In this study, the authors test whether providing a larger dataset of glass transition temperatures (Tg) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and Tg is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's Tg, providing valuable improvements to this useful platform.
With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.
We are looking into natural ways to help hair grow better and stronger by studying keratin synthesis in human hair follicles. The reason for conducting this research was to have the ability to control hair growth through future innovations. We wanted to answer the question: How can we find natural ways to enhance hair growth by understanding the connection with natural resources, particularly keratin dynamics? The main focus of this experiment is understanding the promotion of keratin synthesis within human hair follicles, which is important for hair development and health. While keratin is essential for the growth and strength of body tissues, including skin and hair, our research hints at its specific synthesis within hair follicles. In our research utilizing castor oil, coconut oil, a turmeric and baking soda mixture, and a sugar, honey, and lemon mixture, we hypothesize that oils, specifically coconut oil and castor oil, will enhance keratin synthesis, whereas mixtures, such as the turmeric and baking soda mixture and the sugar, honey, and lemon mixture, will result in a decrease keratin synthesis. The methods used show how different natural substances influence keratin formation within the hair follicles. The experiment involved applying natural resources to hair strands and follicles, measuring their length under the microscope daily, and assessing their health and characteristics over seven days. In summary, our research helps us understand how hair grows better. We found that using natural items like essential oils effectively alters keratin growth within the hair follicles and hair strands.
Virtual labs have been gaining popularity over the last few years, especially during the worldwide lockdown due to the COVID-19 pandemic. In this study, the suitability of virtual labs for school chemistry experiments is addressed and their effectiveness is compared to traditional physical lab experiments by focusing on physical and human resources, convenience, cost, safety, and time involved as well as topic "matter".
The authors looked at the ability of plants to transfer phosphate between each other through mycorrhizal fungi. Specifically, they looked at whether plants with excess phosphate would transfer this nutrient to other plants that had depleted levels of phosphate.
Every year, more than 30% of food products go to waste. This is approximately 1.3 billion tons of food, which is equivalent to 1.3 trillion U.S. dollars. While conventional solid waste treatments and fertilization of food waste are common, citrus fruit peels require secondary applications and advanced disposal management due to their low pH values and high antimicrobial characteristics. Since citrus fruits are well-known sources of vitamin C and antioxidants, we hypothesized that their peels also contain high amounts of vitamin C and antioxidants. In our study, five common citrus peels including grapefruit, lemon, lime, orange, and tangerine, were used to determine the amounts of vitamin C and total soluble antioxidants.
Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.
Nosocomial infections acquired in hospitals pose a risk to patients, a risk compounded by resistant microorganisms. To combat this problem, researchers have turned to bioactive compounds from medicinal plants such as the widely used neem. In the present study, researchers sought to determine the effectiveness of different neem preparations against several hospital acquired human pathogens. Neem powder in water successfully inhibited microorganism growth making it a potential agent to combat these infections.
Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.
