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A potentially underestimated source of CO2 and other greenhouse gases in agriculture

Corcimaru et al. | May 18, 2022

A potentially underestimated source of CO<sub>2</sub> and other greenhouse gases in agriculture

Here the authors investigated the role of agricultural fertilizers as potential contributors to greenhouse gas emissions. In contrast to the typical investigations that consider microbiological processes, the authors considered purely chemical processes. Based on their results they found that as much as 20.41% of all CO2 emission from land-based activities could be a result of mineral nitrogen fertilizers.

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Modelling effects of alkylamines on sea salt aerosols using the Extended Aerosols and Inorganics Model

Chang et al. | Apr 29, 2022

Modelling effects of alkylamines on sea salt aerosols using the Extended Aerosols and Inorganics Model

With monitoring of climate change and the evolving properties of the atmosphere more critical than ever, the authors of this study take sea salt aerosols into consideration. These sea salt aerosols, sourced from the bubbles found at the surface of the sea, serve as cloud condensation nuclei (CCN) and are effective for the formation of clouds, light scattering in the atmosphere, and cooling of the climate. With amines being involved in the process of CCN formation, the authors explore the effects of alkylamines on the properties of sea salt aerosols and their potential relevance to climate change.

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Prediction of molecular energy using Coulomb matrix and Graph Neural Network

Hazra et al. | Feb 01, 2022

Prediction of molecular energy using Coulomb matrix and Graph Neural Network

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.

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An analysis of junior rower performance and how it is affected by rower's features

Biller et al. | Jan 07, 2022

An analysis of junior rower performance and how it is affected by rower's features

In this study, with consideration for the increasing participation of high school students in indoor rowing, the authors analyzed World Indoor Rowing Championship data. Statistical analysis revealed two key features that can determine the performance of a rower as well as increasing competitiveness in nearly all categories considered. They conclude by offering a 2000-meter ergometer time distribution that can help junior rowers assess their current performance relative to the world competition.

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Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques

Chatterjee et al. | Oct 25, 2021

Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques

Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.

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Strain-selective in vitro and in silico structure activity relationship (SAR) of N-acyl β-lactam broad spectrum antibiotics

Poosarla et al. | Oct 19, 2021

Strain-selective <i>in vitro</i> and <i>in silico</i> structure activity relationship (SAR) of N-acyl β-lactam broad spectrum antibiotics

In this study, the authors investigate the antibacterial efficacy of penicillin G and its analogs amoxicillin, carbenicillin, piperacillin, cloxacillin, and ampicillin, against four species of bacteria. Results showed that all six penicillin-type antibiotics inhibit Staphylococcus epidermidis, Escherichia coli, and Neisseria sicca with varying degrees of efficacy but exhibited no inhibition against Bacillus cereus. Penicillin G had the greatest broad-spectrum antibacterial activity with a high radius of inhibition against S. epidermidis, E. coli, and N. sicca.

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