Watson–Crick and Sugar-Edge Base Pairing of Cytosine in the Gas Phase: UV and Infrared Spectra of Cytosine·2-Pyridone

While keto-amino cytosine is the dominant species in aqueous solution, spectroscopic studies in molecular beams and in noble gas matrices show that other cytosine tautomers prevail in apolar environments. Each of these offers two or three H-bonding sites (Watson–Crick, wobble, sugar-edge). The mass- and isomer-specific S1 ← S0 vibronic spectra of cytosine·2-pyridone (Cyt·2PY) and 1-methylcytosine·2PY are measured using UV laser resonant two-photon ionization (R2PI), UV/UV depletion, and IR depletion spectroscopy. The UV spectra of the Watson–Crick and sugar-edge isomers of Cyt·2PY are separated using UV/UV spectral hole-burning. Five different isomers of Cyt·2PY are observed in a supersonic beam. We show that the Watson–Crick and sugar-edge dimers of keto-amino cytosine with 2PY are the most abundant in the beam, although keto-amino-cytosine is only the third most abundant tautomer in the gas phase. We identify the different isomers by combining three different diagnostic tools: (1) methylation of the cytosine N1–H group prevents formation of both the sugar-edge and wobble isomers and gives the Watson–Crick isomer exclusively. (2) The calculated ground state binding and dissociation energies, relative gas-phase abundances, excitation and the ionization energies are in agreement with the assignment of the dominant Cyt·2PY isomers to the Watson–Crick and sugar-edge complexes of keto-amino cytosine. (3) The comparison of calculated ground state vibrational frequencies to the experimental IR spectra in the carbonyl stretch and NH/OH/CH stretch ranges strengthen this identification

Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment

Background: The extreme flexibility of the HIV type-1 (HIV-1) genome makes it challenging to build the ideal antiretroviral treatment regimen. Interpretation of HIV-1 genotypic drug resistance is evolving from rule-based systems guided by expert opinion to data-driven engines developed through machine learning methods. Methods: The aim of the study was to investigate linear and non-linear statistical learning models for classifying short-term virological outcome of antiretroviral treatment. To optimize the model, different feature selection methods were considered. Robust extra-sample error estimation and different loss functions were used to assess model performance. The results were compared with widely used rule-based genotypic interpretation systems (Stanford HIVdb, Rega and ANRS). Results: A set of 3,143 treatment change episodes were extracted from the EuResist database. The dataset included patient demographics, treatment history and viral genotypes. A logistic regression model using high order interaction variables performed better than rulebased genotypic interpretation systems (accuracy 75.63% versus 71.74–73.89%, area under the receiver operating characteristic curve [AUC] 0.76 versus 0.68–0.70) and was equivalent to a random forest model (accuracy 76.16%, AUC 0.77). However, when rule-based genotypic interpretation systems were coupled with additional patient attributes, and the combination was provided as input to the logistic regression model, the performance increased significantly, becoming comparable to the fully data-driven methods. Conclusions: Patient-derived supplementary features significantly improved the accuracy of the prediction of response to treatment, both with rule-based and datadriven interpretation systems. Fully data-driven models derived from large-scale data sources show promise as antiretroviral treatment decision support tools