ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images.

One of the most familiar types of disease is Alzheimer's disease (AD) and it mainly impacts people over the age limit of 60. AD causes irreversible brain damage in humans. It is difficult to recognize the various stages of AD, hence advanced deep learning methods are suggested for recognizing AD in its initial stages.

Helical growth during the phototropic response, avoidance response, and in stiff mutants of Phycomyces blakesleeanus.

The sporangiophores of Phycomyces blakesleeanus have been used as a model system to study sensory transduction, helical growth, and to establish global biophysical equations for expansive growth of walled cells. More recently, local statistical biophysical models of the cell wall are being constructed to better understand the molecular underpinnings of helical growth and its behavior during the many growth responses of the sporangiophores to sensory stimuli. Previous experimental and theoretical findings guide the development of these local models.