Abstract
Objective.The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction. Approach. A generative adversarial network with fully connected layers is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets. Main results. The heart rate (HR) of this dataset was further extracted to evaluate the performance of the model obtaining a mean absolute error of 1.31 bpm comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 bpm. Significance. The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of HR (70-115 bpm), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.

Abstract

Abstract
Segmentation of lung nodules in CT images is an important step during the clinical evaluation of patients with lung cancer. Furthermore, early assessment of the cancer is crucial to increase the overall survival chances of patients with such disease, and the segmentation of lung nodules can help detect the cancer in its early stages. Consequently, there are many works in the literature that explore the use of neural networks for the segmentation of lung nodules. However, these frameworks tend to rely on accurate labelling of the nodule centre to then crop the input image. Although such works are able to achieve remarkable results, they do not take into account that the healthcare professional may fail to correctly label the centre of the nodule. Therefore, in this work, we propose a new framework based on the U-Net model that allows to correct such inaccuracies in an interactive fashion. It is composed of two U-Net models in cascade, where the first model is used to predict a rough estimation of the lung nodule location and the second model refines the generated segmentation mask. Our results show that the proposed framework is able to be more robust than the studied baselines. Furthermore, it is able to achieve state-of-the-art performance, reaching a Dice of 91.12% when trained and tested on the LIDC-IDRI public dataset.

Abstract
Advancements in the care for patients with breast cancer have demanded the development of biomechanical breast models for the planning and risk mitigation of such invasive surgical procedures. However, these approaches require large amounts of high-quality magnetic resonance imaging (MRI) training data that is of difficult acquisition and availability. Although this can be solved using synthetic data, generating high resolution images comes at the price of very high computational constraints and tipically low performances. On the other hand, producing lower resolution samples yields better results and efficiency but falls short of meeting health professional standards. Therefore, this work aims to validate a joint approach between lower resolution generative models and the proposed superresolution architecture, titled Shifted Window Image Restoration (SWinIR), which was used to achieve a 4x increase in image size of breast cancer patient MRI samples. Results prove to be promising and to further expand upon the super-resolution state-of-the-art, achieving good maximum peak signal-to-noise ratio of 41.36 and structural similarity index values of 0.962 and thus beating traditional methods and other machine learning architectures. © 2025 IEEE.

Abstract
We study the dynamics as well as the interaction of stable dissipative solitons (DSs) of the cubic complex Ginzburg-Landau equation which are stabilized only by nonlinear gradient (NLG) terms. First we review stationary, periodic, quasi-periodic, and chaotic solutions. Then we investigate sudden transitions to chaotic from periodic and vice versa as a function of one parameter, as well as different outcomes, for fixed parameters, when varying the initial condition. In addition, we present a quasi-analytic approach to evaluate the separation of nearby trajectories for the case of stationary DSs as well as for periodic DSs, both stabilized by nonlinear gradient terms. In a separate section collisions between different types of DSs are reviewed. First we present a concise review of collisions of DSs without NLG terms and then the results of collisions between stationary DSs stabilized by NLG terms are summarized focusing on the influence of the nonlinear gradient term associated with the Raman effect. We point out that both, meandering oscillatory bound states as well as bound states with large amplitude oscillations appear to be specific for coupled cubic complex Ginzburg-Landau equations with a stabilizing cubic nonlinear gradient term.

Abstract
Measuring the density of tartrazine (TZ) powder allowed to develop a protocol for fast preparation of aqueous solutions with a desired concentration. The stability time of these solutions decreases exponentially with the increase of TZ concentration: solutions with TZ concentrations below 25% remain stable for more than 24 h, while the solution with 60% TZ remains stable only for 35 min. To validate the developed protocol, muscle samples were immersed in the 40% TZ solution and, as expected, the tissue transparency increased smoothly and exponentially during the whole treatment of 30 min. The diffusion time of TZ in ex vivo skeletal muscle was quantitatively determined with high accuracy as tau TZ = 5.39 +/- 0.49 min for sample thickness of 0.5 mm. By measuring the refractive index of TZ solutions during preparation, it will be easier to prepare such solutions in a fast manner for future research on tissue optical clearing.