In recent years, usage of interactive electronic devices such as computers, smartphones, and tablets has increased dramatically. Many studies have examined the potential adverse effects of excessive usage of such devices on children and adolescents, but the effects on adults are not well understood. In this study, the authors examined the relationship between adult usage of interactive electronic devices and a variety of clinical measures of psychological well-being. They found that according to some metrics, higher usage of interactive electronic devices is associated with several adverse psychological outcomes, suggesting a need for more careful consideration of such usage patterns in clinical settings.

Stem cells are at the forefront of research in regenerative medicine and cell therapy. Two essential properties of stem cells are self-renewal and potency, having the ability to specialize into different types of cells. Here, Park and Jeong took advantage of previously identified stem cell transcription factors associated with potency to differentiate umbilical cord mesenchymal stem cells (US-MSCs) from morphologically similar fibroblasts. Western blot analysis of the transcription factors Klf4, Nanog, and Sox2 revealed their expression was unique to US-MSCs providing insight for future methods of differentiating between these cell lines.

Machine learning algorithms are becoming increasingly popular for data crunching across a vast area of scientific disciplines. Here, the authors compare two machine learning algorithms with respect to accuracy and user-friendliness and find that random forest algorithms outperform logistic regression when applied to the same dataset.

The authors explore hearing students' misconceptions about the Deaf and Hard of Hearing (HoH) community. Results indicate that some misconceptions are more common than others, and that personal experience with individuals in the Deaf and HoH community reduces the frequency of such misconceptions.

In this study, the authors determined whether tautomerization dynamics in protic and aprotic solvents displayed differences in reaction rates and in the proportion of the keto and enol tautomers present.

In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.

In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!

Human immunodeficiency virus (HIV), which affects tens of millions of individuals worldwide, can lead to acquired immunodeficiency syndrome (AIDS). While there is currently no cure for HIV, the development of small molecule antiretroviral agents has greatly improved the prognosis of infected individuals, especially in developed countries. Here, the authors employ homology modeling and molecular docking towards the identification of novel rilpivirine analogs that retain high binding affinity to clinically relevant rilpivirine-resistant mutations of the HIV reverse transcriptase enzyme.

The authors looked at the ability of machine learning algorithms to interpret language given their increasing use in moderating content on social media. Using an explainable model they were able to achieve 81% accuracy in detecting fake vs. real news based on language of posts alone.

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.