In this study, the authors test whether providing a larger dataset of glass transition temperatures (T_g) 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 T_g 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 T_g, providing valuable improvements to this useful platform.

Human amylase is important to digestion and has broad applications for therapeutic use in patients with pancreatic insufficiency. The authors present a method to increase amylase production in E. coli by adding the amino acids L-glutamate and L-glutamine.

Many diabetics agree that the current glucometer methods are invasive, inefficient, and unsustainable for measuring blood glucose. These authors investigate the possibility of using a non-invasive glucometer patch that predicts blood glucose from patient sweat, with high accuracy.

Recognizing the increasing threat of acid deposition inn soil through the reaction of NOx and SO₂ pollutants with water in Spain, the authors investigates the effects of Al(NO₃)₃ concentrations on the health of Allium sativum. By tracking its mitotic index, they found a negative exponential correlation between Al(NO₃)₃ concentrations and the mitotic index of A. sativum.

The authors looked at 5-FU resistant HeLa cells and the ability of an herbal extract to show anti-inflammatory properties.

The use of fertilizers is associated with an increase in soil degradation, which is predicted to lead to a decrease in crop production within the next decade. Thus, it is critical to find solutions to support crop production to sustain the robust global population. In this study, the authors investigate how probiotic bacteria, like Rhizobium leguminosarum, Bacillus subtilis and Pseudomonas fluorescens, can impact the growth of Arabidopsis thaliana when applied to the seeds.They hypothesized that solutions with multiple bacterial species compared to those with only a single bacterial species would promote seed germination more effectively.

Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.

Neural machine translation (NMT) is a software that uses neural network techniques to translate text from one language to another. However, one of the most famous NMT models—Google Translate—failed to give an accurate English translation of a famous Korean nursery rhyme, "Airplane" (비행기). The authors fine-tuned a pre-trained model first with a dataset from the lyrics domain, and then with a smaller dataset containing the rhythmical properties, to teach the model to translate rhythmically accurate lyrics. This stacked fine-tuning method resulted in an NMT model that could maintain the rhythmical characteristics of lyrics during translation while single fine-tuned models failed to do so.

In this study, the authors test different infill patterns to determine which would be the strongest and most durable for 3D printing applications, which have become an integral part of many facets of life.

Vibrio fischeri is an amazing species of bacteria that lives symbiotically in the light organ of luminescent bobtail squid. In this study, authors study the strength and optimal conditions for V. fischeri light production, and assess whether this luminescence could be a natural light source comparable to manmade lighting.