medicine beyond the current impasse. Full article

In roadside surveillance imagery, shadows, vegetation, vehicles, exposed pavement, and water stains may exhibit local textures and morphological characteristics similar to those of rockfalls, causing a standalone You Only Look Once (YOLO) real-time object detector to generate frequent false-positive detections. To address this issue, this study proposes a serial cascaded detection method that integrates an improved YOLO detector with machine-learning-based secondary verification. In the YOLO branch, channel-prior convolutional attention (CPCA) and learnable weighted multiscale feature fusion are introduced to enhance target representation under complex background conditions and generate candidate bounding boxes. In the machine-learning branch, handcrafted features describing texture, color, shape, edges, morphology, and frequency-domain characteristics are extracted from the candidate regions. A verifier selected through multi-model comparison and ensemble evaluation is then employed to confirm the YOLO-generated candidates. For parameter optimization, the operating point of the standalone YOLO detector with the highest $F_1$-score is first selected as the baseline. A two-dimensional grid search is subsequently performed over 95 threshold combinations consisting of five YOLO candidate-confidence thresholds and nineteen machine-learning confidence thresholds. The optimal configuration is determined using a weighted improvement score defined according to the relative changes in precision, recall, and the $F_1$-score with respect to the baseline. The best overall performance is achieved when the YOLO and machine-learning confidence thresholds are set to 0.25 and 0.75, respectively. Compared with the standalone YOLO detector, the proposed cascaded model improves accuracy from 93.3% to 94.1%, precision from 90.1% to 92.6%, and the $F_1$-score from 93.4% to 94.0%, while recall decreases slightly from 97.0% to 95.5%. These results demonstrate that interpretable local features can effectively filter out false-positive YOLO candidates, thereby suppressing false alarms and improving overall discrimination performance at the cost of only a limited reduction in recall. The developed system has been deployed on rockfall-prone sections of highways G210 and G108, providing technical support for real-time road rockfall monitoring and early warning. Full article

Lithium-ion batteries have become essential for electric vehicles and energy storage systems; however, safety risks related to thermal runaway have emerged as a critical concern. Conventional monitoring methods based on voltage, current, and temperature cannot detect internal faults at an early stage. To address this limitation, proactive diagnostic methods employing electrochemical impedance spectroscopy (EIS) have been extensively investigated. This study presents an excitation-current generation method for EIS measurement based on a variable-inductance bidirectional Ćuk converter. The proposed circuit operates using the energy stored in the battery system, eliminating the need for an external auxiliary power source for excitation energy, while generating sinusoidal excitation currents over a wide frequency range. A small-signal analysis incorporating a first-order battery equivalent circuit model and the parasitic elements of the Ćuk converter was conducted to evaluate system stability and the effects of key design parameters. The experimental results confirm sinusoidal excitation-current generation over the frequency range of 0.1 Hz to 3 kHz. Under a representative test condition, the prototype generates a sinusoidal excitation current with a 2 A peak-to-peak AC component superimposed on a 3 A DC component. These findings verify the feasibility of the proposed method as a dedicated excitation-current generator for embedded EIS measurements. Full article

