Using the Regression Slope of Training Loss to Optimize Chest X-ray Generation in Deep Convolutional Generative Adversarial Networks.

Diffusion models, variational autoencoders, and generative adversarial networks (GANs) are three common types of generative artificial intelligence models for image generation. Among these, GANs are the most frequently used for medical image generation and are often employed for data augmentation in various studies. However, due to the adversarial nature of GANs, where the generator and discriminator compete against each other, the training process can sometimes end with the model unable to generate meaningful images or even producing noise.

Direct View of Gate-Tunable Miniband Dispersion in Graphene Superlattices Near the Magic Twist Angle.

Superlattices from twisted graphene mono- and bilayer systems give rise to on-demand many-body states such as Mott insulators and unconventional superconductors. These phenomena are ascribed to a combination of flat bands and strong Coulomb interactions. However, a comprehensive understanding is lacking because the low-energy band structure strongly changes when an electric field is applied to vary the electron filling.

Revolutionizing Epithelial Differentiability Analysis in Small Airway-on-a-Chip Models Using Label-Free Imaging and Computational Techniques.

Organ-on-a-chip (OOC) devices mimic human organs, which can be used for many different applications, including drug development, environmental toxicology, disease models, and physiological assessment. Image data acquisition and analysis from these chips are crucial for advancing research in the field. In this study, we propose a label-free morphology imaging platform compatible with the small airway-on-a-chip system.

iGTP: Learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics.

Deep-learning models like Variational AutoEncoder have enabled low dimensional cellular embedding representation for large-scale single-cell transcriptomes and shown great flexibility in downstream tasks. However, biologically meaningful latent space is usually missing if no specific structure is designed. Here, we engineered a novel interpretable generative transcriptional program (iGTP) framework that could model the importance of transcriptional program (TP) space and protein-protein interactions (PPI) between different biological states.

Depression, Anxiety, and Pain Predict Quality of Life in Patients with Differentiated Thyroid Cancer Postradiotherapy Ablation in Taiwan: A 48-Week Follow-Up Study.

Despite the generally good prognosis of differentiated thyroid cancer (DTC), impairments in health-related quality of life (HRQoL) remain a major concern in these patients. This study examined the patterns and predictors of change in mental and physical HRQoL in DTC survivors following radiotherapy ablation. Two hundred patients with DTC who received radiotherapy ablation in southern Taiwan between 2015 and 2018 were interviewed using the Taiwan version of the 36-item Short-form Health Survey (SF-36), the Taiwanese Depression Questionnaire (TDQ), and the Hamilton Rating Scale for Anxiety (HAM-A) at baseline and after 24 and 48 weeks of treatment.