A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography.

Cryogenic electron tomography is a technique for imaging biological samples in 3D. A microscope collects a series of 2D projections of the sample, and the goal is to reconstruct the 3D density of the sample called the tomogram. Reconstruction is difficult as the 2D projections are noisy and can not be recorded from all directions, resulting in a missing wedge of information.

Deep learning for accelerated and robust MRI reconstruction.

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations.

K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets.

Although deep learning (DL) methods are powerful for solving inverse problems, their reliance on high-quality training data is a major hurdle. This is significant in high-dimensional (dynamic/volumetric) magnetic resonance imaging (MRI), where acquisition of high-resolution fully sampled k-space data is impractical. We introduce a novel mathematical framework, dubbed k-band, that enables training DL models using only partial, limited-resolution k-space data.

Chemical unclonable functions based on operable random DNA pools.

Physical unclonable functions (PUFs) based on unique tokens generated by random manufacturing processes have been proposed as an alternative to mathematical one-way algorithms. However, these tokens are not distributable, which is a disadvantage for decentralized applications. Finding unclonable, yet distributable functions would help bridge this gap and expand the applications of object-bound cryptography.

Evaluation of acceptance and preference of topical lidocaine application versus articaine injection anesthesia after nonsurgical periodontal treatment: A randomized clinical trial.

Background: To compare acceptance and preference of topical lidocaine gel anesthesia with articaine injection anesthesia in patients with moderate periodontitis undergoing scaling and root debridement.

Methods: Ninety-one patients completed this randomized multicenter split-mouth controlled study and underwent two separate periodontal treatment sessions on different days, one with a topical intrapocket lidocaine gel application and the other with an articaine injection anesthesia in a different order depending on randomization. Parameters measured were the patients' preference for topical lidocaine gel anesthesia or injection anesthesia with articaine (primary efficacy criterion), their maximum and average pain, and their intensity of numbness as well as experience of side effects; the probing depth; and the dentists' preference and their evaluations of handling/application, onset and duration of anesthetic effect, and patient compliance.