Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators.

In recent years, large convolutional neural networks have been widely used as tools for image deblurring, because of their ability in restoring images very precisely. It is well known that image deblurring is mathematically modeled as an ill-posed inverse problem and its solution is difficult to approximate when noise affects the data. Really, one limitation of neural networks for deblurring is their sensitivity to noise and other perturbations, which can lead to instability and produce poor reconstructions.

Breast shells for pain and nipple injury prevention: A non-randomized clinical trial.

Objective: This study aimed to analyze the effectiveness of breast shells in preventing pain and nipple injury during breastfeeding.

Method: A non-randomized clinical trial was carried out with blinding to the evaluators of the study results. The study included women with ≥35 weeks of singleton pregnancy, no nipple changes, and a desire to breastfeed.

Biochemical and biophysical properties of a recombinant serine peptidase from Purpureocillium lilacinum.

The industrial uses of peptidases have already been consolidated; however, their range of applications is increasing. Thus, the biochemical characterization of new peptidases could increase the range of their biotechnological applications. In silico analysis identified a gene encoding a putative serine peptidase from Purpureocillium lilacinum (Pl_SerPep), annotated as a cuticle-degrading enzyme.

RISING: A new framework for model-based few-view CT image reconstruction with deep learning.

Medical image reconstruction from low-dose tomographic data is an active research field, recently revolutionized by the advent of deep learning. In fact, deep learning typically yields superior results than classical optimization approaches, but unstable results have been reported both numerically and theoretically in the literature. This paper proposes RISING, a new framework for sparse-view tomographic image reconstruction combining an early-stopped Rapid Iterative Solver with a subsequent Iteration Network-based Gaining step.