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To best identify tuberculosis and pneumonia diagnoses in chest x-rays, the authors compare different deep learning convolution neural networks.

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.

Free radical chain reactions result when atoms containing unpaired electrons bind with biomolecules and alter their biological functions, contributing to the progression of diseases such as atherosclerosis, cancer, and diabetes. Antioxidants, such as vitamin E and sulforaphane, are effective neutralizers of free radicals and prevent cellular damage. This present study is conducted to determine the relative effectiveness of sulforaphane against free radicals generated by hydrogen peroxide (H2O2) compared with the known antioxidant vitamin E.

Cannabidiol (CBD) is a chemical extracted from cannabis and shown by some studies to alleviate the symptoms of many mental disorders, especially major depressive disorder. The authors hypothesized that chronic treatments with purified CBD through oral administration would relieve depression-associated behaviors in normal healthy rats under adverse conditions. A statistical analysis of the experimental data suggested that long-term consumption of CBD could elicit depression associated symptoms in normal rats without depression. The results imply that people should consume CBD-containing products with extreme caution and highlight the need to carefully monitor the use of CBD in health care products.

In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.

Resveratrol is a type of stillbenoid, a phenolic compound produced in plants, that is known for its anti-inflammatory and anticancer effects. Many oligomers of resveratrol have recently been isolated their bioactivities remain unknown. Here, authors compared the bioactivities of resveratrol with natural dimers (ε-viniferin and gnetin H) and trimers (suffruticosol B and C). Results provide preliminary evidence that resveratrol oligomers could be potential preventive or therapeutic agents for cancers and other immune-related diseases

In this article, the authors identify the characteristics that make a book a best-seller. Knowing what, besides content, predicts the success of a book can help publishers maximize the success of their print products.

In this study, the authors characterized the trends of cigarette use amongst people who do and don't use electronic nicotine delivery systems (or ENDS). This was done to help determine if the use of ENDS is aiding in helping smokers quit, as the data on this has been controversial. They found that use of ENDS among people either with or without previous cigarette usage were more likely to continue using cigarettes in the future. This is important information contributing to our understanding of ways to effectively (and not effectively) reduce cigarette use.

Neural networks are used throughout modern society to solve many problems commonly thought of as impossible for computers. Fountain and Rasmus designed a convolutional neural network and ran it with varying levels of training to see if consistent, accurate, and precise changes or patterns could be observed. They found that training introduced and strengthened patterns in the weights and visualizations, the patterns observed may not be consistent between all neural networks.

Studying exoplanets, or planets that orbit a star other than the Sun, is critical to a greater understanding the formation of planets and how Earth's solar system differs from others. In this study the authors analyze the transit light curves of three hot Jupiter exoplanets to ultimately determine if and how these planets have changed since their discovery.