In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.
Read More...A novel CNN-based machine learning approach to identify skin cancers
In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.
Read More...Autologous transplantation of fresh ovarian tissue in the ICR mice model
In this study, we performed orthotopic auto-transplantation of fresh ovarian tissues by transplanting unilateral half ovarian tissue to the contralateral ovary in the ICR (Institute of Cancer Research) strain of outbred, heterogeneous mice to determine if the transplanted tissue could be functional. We found that the freshly transplanted mouse ovarian tissue survived and functional, as histochemical and immunofluorescence assays have shown that not only both follicles at different developing stages and corpus luteum are available, but the morphology of them are properly maintained within the transplanted tissue.
Read More...Validation of impact-absorbing football helmet facemask for head injury prevention with simulation
Head injuries are common in American football and the facemasks found on football helmets receive a lot of impact during contacts. This study investigates how effective they are at reducing concussion risk.
Read More...Machine learning for the diagnosis of malaria: a pilot study of transfer learning techniques
The diagnosis of malaria remains one of the major hurdles to eradicating the disease, especially among poorer populations. Here, the authors use machine learning to improve the accuracy of deep learning algorithms that automate the diagnosis of malaria using images of blood smears from patients, which could make diagnosis easier and faster for many.
Read More...Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques
Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.
Read More...A bibliometric analysis of the use of biomimetic silk conduits for treating peripheral nerve injuries
In this study, the authors conduct a bibliometric analysis to understand the recent growth in and current state of peripheral nerve regeneration research. They also explored potential future studies.
Read More...Thermoelectric Power Generation: Harnessing Solar Thermal Energy to Power an Air Conditioner
The authors test the feasibility of using thermoelectric modules as a power source and as an air conditioner to decrease reliance on fossil fuels. The results showed that, at its peak, their battery generated 27% more power – in watts per square inch – than a solar panel, and the thermoelectric air conditioner operated despite an unsteady input voltage. The battery has incredible potential, especially if its peak power output can be maintained.
Read More...Fourier-Transform Infrared (FTIR) spectroscopy analysis of seven wisconsin biosolids
The authors analyzed biosolids from five Wisconsin wastewater treatment plants and suggest using KBr pellet FTIR as a simple and rapid method to start characterizing P species in biosolids.
Read More...Slowing the Mold Growth on Stored Corn: The Effects of Vinegar, Baker’s Yeast, and Yogurt on Corn Weight Loss
Chemical preservatives are often used to reduce grain spoilage due to mold, but can have harmful heath and environmental effects. In this study, the authors tested three low toxic compounds for their effects on mold growth on corn kernels and found that all three were successful at slowing growth.
Read More...The impact of genetic analysis on the early detection of colorectal cancer
Although the 5-year survival rate for colorectal cancer is below 10%, it increases to greater than 90% if it is diagnosed early. We hypothesized from our research that analyzing non-synonymous single nucleotide variants (SNVs) in a patient's exome sequence would be an indicator for high genetic risk of developing colorectal cancer.
Read More...Search articles by title, author name, or tags