Spatial versus graphical representation of distributional semantic knowledge.

Spatial distributional semantic models represent word meanings in a vector space. While able to model many basic semantic tasks, they are limited in many ways, such as their inability to represent multiple kinds of relations in a single semantic space and to directly leverage indirect relations between two lexical representations. To address these limitations, we propose a distributional graphical model that encodes lexical distributional data in a graphical structure and uses spreading activation for determining the plausibility of word sequences.

Regenerative Engineering of a Biphasic Patient-Fitted Temporomandibular Joint Condylar Prosthesis.

Regenerative medicine approaches to restore the mandibular condyle of the temporomandibular joint (TMJ) may fill an unmet patient need. In this study, a method to implant an acellular regenerative TMJ prosthesis was developed for orthotopic implantation in a pilot goat study. The scaffold incorporated a porous, polycaprolactone-hydroxyapatite (PCL-HAp, 20wt% HAp) 3D printed condyle with a cartilage-matrix-containing hydrogel.

Characterization of Experimental Nanoparticulated Dental Adhesive Resins with Long-Term Antibacterial Properties.

Experimental adhesives with functional nitrogen-doped titanium dioxide nanoparticles (N_TiO) have been shown to display improved properties. However, these materials have not been characterized regarding their degree of conversion (DC), biaxial flexure strength (BFS), surface roughness (SR), elastic modulus (EM), and long-term antibacterial functionalities. Experimental adhesives were synthesized by dispersing N_TiO (10%, 20%, or 30%, /%) into OptiBond Solo Plus (OPTB, Kerr Corp.

Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence.

Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARS-CoV-2 infection from chest X-ray images.

Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified and .

The COVID-19 global pandemic caused by the widespread transmission of the novel coronavirus (SARS-CoV-2) has become one of modern history's most challenging issues from a healthcare perspective. At its dawn, still without a vaccine, contagion containment strategies remained most effective in preventing the disease's spread. Patient isolation has been primarily driven by the results of polymerase chain reaction (PCR) testing, but its initial reach was challenged by low availability and high cost, especially in developing countries.