Nutritional supplementation with pollen and protein-based patties is widely used in apiculture to support honey bee (Apis mellifera carnica) colonies during periods of limited natural forage, particularly in early spring and late summer. This study evaluated the effects of different supplemental diets on the survival, food intake, and gross energy content of caged adult worker bees of mixed ages under controlled laboratory conditions. Worker bees were fed four different diets: sucrose syrup (control), sugar patty, protein patty, and pollen patty. Survival, food intake, and gross energy content were assessed over an 18-day experimental period. Survival differed significantly among dietary treatments in both spring and summer experiments, with the highest final survival observed in bees receiving sugar patty (HBP). Aged-related differences in survival were treatment-dependent. Descriptive calorimetric data showed that bees receiving the pollen-enriched patty (HBP-PO) had the highest mean GE of bee body (22.05 ± 0.38 MJ/kg), with similarly high values observed in the 18-day old bees (22.28 ± 0.18 MJ/kg) and 20-day old bee workers (22.26 ± 0.23 MJ/kg). Notably, the GE content of the experimental diets did not correspond directly to the GE measured in bee bodies: the sucrose syrup had the highest dietary GE (16.56 ± 0.01 MJ/kg), whereas bees fed pollen-enriched patty showed the highest descriptive mean GE despite this diet having the lowest GE among the tested patties. These findings indicate that dietary caloric density alone may not predict the energetic status or survival of worker bees and suggest that diet composition and worker age contribute to different physiological responses to supplemental feeding. The experiment was conducted under controlled cage conditions; further studies at the colony level under apiary conditions are needed to determine whether these individual-level responses are maintained within functioning honey bee colonies. Full article

Accurate and reliable fruit detection in agricultural fields is crucial for applications such as yield estimation, crop tracking, and autonomous harvesting. However, the high density of small fruits in images, overlap, and complex vegetation can limit object detection performance. In this study, LemonDet, a parameter-efficient object detection model based on YOLO11n, is proposed to improve the detection of small lemons in particular. Considering the object scale distribution in the dataset, the standard P3–P5 detection structure is restructured as P2–P4 to ensure the preservation of high-resolution spatial features. In addition, the Lemon Color Prior Convolution (LCP-Conv) module, which transfers the dataset-specific RGB color prior obtained from labeled lemon regions in the training data to the early feature extraction process, has been developed. A label-guided local image enhancement approach, applied only to training images, has also been included in the model to strengthen the limited pixel representations of small objects. In the experimental results, LemonDet achieved 87.2% Precision, 81.6% Recall, 84.3% F1-score, 89.2% mAP50, and 54.8% mAP50-95. With 0.99 million parameters, the model provides a more parameter-efficient architecture than the baseline YOLO models while achieving higher detection performance. Ablation results show that color normalization and label-guided local enhancement contribute to performance. The findings indicate that considering object scale distribution and dataset-specific color information together in model design is an effective approach for detecting small lemons. Full article

Spinal cord injury (SCI) triggers a complex and evolving cascade of pathological events in which neuroinflammation and microenvironmental imbalance critically constrain repair. Among the cellular mediators, microglia exhibit remarkable functional plasticity, transitioning across diverse states that can either support tissue repair or exacerbate secondary damage. However, the mechanisms governing this dynamic reprogramming remain incompletely understood, limiting the development of effective immunomodulatory strategies. Recent evidence identifies the interleukin-33 (IL-33)/ST2 axis as an emerging, context-dependent regulator linking immune responses to neural repair. Rapidly released as an alarmin following injury, IL-33 modulates microglial function across spatiotemporal dimensions. Here, we propose a two-stage working model integrating direct evidence from SCI with extrapolated insights from broader central nervous system (CNS) pathologies. While acute IL-33 signaling is directly demonstrated in SCI models to limit early neuroinflammation and contain tissue damage, its putative role in driving a subsequent reparative microglial phenotype—encompassing metabolic adaptation and specialized phagocytic clearance—remains a hypothesis largely inferred from brain injury and neurodegenerative paradigms. Furthermore, we explore how IL-33 might modulate intercellular crosstalk with regulatory T cells and astrocytes, emphasizing that these downstream reparative mechanisms require definitive validation within the specific microenvironment of the injured spinal cord. In this Review, we synthesize current advances in the understanding of SCI pathology, microglial heterogeneity, and IL-33-mediated signaling. We highlight how IL-33 integrates inflammatory, metabolic, and transcriptional programs to drive microglial functional reprogramming, and we evaluate emerging therapeutic strategies targeting this pathway. Despite promising preclinical findings, challenges remain in optimizing delivery, timing, and safety. A deeper understanding of the IL-33/ST2 axis and its context-dependent effects may enable precise immunomodulation, offering new avenues to overcome barriers to regeneration and improve functional recovery after SCI. Full article

