Real-time mechanical modeling of soft tissues using deep learning has long been a research hotspot in surgical simulation. Current studies predominantly focus on scenarios where soft tissues are subjected to external concentrated forces, which fail to align with the complex spatial displacement interaction strategies inherent to surgical simulations. Moreover, while deformation prediction has received significant attention, the indispensable role of multiple mechanical fields information in accurate surgical simulations is often overlooked. Achieving precise and synchronized modeling of multiple mechanical fields remains a major challenge due to the significant differences in their magnitudes. To address these issues, this paper presents a UNet-based surrogate model for real-time simultaneous prediction of soft tissue behavior under spatial displacement interactions, the Adaptive Weighted Parallel Attention UNet (AWPAUNet). The proposed model incorporates a parallel attention module to enhance feature extraction in mechanically sensitive regions and an adaptive weighting strategy to balance the joint learning of mechanical fields with different numerical scales. This effectively addresses the instability in synchronized modeling of multiple mechanical fields caused by larger-scale mechanical fields dominating the training process. Extensive experiments across multiple modeling objects demonstrate that AWPAUNet is an efficient and accurate surrogate for real-time synchronized modeling of multiple mechanical fields of soft tissues. It may support surgical simulation and related medical-assistance applications by enabling real-time, accurate prediction of deformation, stress, and reaction force information.
Visual field testing is essential for monitoring field defects, but traditional devices are bulky and resource intensive. This study evaluated the agreement and usability of the RetinaLogik RVF100 virtual reality perimetry device compared with the Humphrey visual field analyzer (HVF) among Filipino adults. A comparative cross-sectional study. Participants were Filipino adults presenting to 2 major eye centres in the Philippines from January to October 2024. A total of 46 participants (76 eyes) were included. Participants were categorized as normal, glaucoma, or other diagnoses (e.g., optic neuritis, ocular hypertension). Both devices tested visual fields using the 30-2 grid, measuring mean deviation (MD), pattern standard deviation (PSD), fixation losses (FL), false positives (FP), false negatives, and test duration. Agreement was assessed using Bland-Altman analysis and Pearson correlation. Pointwise analyses with heatmap visualizations were also used. Usability was evaluated using a postexamination Likert-scale questionnaire. RVF100 demonstrated strong agreement with HVF (MD: r = 0.979, PSD: r = 0.837; p < .0001). The Bland-Altman analysis showed a mean difference in sensitivity of -1.06 decibels (dB) (95% CI: -4.2 to 2 dB). RVF100 had shorter test durations (5.41 vs 6.96 min; p < 0.001), fewer FL (1.79% vs 5.59%; p < 0.001), and slightly higher FP rates (3.44% vs 1.92%; p < 0.001). Usability results showed 90% preferred RVF100 over HVF for comfort (86.4%) and engagement (95.3%). RVF100 is a comparable alternative to HVF, offering comparable accuracy with improved patient comfort. Further research is warranted to assess its efficacy in detecting early and advanced disease stages and in broader populations.
The Fusarium graminearum species complex (FGSC) causes multiple cereal diseases. This study aimed to investigate the specific effects of different cropping systems on the population composition and trichothecene chemotypes of the FGSC under the same climatic conditions within a single region. From 2013 to 2023, this study performed isolation and identification of the FGSC population in adjacent wheat-maize and wheat-rice rotation fields in Kaifeng, Henan, China, and 1,586 FGSC isolates were obtained. The results showed that the 15-acetyldeoxynivalenol genotype of F. graminearum sensu stricto (F. graminearum-15ADON) was predominant in both rotation fields (93.6%), followed by the 3ADON genotype of F. asiaticum (F. asiaticum-3ADON) (5.7%). There were significant differences in the isolation frequencies of the two species from Fusarium Head Blight (FHB) samples. The isolation frequency of F. asiaticum-3ADON was the highest in wheat, followed by that in rice and rice residues; no F. asiaticum isolates were obtained from maize and maize residues. Rotation systems, isolation source and annual climatic conditions all have a significant effect on isolation frequencies of F. graminearum-15ADON and F. asiaticum-3ADON. Cross-pathogenicity tests showed that F. graminearum-15ADON was more pathogenic to wheat and maize than F. asiaticum-3ADON. Interspecific competition assays revealed that the pathogenicity following mixed inoculation with the two species was lower than that of F. graminearum-15ADON alone. Meanwhile, F. graminearum-15ADON displayed strong competitive dominance in hosts, with its re-isolation frequency consistently exceeding 98%. This likely explains the dominance of F. graminearum-15ADON in the two rotation fields.
