Data driven regularization models of non-linear ill-posed inverse problems in imaging

Imaging technologies are widely used in application fields such as natural sciences, engineering, medicine, and life sciences. A broad class of imaging problems reduces to solve ill-posed inverse problems (IPs). Traditional strategies to solve these ill-posed IPs rely on variational regularization methods, which are based on minimization of suitable energies, and make use of knowledge about the image formation model (forward operator) and prior knowledge on the solution, but lack in incorporating knowledge directly from data. On the other hand, the more recent learned approaches can easily learn the intricate statistics of images depending on a large set of data, but do not have a systematic method for incorporating prior knowledge about the image formation model. The main purpose of this thesis is to discuss data-driven image reconstruction methods which combine the benefits of these two different reconstruction strategies for the solution of highly nonlinear ill-posed inverse problems. Mathematical formulation and numerical approaches for image IPs, including linear as well as strongly nonlinear problems are described. More specifically we address the Electrical impedance Tomography (EIT) reconstruction problem by unrolling the regularized Gauss-Newton method and integrating the regularization learned by a data-adaptive neural network. Furthermore we investigate the solution of non-linear ill-posed IPs introducing a deep-PnP framework that integrates the graph convolutional denoiser into the proximal Gauss-Newton method with a practical application to the EIT, a recently introduced promising imaging technique. Efficient algorithms are then applied to the solution of the limited electrods problem in EIT, combining compressive sensing techniques and deep learning strategies. Finally, a transformer-based neural network architecture is adapted to restore the noisy solution of the Computed Tomography problem recovered using the filtered back-projection method.

Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications

Ill-conditioned inverse problems frequently arise in life sciences, particularly in the context of image deblurring and medical image reconstruction. These problems have been addressed through iterative variational algorithms, which regularize the reconstruction by adding prior knowledge about the problem's solution. Despite the theoretical reliability of these methods, their practical utility is constrained by the time required to converge. Recently, the advent of neural networks allowed the development of reconstruction algorithms that can compute highly accurate solutions with minimal time demands. Regrettably, it is well-known that neural networks are sensitive to unexpected noise, and the quality of their reconstructions quickly deteriorates when the input is slightly perturbed. Modern efforts to address this challenge have led to the creation of massive neural network architectures, but this approach is unsustainable from both ecological and economic standpoints. The recently introduced GreenAI paradigm argues that developing sustainable neural network models is essential for practical applications. In this thesis, we aim to bridge the gap between theory and practice by introducing a novel framework that combines the reliability of model-based iterative algorithms with the speed and accuracy of end-to-end neural networks. Additionally, we demonstrate that our framework yields results comparable to state-of-the-art methods while using relatively small, sustainable models. In the first part of this thesis, we discuss the proposed framework from a theoretical perspective. We provide an extension of classical regularization theory, applicable in scenarios where neural networks are employed to solve inverse problems, and we show there exists a trade-off between accuracy and stability. Furthermore, we demonstrate the effectiveness of our methods in common life science-related scenarios. In the second part of the thesis, we initiate an exploration extending the proposed method into the probabilistic domain. We analyze some properties of deep generative models, revealing their potential applicability in addressing ill-posed inverse problems.

The Effect Of Pelvic Floor Muscle Training Alone Or In Combination With Electrostimulation In The Treatment Of Sexual Dysfunction In Women With Multiple Sclerosis.

Sexual dysfunction (SD) affects up to 80% of multiple sclerosis (MS) patients and pelvic floor muscles (PFMs) play an important role in the sexual function of these patients. The objective of this paper is to evaluate the impact of a rehabilitation program to treat lower urinary tract symptoms on SD of women with MS. Thirty MS women were randomly allocated to one of three groups: pelvic floor muscle training (PFMT) with electromyographic (EMG) biofeedback and sham neuromuscular electrostimulation (NMES) (Group I), PFMT with EMG biofeedback and intravaginal NMES (Group II), and PFMT with EMG biofeedback and transcutaneous tibial nerve stimulation (TTNS) (Group III). Assessments, before and after the treatment, included: PFM function, PFM tone, flexibility of the vaginal opening and ability to relax the PFMs, and the Female Sexual Function Index (FSFI) questionnaire. After treatment, all groups showed improvements in all domains of the PERFECT scheme. PFM tone and flexibility of the vaginal opening was lower after the intervention only for Group II. All groups improved in arousal, lubrication, satisfaction and total score domains of the FSFI questionnaire. This study indicates that PFMT alone or in combination with intravaginal NMES or TTNS contributes to the improvement of SD.

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

Biogenic Silver Nanoparticles Associated With Silver Chloride Nanoparticles (ag@agcl) Produced By Laccase From Trametes Versicolor.

In the present study, semi-purified laccase from Trametes versicolor was applied for the synthesis of silver nanoparticles, and the properties of the produced nanoparticles were characterized. All of the analyses of the spectra indicated silver nanoparticle formation. A complete characterization of the silver nanoparticles showed that a complex of silver nanoparticles and silver ions was produced, with the majority of the particles having a Ag(2+) chemical structure. A hypothetical mechanistic scheme was proposed, suggesting that the main pathway that was used was the interaction of silver ions with the T1 site of laccase, producing silver nanoparticles with the concomitant inactivation of laccase activity and posterior complexing with silver ions.

Molecularly Imprinted Solid Phase Extraction Of Fluconazole From Pharmaceutical Formulations.