Cognitive impairment is increasingly recognized as a frequent and clinically significant complication of chronic kidney disease (CKD). Across the CKD spectrum, patients commonly exhibit a cognitive profile dominated by executive dysfunction and slowed processing speed, reflecting cumulative vascular, metabolic, and systemic burden. Cognitive impairment is not merely an epiphenomenon of aging but a modifier of clinical trajectory, with implications for treatment adherence, decision-making capacity, dialysis planning, transplantation, and mortality risk. This narrative review synthesizes current evidence regarding epidemiology, domain-specific cognitive patterns, risk modifiers, nutritional determinants, and clinical consequences across predialysis and dialysis populations. We discuss executive–attentional dysfunction and the interaction among kidney dysfunction, vascular burden, depressive symptoms, nutritional status, and cognitive vulnerability. Particular emphasis is placed on pragmatic cognitive assessment and potentially modifiable contributors, including anemia, nutritional deficiencies, dietary patterns, medication-related effects, and dialysis-related hemodynamic instability. Brief screening approaches combining global and executive-function tools may facilitate early identification of clinically meaningful impairment. Integrating cognitive and nutritional assessment into routine CKD care may improve risk stratification, individualized management, and clinical decision-making while identifying potential targets for prevention and intervention. Full article

Utilizing ammonia (NH₃) as a zero-carbon alternative fuel requires a deeper understanding of its combustion characteristics, with ignition delay time (IDT) serving as a critical descriptive parameter. To investigate the effects of propane (C₃H₈) addition on NH₃ ignition at high temperatures, the ignition delay times of NH₃/C₃H₈ blends were systematically measured using a shock tube over a wide range of conditions: temperatures of 1263–2117 K, pressures of 0.14–0.5 MPa, equivalence ratios of 0.5–2.0 and propane mole fractions of 5–70%. The results indicate that propane addition significantly promotes ammonia ignition. Under stoichiometric and 1.4 atm pressure conditions, the addition of C₃H₈ to pure NH₃ reduces the ignition delay time by approximately fivefold, allowing auto-ignition to occur at a lower temperature. Furthermore, detailed chemical kinetic mechanisms from the literature were adopted for numerical simulations and validated against the newly acquired experimental data. Kinetic analysis reveals that the propane addition does not alter the main oxidation pathways of ammonia, while it triggers C-N cross-reactions at higher blending ratios (>30%). The active radicals generated during the early stages of propane oxidation accelerate H-abstraction reactions, serving as the key mechanism responsible for the shortened ignition delay times and promoted ignition. Full article

Fertilizer source shapes soil nutrient dynamics in tomato systems, yet its effects across soil depths and crop season stages remain poorly understood. A two-year field experiment (2024–2025) was conducted on a Crider silt loam soil at Western Kentucky University Agriculture Research & Education Center to evaluate the effects of inorganic fertilizer (IF), organic fertilizer (OF), and an unfertilized control (CK) on soil nutrient concentrations across soil depths and sampling times, using a randomized complete block design with three replications. Soil pH declined under fertilization (IF 6.7, OF 6.6 vs. CK 7.2; p < 0.01). Nitrate-N showed a treatment × depth × sampling time interaction in both years (p ≤ 0.04); IF and OF raised early-season nitrate-N, but concentrations matched CK by harvest. Phosphorus at 0–5 cm was greater under IF and OF than CK in both years (p < 0.001), and surface potassium more than doubled under both fertilizer treatments in 2024. Conversely, IF reduced calcium and magnesium during late-season sampling in 2024, whereas fertilization increased sulfur, iron and zinc, primarily in surface layers. Overall, organic fertilization matched inorganic nutrient dynamics, demonstrating comparable nutrient synchrony and effective soil fertility management in tomato production systems. Full article