The large 2025 Mpox clade IIb outbreak in Sierra Leone underscores the urgent need for portable, low-cost diagnostics in decentralized settings. While CRISPR-based assays offer high sensitivity and flexibility, their deployment during active outbreaks remains limited. Here we show the rapid development and field evaluation of Mpox SHINE, a CRISPR-Cas13 assay that integrates lyophilized reagents, ambient-temperature lysis, and automated fluorescence detection on the portable DxHub device. The assay achieves analytical sensitivity down to 10 copies/µL. Clinical validation in Sierra Leone, using 56 clinical specimens, confirms complete concordance with qPCR, demonstrating 100% sensitivity and 100% specificity. Crucially, Mpox SHINE also detects the virus directly from unextracted lesion swabs while maintaining 100% sensitivity and specificity. The mean time-to-result is fast, averaging 11.4 minutes for extracted samples and 27.9 minutes for unextracted samples. These findings demonstrate that CRISPR-based diagnostics translate quickly from genomic sequence to clinically validated, deployable tools within a single outbreak window.
Adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD) aims to improve treatment efficacy by adjusting stimulation amplitude based on a neurophysiological feedback signal reflecting the patient's clinical state. Although beta band (± 13-35 Hz) spectral power from local field potential (LFP) recordings is the best-characterized physiomarker to-date, correlations with motor symptom severity remain modest. To explore whether combining spectral information from multiple frequencies improves explained variance in motor symptom severity, we applied canonical correlation analysis (CCA) to the full power spectral density (1-100 Hz) of LFP recordings from 67 patients with PD undergoing subthalamic nucleus DBS, alongside clustered and individual items of the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (UPDRS-III). CCA redundancy indices quantified that over 20% of variance in UPDRS-III scores was explained by a linear combination of spectral power across frequencies, compared to ~10% by beta power alone. Beta frequencies contributed most strongly and positively to total UPDRS-III scores, with slightly stronger contributions of low-beta (~13-25 Hz) than high-beta (~26-35 Hz) ranges. Low-frequency (<10 Hz) and finely-tuned gamma activity at half the stimulation frequency (62-63 Hz) contributed negatively. Symptom-specific analyses revealed a clear double beta peak associated with bradykinesia, while rigidity was primarily associated with low-beta power. Tremor showed a less distinctive spectral pattern. CCA results were highly similar for contralateral and ipsilateral symptoms. Overall, CCA findings revealed both shared and symptom-specific spectral patterns underlying PD motor symptoms and demonstrate the added value of a broader spectral physiomarker.
Rice paddies are a major source of agricultural methane (CH4) and nitrous oxide (N2O) emissions, making the optimization of straw return practices and water management critical for greenhouse gas mitigation. A two-year field experiment (2021-2022) evaluated the combined effects of three wheat-straw return modes (no return, S0; mulching, S1; incorporation, S2) and two irrigation regimes (continuous flooding, W1; alternate wetting and drying, W2) on CH4 and N2O emissions, heading-stage soil organic carbon (SOC) and dissolved organic carbon (DOC), methanogenic archaeal communities, and rice grain yield. The CH4-mitigating effect of W2 was strongly contingent on straw return mode. The S1W2 treatment achieved the lowest cumulative CH4 emissions and global warming potential (GWP), reducing cumulative CH4 and GWP by 44.9% and 39.6% relative to S0W2, and attained the lowest greenhouse gas intensity (GHGI) at 0.20 kg CO2-eq kg-1 grain yield without compromising rice yield. By contrast, S2W2 generated the highest CH4 and N2O emissions, with cumulative CH4 under S2W2 exceeding that under S2W1 by 2.64-fold in 2021 and 1.37-fold in 2022. Methanogenic archaeal communities were dominated by Methanobacterium and Methanosphaera. Under W2, α-diversity and community composition shifted markedly among treatments; however, these compositional changes were not directly proportional to CH4 fluxes, indicating that taxonomic patterns cannot be equated with methanogenic functional activity. N2O emissions were primarily associated with water management, with straw return mode playing a comparatively limited role. Overall, S1W2 offers a promising agronomic pathway toward lower rice-season paddy greenhouse gas intensity, and these outcomes were associated with differences in carbon availability and soil redox conditions across treatments.
