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Generative inpainting of incomplete Euclidean distance matrices of trajectories generated by a fractional Brownian motion
Abstract Fractional Brownian motion (fBm) exhibits both randomness and strong scale-free correlations, posing a challenge for generative artificial intelligence to replicate the underlying stochastic process. In this study, we evaluate the performance of diffusion-based inpainting methods on a specific dataset of corrupted images, which represent incomplete Euclidean distance matrices (EDMs) of fBm across various memory exponents (H). Our dataset reveals that, in the regime of low missing ratios, data imputation is unique, as the remaining partial graph is rigid, thus providing a reliable ground truth for inpainting. We find that conditional diffusion generation effectively reproduces the inherent correlations of fBm paths across different memory regimes, including sub-diffusion, Brownian motion, and super-diffusion trajectories, making it a robust tool for statistical imputation in cases with high missing ratios. Moreover, while recent studies have suggested that diffusion models memorize samples from the training dataset, our findings indicate that diffusion behaves qualitatively differently from simple database searches, allowing for generalization rather than mere memorization of the training data. As a biological application, we utilize our fBm-trained diffusion model to impute microscopy-derived distance matrices of chromosomal segments (FISH data), which are incomplete due to experimental imperfections. We demonstrate that our inpainting method outperforms standard bioinformatic methods, suggesting a novel physics-informed generative approach for the enrichment of high-throughput biological datasets.
Role of Bronchoscopy in Difficult to Wean Patients
Vortex-assisted liquid–liquid microextraction for the simultaneous derivatization and extraction of five primary aliphatic amines in water sample
Association of Nutritional Status with Clinical Outcomes of Post-bilateral Lung Transplant Patients
A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma
Assessment of Bedside Ultrasound in Patients with Pneumonia and its Correlation with Chest Radiography Rale Score and Chest Computed Tomography
Biomechanical and neuromuscular adaptations in joint contributions during loaded countermovement jumps
Empowering Healthcare Workers: Advancing Sustainable Biomedical Waste Management at Meenakshi Mission Hospital
Impact behavior of recycled aggregate concrete modified with nano-silica and fiber
Incidence and In-hospital Outcome of Ventilator Associated Pneumonia in Intensive Care Units of a Tertiary Care Hospital in Kashmir
Selection and validation of reference genes for RT-qPCR normalization in dormant cancer cells
Determination of Adequacy of Hemodialysis (SLED vs. IHD) Delivered Among Critically Ill Patients in Medical ICU
Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer
Relationship between Mitral Annular Plane Systolic Excursion and Left Ventricular Ejection Fraction by Modified Simpson\'s Method using Point of Care Cardiac Ultrasound: An Observational Analytical Study
Improvement of photocatalytic degradation of organic dyes by vanadium doped titanium oxide nanoparticles using solar simulator
Abstract The removal of complex organic contaminants and pollutants from water, the heterogeneous photocatalytic degradation of methylene blue (MB), Eosin, Fluorescein and Rhodamine 6G (Rh 6G) dyes were studied using vanadium-doped titanium oxide nanoparticles (V-TiO2 NPs) as a derived catalyst, incorporated in a Polyvinyl Alcohol (PVA) solution as a host to form thin films doped from dyes and V-TiO2 NPs. Solar simulator light was tested for the photodegradation process. Vanadium-doped titanium oxide nanoparticles (V-TiO2 NPs) was successfully synthesized by a sol-gel technique and the solid-state thin films of PVA/dye/V-TiO2 were prepared by a casting method. The chemical synthesis of V-TiO2 NPs was confirmed by X-ray diffractograms, Energy Dispersive X-ray (EDX), High Resolution Transmission Electron [HRTEM] and Fourier transform infrared spectroscopy [FTIR]. We had studied the effect of V-TiO2 NPs as a catalyst to enhance the photocatalytic activity of different organic dyes and we found that the vanadium doped TiO2 nanoparticle behave as the best catalyst for successfully photodegraded of Methylene Blue (MB), Eosin and Fluorescein dyes but effect of V-TiO2 NPs as a catalyst on the photodegradation of Rh 6G dye take a more time to degraded under solar simulator. The V-TiO2 nanoparticles displayed eminent realization for achieving nearly 99% degradation of MB, 97% of Fluorescein, 99% of Eosin after 120 min. So, the V-TiO2 NPs can be used as a pollutant removal in the wastewater application. But from the results of photocatalytic performance of Rh 6G, we noticed that the photodegradation of Rh 6G dye in the presence of V-TiO2 NPs is weak as 50% photodegraded after 300 min and this is due to the strong reaction between the catalyst and Rh 6G dye. In this case, V-TiO2 NPs behave as a photostabilizer for Rh 6G dye under solar simulator and this structure (Rh 6G/V-TiO2) can be apply as a photoanode in a dye sensitized solar cell application. The optical band gap energy of V-TiO2 NPs with a MB, Eosin and Fluorescein dyes were calculated at zero minute and at 120 min but the band gap energy for Rh 6G dye with a catalyst was calculated at 300 min and we found that the band gap energy was increased by increasing exposure time. From the observation to measure the pseudo-first-order rate constants of four dyes were computed. The photodegradation percentage was determined and found that the photodegradation percentage of MB, Eosin and Fluorescein in the presence of the of V-TiO2 NPs as a catalyst is higher than the photodegradation percentage of pure dyes.