Here, the authors investigated the most efficient way to position magnets to hold the most pieces of paper on the surface of a refrigerator. They used a regression model along with an artificial neural network to identify the most efficient positions of four magnets to be at the vertices of a rectangle.

Engineered bacteria that degrade oil are currently being considered as a safe option for the treatment of oil spills. For this approach to be successful, the bacteria must effectively express oil-degrading genes they uptake as part of an external genoming vehicle called a "plasmid". Using a computational approach, the authors investigate plasmid-bacterium compatibility to find pairs that ensure high levels of gene expression.

In a world where water shortage is becoming an increasing concern, and where population increase seems inevitable, food shortage is an overwhelming concern for many. In this paper, the authors aim to characterize a drought-resistant strain of A. thaliana, investigating the cause for its water resistance. These and similar studies help us learn how plants could be engineered to improve their ability to flourish in a changing climate.

Control of voluntary and involuntary movements is one of the most important aspects of human neurological function, but the mechanisms of motor control are not completely understood. In this study, the authors use transcranial magnetic stimulation (TMS) to stimulate a portion of the motor cortex while subjects performed either discrete (e.g. throwing) or rhythmic (e.g. walking) movements. By recording electrical activity in the muscles during this process, the authors showed that motor evoked potentials (MEPs) measured in the muscles during TMS stimulation are larger in amplitude for discrete movements than for rhythmic movements. Interestingly, they also found that MEPs during transitions between rhythmic and discrete movements were nearly identical and larger in amplitude than those recorded during either rhythmic or discrete movements. This research provides important insights into the mechanisms of neurological control of movement and will serve as the foundation for future studies to learn more about temporal variability in neural activity during different movement types.

Modern artificial neural networks have been remarkably successful in various applications, from speech recognition to computer vision. However, it remains less clear whether they can implement abstract concepts, which are essential to generalization and understanding. To address this problem, the authors investigated the above vs. below task, a simple concept-based task that honeybees can solve, using a conventional neural network. They found that networks achieved 100% test accuracy when a visual target was presented below a black bar, however only 50% test accuracy when a visual target was presented below a reference shape.

Here, seeking to develop an understanding of the properties that determine the viability of piezoelectric flexible materials for applications in electro-mechanical sensors, the authors investigated the effects of the inclusion BaTiO₃ nanoparticles in electrospun Polyvinyledene Fluoride. They found the voltage generated had a piecewise linear dependence on the applied force at a few temperatures.

In this study, the authors investigated the biological mechanism underlying the actions of a traditional medicinal plant, Astragalus membranaceus. Using C. elegans as an experimental model, they tested the effects of AM root on heat stress responses. Their results suggest that AM root extract may enhance the activity of endogenous pathways that mediate cellular responses to heat stress.

Seeking an approach to address the increasing levels of methane and chlorinated hydrocarbons that threaten the environment, the authors worked to develop a novel, low-cost biotrickling filter for use as an ex situ method tailored to marine environments. By using methanotrophic bacteria in the filter, they observed methane degradation, suggesting the feasibility of chlorinated hydrocarbon degradation.

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.

In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.