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Unveiling chaos and stability in advection diffusion reaction systems via advanced dynamical and sensitivity analysis
Size dependence of the ion pairing preferences investigated by free energy calculations
We calculate pairing free energies for a series of model monovalent ion pairs with equal sizes spanning a large radius range in the aqueous solution. Thermodynamic analysis reveals that the ion pairing strength displays an initial decrease in a nearly linear fashion followed by a significant enhancement at a relatively slower rate as the ion size gradually increases, resulting in the weakest pairing preference for ions with intermediate size. The free energy decomposition illustrates that the ion pairing is jointly governed by a delicate balance of the favorable ion–ion interaction term and the repulsive solvent-induced contribution, with their compensation leading to a small pairing free energy. The dependence of this substantial compensation on the ion size actually dominates the ion pairing strength and the magnitude of the free energy, which accounts for the occurrence of the less association between the intermediate-size ions. Estimation of van der Waals and electrostatic free energies highlights the importance of attractive dispersion forces in determining the shape of the potential of mean force, specifically for large ions with less favorable interaction with water molecules than the water–water interactions. In addition, the effect of varying the cation size on the decreased pairing preference surpasses that of altering the anion size. Our study significantly enhances our understanding of the empirical rule of matching ion size for predicting ion pairing preferences in aqueous solutions.
Oral health of nursing home residents in Flanders, Belgium, and its associated factors
Local dynamics drive the C–CX3 (X = H and F) bond photodissociation in acetylacetones
The primary photodissociation events of acetylacetone and its fluorinated analogs reveal that the translational energy distribution profiles of the CH3 and CF3 radicals follow a barrier-impulsive model for the C–C bond cleavage. Analysis based on the one-dimensional potential energy surfaces in the T1 state, as well as dynamics simulations using on-the-fly semi-empirical potentials, suggest that the C–C bond cleavage proximal to the OH group, in general, is accompanied by proton migration. Interestingly, the near identical fragment translational energy distribution profiles of CH3 radical release from acetylacetone and trifluoroacetylacetone, as well as CF3 radical release from trifluoroacetylacetone and hexafluoroacetylacetone, suggest that the dynamics of formation of CH3/CF3 radicals in acetylacetones appears to be independent of the nature of the substituent on the other end of the molecule. In the case of acetylacetones, the C–C bond cleavage is governed by the local intramolecular vibrational redistribution along the complex reaction coordinate, which appears to be statistical for CF3 release and non-statistical for CH3 release; however, it remains non-statistical over the entire molecular framework.
Tuberculosis treatment success rate and its predictors among TB HIV co-infected patients in East and North Eastern Uganda
Applications of machine learning in predicting rut depth in off-road environments
The prognostic effect and mechanism of erysipelas in cancer-associated lymphedema
A unique AI-based tool for automated segmentation of pulp cavity structures in maxillary premolars on CBCT
Abstract To develop and validate an artificial intelligence (AI)-driven tool for the automatic segmentation of pulp cavity structures in maxillary premolars teeth on cone-beam computed tomography (CBCT). One hundred and eleven CBCT scans were divided into training (n = 55), validation (n = 14), and testing (n = 42) sets, with manual segmentation serving as the ground truth. The AI tool automatically segmented the testing dataset, with errors corrected by an operator to create refined 3D (R-AI) models. The overall AI performance was assessed by comparing AI and R-AI models, and thirty percent of the test sample was manually segmented to compare AI and human performance. Time-efficiency of each method was recorded in seconds (s). Statistical analysis included independent and paired t-tests to evaluate the effect of tooth type on accuracy metrics and AI versus manual segmentation. One-way ANOVA with Tukey’s post hoc test was used for time efficiency analysis. A 5% significance level was used for all analyses.The AI tool demonstrated excellent performance with Dice similarity coefficients (DSC) ranging from 88% ± 7 to 93% ± 3 and 95% Hausdorff distances (HD) from 0.13 ± 0.06 to 0.16 ± 0.06 mm. Automated segmentation of maxillary second premolars performed slightly better than that of maxillary first premolars in terms of intersection over union (p = 0.005), DSC (p = 0.008), recall (p = 0.008), precision (p = 0.02), and 95% HD (p = 0.04). The AI-based approach showed higher recall (p = 0.04), accuracy (p = 0.01), and lower 95% HD than manual segmentation (p < 0.001). AI segmentation (42.8 ± 8.4 s) was 75 times faster than manual segmentation (3218.7 ± 692.2 s) (p < 0.001). The AI tool proved highly accurate and time-efficient, surpassing human expert performance.