This work encompasses a direct and coherent strategy to synthesise a molecularly imprinted polymer (MIP) capable of extracting fluconazole from its sample. The MIP was successfully prepared from methacrylic acid (functional monomer), ethyleneglycoldimethacrylate (crosslinker) and acetonitrile (porogenic solvent) in the presence of fluconazole as the template molecule through a non-covalent approach. The non-imprinted polymer (NIP) was prepared following the same synthetic scheme, but in the absence of the template. The data obtained from scanning electronic microscopy, infrared spectroscopy, thermogravimetric and nitrogen Brunauer-Emmett-Teller plot helped to elucidate the structural as well as the morphological characteristics of the MIP and NIP. The application of MIP as a sorbent was demonstrated by packing it in solid phase extraction cartridges to extract fluconazole from commercial capsule samples through an offline analytical procedure. The quantification of fluconazole was accomplished through UPLC-MS, which resulted in LOD≤1.63×10(-10) mM. Furthermore, a high percentage recovery of 91±10% (n=9) was obtained. The ability of the MIP for selective recognition of fluconazole was evaluated by comparison with the structural analogues, miconazole, tioconazole and secnidazole, resulting in percentage recoveries of 51, 35 and 32%, respectively.

Modelagem matemática e simulação numérica do resfriamento rápido de morango com ar forçado

This work approaches the forced air cooling of strawberry by numerical simulation. The mathematical model that was used describes the process of heat transfer, based on the Fourier's law, in spherical coordinates and simplified to describe the one-dimensional process. For the resolution of the equation expressed for the mathematical model, an algorithm was developed based on the explicit scheme of the numerical method of the finite differences and implemented in the scientific computation program MATLAB 6.1. The validation of the mathematical model was made by the comparison between theoretical and experimental data, where strawberries had been cooled with forced air. The results showed to be possible the determination of the convective heat transfer coefficient by fitting the numerical and experimental data. The methodology of the numerical simulations was showed like a promising tool in the support of the decision to use or to develop equipment in the area of cooling process with forced air of spherical fruits.

O jogo de futebol : investigação de sua estrutura, de seus modelos e da inteligencia de jogo, do ponto de vista da complexidade

Universidade Estadual de Campinas . Faculdade de Educação Física

Tônus muscular : uma contribuição para os estudos em imagem corporal

Universidade Estadual de Campinas . Faculdade de Educação Física

Futebol como projeto profissional de mulheres : interpretações da busca pela legitimidade = Football as a career project for women : legitimacy search interpretation

Universidade Estadual de Campinas . Faculdade de Educação Física

Estudo teorico dos aspectos prosturais e da plasticidade muscular frente ao alongamento

Universidade Estadual de Campinas. Faculdade de Educação Física

Corpo : tradição, valores, possibilidades do desvelar

Universidade Estadual de Campinas. Faculdade de Educação Física

Wavelets And Adaptive Grids For The Discontinuous Galerkin Method

In this paper, space adaptivity is introduced to control the error in the numerical solution of hyperbolic systems of conservation laws. The reference numerical scheme is a new version of the discontinuous Galerkin method, which uses an implicit diffusive term in the direction of the streamlines, for stability purposes. The decision whether to refine or to unrefine the grid in a certain location is taken according to the magnitude of wavelet coefficients, which are indicators of local smoothness of the numerical solution. Numerical solutions of the nonlinear Euler equations illustrate the efficiency of the method. © Springer 2005.

Desempenho e qualidade dos ovos de poedeiras comerciais alimentadas com rações contendo diferentes níveis de metionina e lisina

Foram avaliados os efeitos dos níveis de metionina e lisina sobre o desempenho e a qualidade interna e externa dos ovos de poedeiras comerciais. Foram utilizadas 256 poedeiras Hisex White com 68 semanas de idade, alojadas individualmente em delineamento inteiramente casualizado em arranjo fatorial 4 õ 4, com quatro níveis de lisina (0,482; 0,682; 0,882 e 1,082%) e metionina (0,225; 0,318; 0,411 e 0,505%), totalizando 16 dietas, cada uma com quatro repetições de quatro aves. O desempenho foi avaliado por meio das características consumos de ração, lisina, metionina, proteína bruta e de energia, peso, produção e massa de ovos e conversão alimentar. A qualidade interna dos ovos foi avaliada por meio das características peso e porcentagem de albúmen e gema e pela unidade Haugh. As aves apresentaram máxima produção de ovos quando alimentadas com rações contendo 0,444% de metionina total e 0,872% de lisina total. A classificação dos ovos por tipo e as características de qualidade interna e externa dos ovos não foram influenciadas pelos níveis de metionia e lisina da dieta.

Desempenho, qualidade dos ovos e balanço de nitrogênio de poedeiras comerciais alimentadas com rações contendo diferentes níveis de proteína bruta e lisina

O experimento foi conduzido para avaliar os efeitos de rações com diferentes níveis de proteína bruta (PB) e lisina sobre as características de desempenho, a qualidade interna dos ovos e o balanço/retenção do nitrogênio. Foram utilizadas 160 poedeiras Hisex White com 48 semanas de idade, alojadas individualmente em delineamento inteiramente casualizado em esquema fatorial 4 × 2, com quatro níveis de PB (12, 14, 16 e 18%) e dois de lisina (0,85 e 1,00%), totalizando oito rações com cinco repetições de quatro aves. O consumo de proteína bruta, o peso dos ovos, a massa de ovos e a porcentagem de albúmen apresentam resposta linear crescente aos níveis de PB na dieta. O balanço de nitrogênio não é alterado pelos níveis de proteína das rações.