Police administer live showup procedures when a suspect is located shortly after a crime, yet most prior research has relied on static, image-based showups, where confidence is often a weaker predictor of accuracy than in lineup procedures. Because actual police showup procedures involve live, in-person identifications, we conducted two studies to test the reliability of eyewitness confidence in live showups. In Study 1, a laboratory experiment (N = 229), identifications made with both high confidence and fast response time were more likely to be correct, and high-confidence rejections indicated suspect innocence. In Study 2 (N = 153), a field study using showup cases from two US jurisdictions, eyewitness confidence again indicated accuracy, assessed with the Strength of Evidence Scale as a proxy for ground truth. These findings demonstrate that eyewitness confidence is an indicator of accuracy in live showups, with implications for law enforcement policy and safeguards against wrongful convictions. When administered with procedures to reduce bias, live showups can yield reliable and probative identification, supporting their continued use in police investigations.
Real-world outcomes on catheter ablation of atrial fibrillation (AF) using the circular multielectrode (PulseSelect, Medtronic) pulsed-field ablation (PFA) system remain sparse. This study evaluated the safety, feasibility, and efficacy of pulmonary vein isolation (PVI) with/without posterior wall isolation (PWI) using the circular multielectrode PFA system. We retrospectively analyzed the outcomes of consecutive patients with symptomatic paroxysmal/persistent AF, who underwent catheter ablation using the circular multielectrode PFA catheter. Patients underwent PVI or PVI+PWI at operators' discretion. Procedural outcomes, safety events, renal biomarkers, and atrial arrythmia recurrence were assessed. Altogether, 178 patients (age: 69±11 years; 62% male; 61% paroxysmal AF) underwent AF ablation using the circular multielectrode PFA catheter between 2/2024 and 8/2024 using 62±17 PFA application (procedure time: 66±11 min, fluoroscopy time: 14±5 min). Acute PVI was achieved in 100% (n=178), and 96.6% of patients (n=172) received PVI+PWI. Four (2.2%) adverse events (2 minor) were encountered. Analysis of biomarkers demonstrated an increase in serum total bilirubin (1.01mg/dL; 95% CI: 0.84, 1.17, P<0.001) 2 h post-ablation and stage 1 acute kidney injury in one patient with a history of chronic kidney disease. All changes in biomarkers resolved spontaneously within 48-72h without clinical sequelae. Freedom from recurrent atrial arrythmia was 81.6% in paroxysmal and 66.6% in persistent AF patients at 1 year. These real-world results demonstrate that PVI with/without PWI using the circular multielectrode array PFA catheter is safe, feasible, and effective in patients with symptomatic paroxysmal and persistent AF with a low risk of complications, including hemolysis.
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Multi-rotor plant protection drones are extensively utilized in rice field operations. However, the airflow generated by the rotors not only disturbs the rice canopy but also affects the deposition of pesticide droplets. The investigation of the impact of rotor wind fields on the protection of rice crops has the potential to enhance the effectiveness of operations of this nature. The present study firstly acquired aerial imagery of plant protection drone operations via the utilization of aerial photography, and subsequently employed machine vision technology to investigate the disturbance patterns of rotor wind fields on rice canopies. Notably, there is currently no effective experimental method to observe the disturbance of rice canopies caused by the wind field generated by plant protection drone rotors, and machine vision processing is proven to be an effective approach, which is a key innovation of this study. This study innovatively adopts an integrated experimental-numerical approach, a distinct advancement over existing Unmanned Aerial Vehicle (UAV) spraying and airflow research that typically focuses on single experimental or simulation methods. Aerial photography and machine vision were used to explore canopy disturbance patterns, while reverse engineering combined with thrust tests and numerical simulations verified rotor model precision and analyzed droplet deposition. Core findings show that flight speeds of 3-4 m/s enable effective overlap between canopy disturbance and droplet deposition zones, achieving optimal plant protection effects; higher speeds cause droplet drift and zone misalignment, reducing efficacy. In addition, this study explores the spray deposition of plant protection drones based on numerical simulation methods, and assists in judging the effect of plant protection operations through the coincidence of droplet deposition and canopy disturbance zones. This integrated approach and related findings provide a reliable technical basis for optimizing UAV flight parameters, and have certain reference significance for the technical research and development of plant protection drones and the setting of operation parameters.
