Dynamic 3D imaging of cerebral blood flow in awake mice using self-supervised-learning-enhanced optical coherence Doppler tomography.

Cerebral blood flow (CBF) is widely used to assess brain function. However, most preclinical CBF studies have been performed under anesthesia, which confounds findings. High spatiotemporal-resolution CBF imaging of awake animals is challenging due to motion artifacts and background noise, particularly for Doppler-based flow imaging.

Osteoporosis Prescreening and Bone Mineral Density Prediction using Dental Panoramic Radiographs.

Recent studies have shown that Dental Panoramic Radiograph (DPR) images have great potential for prescreening of osteoporosis given the high degree of correlation between the bone density and trabecular bone structure. Most of the research works in these area had used pretrained models for feature extraction and classification with good success. However, when the size of the data set is limited it becomes difficult to use these pretrained networks and gain high confidence scores.

A classification algorithm to predict chronic pain using both regression and machine learning - A stepwise approach.

This secondary data analysis study aimed to (1) investigate the use of two sense-based parameters (movement and sleep hours) as predictors of chronic pain when controlling for patient demographics and depression, and (2) identify a classification model with accuracy in predicting chronic pain. Data collected by Oregon Health & Science University between March 2018 and December 2019 under the Collaborative Aging Research Using Technology Initiative were analyzed in two stages. Data were collected by sensor technologies and questionnaires from older adults living independently or with a partner in the community.

Detection of Trabecular Landmarks for Osteoporosis Prescreening in Dental Panoramic Radiographs.

Dental panoramic radiography (DPR) images have recently attracted increasing attention in osteoporosis analysis because of their inner correlation. Many approaches leverage machine learning techniques (e.g.

Structure and melt rheology of polystyrene-based layered silicate nanocomposites.

The melt-state viscoelastic properties of exfoliated in situ polymerized and intercalated solution-blended polystyrene (PS) and organically modified montmorillonite nanocomposites were investigated and compared. The PS nanocomposites prepared by nitroxide-mediated polymerization (NMP) exhibit a stable exfoliated structure whereas the PS nanocomposites prepared by solution mixing exhibit an intercalated structure. The linear viscoelastic properties were strongly correlated with the dispersion state of the nanocomposites.