Insight into mtDNA diversity of wild forest musk deer (Moschus berezovskii) in Shanxi Province mountains
Author Correction: Reinforcement learning-based estimation for spatio-temporal systems
Optical fiber sensor used for measuring the rotation angle of the disconnecting switch
24-Month clinical evaluation of cervical restorations bonded using radio-opaque universal adhesive compared to conventional universal adhesive in carious cervical lesions: A randomized clinical trial
Abstract The aim of the current study was to evaluate the clinical performance of the novel radio-opaque universal adhesive “Scotchbond™ Universal Adhesive Plus” compared to conventional universal adhesive “Single Bond Universal” over 24 months in cervical carious lesions. Fifty participants with cervical carious lesions were randomly allocated into two groups (n = 25); either Scotchbond™ Universal Plus Adhesive (intervention) or Single Bond™ Universal Adhesive (control). Restorations were assessed at baseline, 12 and 24 months using the modified USPHS criteria. Data analysis was conducted using MedCalc software, version 22 for Windows. Intergroup comparisons at each follow-up were performed using the Chi-Square test (p ≤ 0.05). Intragroup comparisons within each intervention were conducted using Cochran’s Q test (p ≤ 0.016). After 24 months, all restorations in Scotchbond™ Universal Plus scored alpha, while in Single Bond™ Universal group, three restorations scored bravo after 24 months in marginal adaptation and discoloration. There was no statistically significant difference between both adhesives (p > 0.05) at all follow-up periods. Intragroup comparison within both adhesives has shown no statistically significant change across follow-up periods regarding all tested outcomes (p > 0.016) except for marginal adaptation within Single Bond Universal, where there was statistically significant difference (p = 0.005). Both adhesives exhibited satisfactory clinical performance in cervical restorations after 24-months. The present study emphasizes the clinical significance of using a new radio-opaque universal adhesive for restoring carious cervical lesions, providing radio-opacity, low viscosity, excellent handling, eliminating misinterpretation of MDP-based adhesive layer and generating reliable bonding performance to support long-term success in restorative dentistry.
Integrating angio-IMR and CMR-assessed microvascular obstruction for improved risk stratification of STEMI patients
A robust method for autofluorescence-free immunofluorescence using high-speed fluorescence lifetime imaging microscopy
Cadmium resistance microbes and TiO2 nanoparticles alleviate cadmium toxicity in wheat
Abstract Cadmium toxicity in the soil is an alarming issue, and among innumerable approaches, microbe-facilitated nanoparticle application for alleviation of Cd stress is a well-accepted technique. The present study explored the efficiency of combined TiO2-NPs and Staphylococcus aureus M1 strains for Cd mitigation in wheat plants. Results depicted that Cd stress attenuates the growth attributes while the collective application of NPs and microbes significantly upsurges the growth attributes as contrasted to Cd treatment. Combined TiO2-NPs and microbes application increased the total chlorophyll (12), a (10), b (11), and carotenoids (13%) under Cd (50 mg kg− 1) compared to microbial treatment. MDA (4), H2O2 (3), and EL (5%) were significantly down-regulated with combined TiO2-NPs and microbes application under Cd (50 mg kg− 1) compared to microbial treatment. CAT (17), SOD (7), POD (8), and APX (29%) were increased with combined TiO2-NPs and microbes application under Cd (50 mg kg− 1) comparison to microbial treatment. Cd accumulation in roots (34), shoots (23), and grains (27%) were significantly reduced under Cd (50 mg kg− 1) with combined TiO2-NPs and microbes application, contrary to microbial treatment. Subsequently, combined TiO2-NPs and microbial strains Staphylococcus aureus M1 application is a sustainable solution to boost crop production under Cd stress.
Hydration behaviors, workability, and strength variations in direct aqueous carbonation (DAC) of Portland cement paste
Robust multi-label surgical tool classification in noisy endoscopic videos
Abstract Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emerging ML techniques in analysing surgical data, particularly recordings of procedures, for digitising clinical and non-clinical functions like preoperative planning, context-aware decision-making, and operating skill assessment. However, this field is still in its infancy and lacks representative, well-annotated datasets for training robust models in intermediate ML tasks. Also, existing datasets suffer from inaccurate labels, hindering the development of reliable models. In this paper, we propose a systematic methodology for developing robust models for surgical tool classification using noisy endoscopic videos. Our methodology introduces two key innovations: (1) an intelligent active learning strategy for minimal dataset identification and label correction by human experts through collective intelligence; and (2) an assembling strategy for a student-teacher model-based self-training framework to achieve the robust classification of 14 surgical tools in a semi-supervised fashion. Furthermore, we employ strategies such as weighted data loaders and label smoothing to enable the models to learn difficult samples and address class imbalance issues. The proposed methodology achieves an average F1-score of 85.88% for the ensemble model-based self-training with class weights, and 80.88% without class weights for noisy tool labels. Also, our proposed method significantly outperforms existing approaches, which effectively demonstrates its effectiveness.