A compact multi-layer silicon beta-ray spectrometer (SBS) has been developed for beta spectrometry and dosimetry in the beta-gamma mixed fields at Canada Deuterium Uranium (CANDU) nuclear plants. Accurate determination of beta fluence spectra from SBS pulse-height data is challenging due to partial energy deposition within each detector. In this work, we present a simulation-based application of Fully Bayesian Unfolding (FBU) to SBS, leveraging Monte Carlo-derived response matrices under anti-coincidence/coincidence conditions. The FBU approach, implemented in Python using PyMC, provides full posterior distributions and credible intervals for unfolded spectra. Performance was evaluated using Geant4 simulations for beta-only and beta-gamma mixed fields with beta-to-gamma source particle ratios from 1 to 0.01. For ratios ⩾ 0.1, the mean unfolded-to-truth ratio for beta fluence above 0.1 MeV was within 20% of unity. The results demonstrate promising fluence unfolding for the SBS, enabling reliable beta fluence spectrum measurements in the beta-gamma mixed radiation fields while providing valuable insights into the gamma component of the field, even at low count rates and low beta-to-gamma ratios. These results demonstrate the feasibility of FBU for SBS, enabling improved beta-gamma discrimination and uncertainty quantification. Future work will focus on optimization of the algorithm to improve the accuracy and efficiency to achieve real-time unfolding for practical measurements.
Rapid urbanization has heightened the need for evidence-based healthy city planning. To resolve the methodological limitations of parallel biometric data stacking, this study developed a synchronized cross-modal framework to evaluate the restorative potential of a 9-typology urban-to-natural landscape continuum. A laboratory experiment was conducted with 42 healthy undergraduate students (21 males, 21 females; mean age = 21.4 ± 1.8 years). Brain activity (EEG), visual attention (eye-tracking), and peripheral autonomic signals (EDA, HRV, respiration) were synchronously recorded alongside the Profile of Mood States (POMS) scale. To integrate these multi-scale data streams, we formulated the Cross-Modal Restorative Index (CMRI). The empirical findings reveal a distinct, non-linear hierarchy of environmental restoration. Pristine natural environments, especially Mountainous and Field landscapes, elicited complete "Integrated Restoration," characterized by significant systemic convergence: central cognitive relaxation via posterior α power activation (Field: 7.35; Water: 7.03), robust parasympathetic upregulation (Field HF: 12518.77), and profound down-regulations in subjective tension (Mountainous: 18.6 → 12.3) and fatigue. Conversely, built landscapes demonstrated "Fragmented Restoration." Notably, Road scenes exhibited a localized dissociation where physiological calming (sharp increase in posterior α wave SD from 15.11 to 20.54) was decoupled from visual and psychological domains, with over 74% of visual dwell time remaining locked on artificial elements and subjective fatigue rising (15.3 → 16.2). These findings provide quantitative, systems-level evidence for integrating ecologically authentic blue-green infrastructure into resilient urban design.
This study aimed to measure magnetically induced displacement forces on metallic dental appliances and instruments in a 3 T MRI scanner and to examine its relationship with magnetic flux density. Magnetic flux density was measured along the gantry centreline, and both the spatial field gradient and the product of magnetic flux density and spatial field gradient were calculated. Displacement force measurements were conducted according to ASTM F2052-21. Tests were performed on a representative material along the gantry centreline and on 29 metallic dental materials-including fixed and removable dental appliances, and dental and medical instruments-at two positions outside the gantry entrance. Displacement force increased rapidly near the scanner entrance, reflecting trends in spatial field gradient and the product of magnetic flux density and gradient, with a maximum observed between 0 and 5 cm inside the gantry entrance. At 25 cm outside the gantry, all tested single fixed and removable dental appliances exhibited displacement forces below 0.03 N. In contrast, nearly all stainless steel dental and medical instruments demonstrated deflection angles exceeding 90° at the same position. In the MRI environment, dental appliances composed of stainless steel or iron that are not securely fixed in the patient's oral cavity are subject to magnetically induced displacement forces. Therefore, assessing intraoral fixation is essential before and after MRI. Additionally, most stainless steel dental and medical instruments, if inadvertently brought into the MRI environment, pose significant safety risks and must be strictly excluded.
