Fourier transform infrared spectroscopy and machine learning for Porphyromonas gingivalis detection in oral bacteria

Porphyromonas gingivalis, a Gram-negative anaerobic bacillus, is the primary pathogen in periodontitis. Herein, we cultivated strains of oral bacteria, including P. gingivalis and the oral commensal bacteria Actinomyces viscosus and Streptococcus mutans, and recorded the infrared absorption spectra of the gases released by the cultured bacteria at a resolution of 0.5 cm-1 within the wavenumber range of 500-7500 cm-1. From these spectra, we identified the infrared wavenumbers associated with characteristic absorptions in the gases released by P. gingivalis using a decision tree-based machine learning algorithm. Finally, we compared the obtained absorbance spectra of ammonia (NH3) and carbon monoxide (CO) using the HITRAN database. We observed peaks at similar positions in the P. gingivalis gases, NH3, and CO spectra. Our results suggest that P. gingivalis releases higher amounts of NH3 and CO than A. viscosus and S. mutans. Thus, combining Fourier transform infrared spectroscopy with machine learning enabled us to extract the specific wavenumber range that differentiates P. gingivalis from a vast dataset of peak intensity ratios. Our method distinguishes the gases from P. gingivalis from those of other oral bacteria and provides an effective strategy for identifying P. gingivalis in oral bacteria. Our proposed methodology could be valuable in clinical settings as a simple, noninvasive pathogen diagnosis technique.

Decision tree-based identification of Staphylococcus aureus via infrared spectral analysis of ambient gas

In this study, eight types of bacteria were cultivated, including Staphylococcus aureus. The infrared absorption spectra of the gas surrounding cultured bacteria were recorded at a resolution of 0.5 cm⁻¹ over the wavenumber range of 7500–500 cm⁻¹. From these spectra, we searched for the infrared wavenumbers at which characteristic absorptions of the gas surrounding Staphylococcus aureus could be measured. This paper reports two wavenumber regions, 6516–6506 cm⁻¹ and 2166–2158 cm⁻¹. A decision tree–based machine learning algorithm was used to search for these wavenumber regions. The peak intensity or the absorbance difference was calculated for each region, and the ratio between them was obtained. When these ratios were used as training data, decision trees were created to classify the gas surrounding Staphylococcus aureus and the gas surrounding other bacteria into different groups. These decision trees show the potential effectiveness of using absorbance measurement at two wavenumber regions in finding Staphylococcus aureus.

First vibrational investigations of N2O-H2O, N2O-(H2O)2, and (N2O)2-H2O complexes from the far to the near-infrared spectral region by neon matrix isolation and ab initio calculations

We present for the first time the investigation of water molecules complexed with dinitrogen monoxide, two abundant molecules in atmosphere, in solid neon using Fourier transform infrared (IR) spectroscopy. We identify at least three complexes from concentration effects, N₂O-H₂O, N₂O-(H₂O)₂, and (N₂O)₂-H₂O, by observation of new absorption bands close to the monomer fundamental modes from the far to the near IR region. We highlight the presence of isomers for the N₂O-H₂O complex with the help of theoretical calculations at second order Møller-Plesset (MP2) and coupled-cluster single double triple-F12a/aug-cc-pVTZ levels. The observed frequencies for the N₂O-(H₂O)₂ and (N₂O)₂-H₂O complexes are compared with MP2/aug-cc-pVTZ harmonic data. Anharmonic coupling constants have been derived from the observations of overtones and combination bands.

An improved model of homogeneous nucleation for high supersaturation conditions: aluminum vapor

A novel model of stationary nucleation, treating the thermodynamic functions of small clusters, has been built. The model is validated against the experimental data on the nucleation rate of water vapor obtained in a broad range of supersaturation values (S = 10-120), and, at high supersaturation values, it reproduces the experimental data much better than the traditional classical nucleation model. A comprehensive analysis of the nucleation of aluminum vapor with the usage of developed stationary and non-stationary nucleation models has been performed. It has been shown that, at some value of supersaturation, there exists a double potential nucleation barrier. It has been revealed that the existence of this barrier notably delayed the establishment of a stationary distribution of subcritical clusters. It has also been demonstrated that the non-stationary model of the present work and the model of liquid-droplet approximation predict different values of nucleation delay time, τ_s. In doing so, the liquid-droplet model can underestimate notably (by more than an order of magnitude) the value of τ_s.

Structure and Abundance of Nitrous Oxide Complexes in Earth's Atmosphere

We have investigated the lowest energy structures and binding energies of a series of atmospherically relevant nitrous oxide (N2O) complexes using explicitly correlated coupled cluster theory. Specifically, we have considered complexes with nitrogen (N2-N2O), oxygen (O2-N2O), argon (Ar-N2O), and water (H2O-N2O). We have calculated rotational constants and harmonic vibrational frequencies for the complexes and the constituent monomers. Statistical mechanics was used to determine the thermodynamic parameters for complex formation as a function of temperature and pressure. These results, in combination with relevant atmospheric data, were used to estimate the abundance of N2O complexes in Earth's atmosphere as a function of altitude. We find that the abundance of N2O complexes in Earth's atmosphere is small but non-negligible, and we suggest that N2O complexes may contribute to absorption of terrestrial radiation and be relevant for understanding the atmospheric fate of N2O.