In narrow conductors, electron-electron collisions can create a viscous fluid state, allowing conductivity beyond ballistic transport into the superballistic regime. Point contacts made from ultrahigh-mobility two-dimensional electron gas serve as a platform to study this effect. Under microwave irradiation, edge magnetoplasmons in point contacts are excited and strongly influence electron dynamics. This study uses photoconductivity signals - changes from microwave exposure - to investigate superballistic electron flow, confirmed by size-dependent photoconductivity, magnetic field effects, and microwave power analysis. At low magnetic fields, weak microwaves lead to positive photoconductivity due to superballistic flow, while strong radiation causes negative photoconductivity because of enhanced phonon scattering.
Artificial intelligence (AI) is a simulation of human intelligence processes by machines, specifically using computer systems. The current article focuses on the application of AI in the field of pharmaceutical analysis. This article describes the history and evolution of AI and its use in different fields along with pharmaceuticals. Current available AI models in pharmaceutical analysis and two creative models were developed and discussed in detail. Visualized schematic diagrams were drawn, and explained hypothesis test procedures with the logical process. In addition, anticipated challenges, advantages and disadvantages were presented. Overall, the current article provides the influence of AI on pharmaceutical analysis in the coming future.
Understanding fluid-driven fracture propagation in anisotropic, layered geological formations is critical for optimizing resource recovery in unconventional reservoirs. In this study, we present a novel experimental and numerical investigation into fluid flow-induced deformation dynamics under biaxial loading using analogue models of shale and reservoir rock. A custom-designed low-pressure biaxial compression system is developed to simulate realistic subsurface conditions while allowing real-time, high-resolution visualization of fracture evolution in low-modulus elastic and viscoelastic materials. Gelatin-based analogues, representing both isotropic and anisotropic formations, are employed to mimic geological heterogeneity and elasticity. Fracturing experiments are conducted using water, SAE 140 oil, and oil-toluene mixtures injected at a constant rate of 1 ml/min under controlled loading conditions (confining load: 1.23 bar; top load: 0.52 bar). The evolving fracture geometries are analysed using ImageJ, focusing on metrics such as aspect ratio and fracture length. Numerical simulations based on a phase field damage model are performed to predict fracture initiation and propagation, showing strong agreement with experimental results and qualitative consistency with the analytical Khristianovic-Geertsma-de Klerk (KGD) model. Our integrated approach provides critical insights into the role of fluid viscosity, heterogeneity and loading conditions on fracture dynamics. The experimental setup also supports studies relevant to CO₂ sequestration and gas hydrate dissociation. This work advances the mechanistic understanding of fracture processes in layered formations, offering insights for field-scale hydraulic fracturing and sustainable subsurface resource management.
The rapid emergence of generative artificial intelligence (genAI) technologies has created new opportunities and challenges in undergraduate nursing education. As educators seek to integrate these tools into curricula, understanding their current applications and implications is essential. This scoping review aimed to explore how generative AI is being utilized in undergraduate nursing education. A comprehensive search was conducted across five databases for studies published between 2022 and March 2025. A total of 1641 records were identified, with 15 studies meeting the inclusion criteria following screening and full-text review. Included studies consisted of three qualitative studies, three teaching tips, and nine case studies. Three primary themes emerged: (1) applications of generative AI in nursing education, including its use for generating course materials and academic administrative tasks; (2) ethical considerations such as academic integrity, bias, and equitable access; and (3) faculty role and readiness, highlighting the need for professional development, clear policies, and institutional support. This scoping review highlights the emerging role of genAI in undergraduate nursing education, revealing both its promise and its complexity. From enhancing classroom engagement and administrative efficiency to raising concerns around bias and academic integrity, the integration of genAI demands thoughtful and ethical implementation. Faculty readiness and institutional guidance are pivotal to ensuring that these tools are used to enrich student learning rather than compromise professional and academic integrity. As the field continues to evolve, future research should focus on evaluating outcomes, addressing gaps in faculty training, and establishing best practices to harness the full potential of genAI in shaping the next generation of nurses.
Passive myocardial stiffness of the left ventricle is a promising biomarker for the early detection of cardiotoxicity and heart failure. It can be estimated non-invasively through inverse optimization, using patient-specific finite element models, derived from Magnetic Resonance Imaging. A key step is defining the myocardium's constitutive law. Guccione transversely-isotropic Fung-type law is widely used. It includes a global stiffness parameter to be estimated, and three anisotropy parameters (bf, bt, bft) whose values vary significantly across studies, complicating parameters selection. This study investigates how anisotropy parameters influence myocardial stiffness estimation. First, biaxial extension and triaxial shear tests were simulated using finite element models, to analyze each parameter's effect on tissue behavior and compare published parameter sets. Then their impact on the displacement fields was studied using an idealized left ventricle geometry. Finally, the impact of the parameter sets on stiffness estimation was evaluated using six patient-specific finite element models. Results showed that varying the anisotropy parameters significantly affected both local tissue behavior and global ventricular mechanics. Within the studied range of variation, bt had the most pronounced impact, and bft had the least impact. Parameter set choice also affected significantly the estimation of stiffness, though no set led to a significantly lower residual error. These findings help clarify how variability in constitutive parameters affects myocardial stiffness estimates. They represent a key step in the methodological framework by guiding parameter selection and interpretation, thereby supporting a more consistent and informed use of stiffness metrics in future research.
The black soil region of Northeast China, a premier grain production base, faces mounting ecological risks from decades of intensive herbicide application. This study systematically investigated the residues, spatial distribution, and key determinants of three frequently detected herbicides-atrazine, acetochlor, and metolachlor. A total of 65 surface soil samples (0-20 cm) were collected from nine representative agricultural areas across Heilongjiang, Jilin, and Liaoning provinces, and analyzed using gas chromatography coupled with micro-electron capture detection. Detection frequencies followed metolachlor (98.5%) > acetochlor (96.9%) > atrazine (63.1%), with mean concentrations highest for acetochlor (123 ng g-1). Pronounced spatial heterogeneity was observed, with contamination hotspots identified in the three cities of Hailun, Songyuan, and Changtu. Crop type dominated residue profiles: atrazine and metolachlor accumulated primarily in maize fields, whereas acetochlor prevailed across both maize and soybean systems. Soil pH and clay content emerged as key edaphic controls, exhibiting significant positive correlations with atrazine (r = 0.41) and metolachlor (r = 0.32-0.35) residues, while total organic carbon (TOC) showed no significant correlation. Significant positive inter-herbicide correlations (r = 0.49-0.72) further indicated co-application practices and shared environmental fate. These findings establish a scientific basis for transitioning from uniform to spatially and agronomically differentiated herbicide management strategies essential for safeguarding soil health and agricultural sustainability in this critical grain-producing region.
Conventional imaging techniques lack the ability to quantify localized brain tissue displacements and strains associated with Glioblastoma (GBM) growth and treatment response. In this proof-of-concept study, a non-invasive approach is presented to map displacement and strain fields using Digital Volume Correlation (DVC) on serial T1-Gadolinium MRI scans of a GBM patient over 63 days. A comprehensive MRI pre-processing pipeline was applied, followed by the generation of meshes segmenting healthy brain tissue, tumor, and ventricles. Three distinct regularization scenarios were implemented to capture localized tissue deformations. DVC results were qualitatively compared to MRI anatomical changes and quantitatively validated against symmetric normalization (SyN) registration. DVC outperforms conventional SyN registration in capturing heterogeneous tissue deformations. These preliminary findings suggest greater sensitivity to biomechanical alterations induced by tumor progression and demonstrate the potential of DVC-augmented imaging to serve as a quantitative biomarker for assessing GBM-induced brain mechanics. By generating maps of ventricle deformation, the method provides new opportunities for early detection and monitoring of elevated intracranial pressure (ICP) in GBM patients.