Nucleophilic aromatic substitution (SAr) is one of the most widely applied reaction classes in pharmaceutical and chemical research, providing a broadly useful platform for the modification of aromatic ring scaffolds. The generally accepted mechanism for SAr reactions involves a two-step addition-elimination sequence via a discrete, non-aromatic Meisenheimer complex. Here we use C/C kinetic isotope effect (KIE) studies and computational analyses to provide evidence that prototypical SAr reactions in fact proceed through concerted mechanisms. The KIE measurements were made possible by a new technique that leverages the high sensitivity of F as an NMR nucleus to quantitate the degree of isotopic fractionation. This sensitive technique permits the measurement of KIEs on 10 mg of natural abundance material in one overnight acquisition. As a result, it provides a practical tool for performing detailed mechanistic analyses of reactions that form or break C-F bonds.

Polarization transfer is demonstrated as a sensitive technique for the measurement of isotopic fractionation of protonated carbons at natural abundance. This method allows kinetic isotope effects (KIEs) to be determined with substantially less material or shorter acquisition time compared with traditional experiments. Computations quantitatively reproduce the KIEs in a Diels–Alder reaction and a catalytic glycosylation. The glycosylation is shown to occur by an effectively concerted mechanism.

Background: A current challenge in osteoporosis is identifying patients at risk of bone fracture. Purpose: To identify the machine learning classifiers that predict best osteoporotic bone fractures and, from the data, to highlight the imaging features and the anatomical regions that contribute most to prediction performance. Study Type: Prospective (cross-sectional) case-control study. Population: Thirty-two women with prior fragility bone fractures, of mean age = 61.6 and body mass index (BMI) = 22.7 kg/m 2 , and 60 women without fractures, of mean age = 62.3 and BMI = 21.4 kg/m 2 . Field Strength/ Sequence: 3D FLASH at 3T. Assessment: Quantitative MRI outcomes by software algorithms. Mechanical and topological microstructural parameters of the trabecular bone were calculated for five femoral regions, and added to the vector of features together with bone mineral density measurement, fracture risk assessment tool (FRAX) score, and personal characteristics such as age, weight, and height. We fitted 15 classifiers using 200 randomized cross-validation datasets. Statistical Tests: Data: Kolmogorov-Smirnov test for normality. Model Performance: sensitivity, specificity, precision, accuracy, F1-test, receiver operating characteristic curve (ROC). Two-sided t-test, with P < 0.05 for statistical significance. Results: The top three performing classifiers are RUS-boosted trees (in particular, performing best with head data, F1 = 0.64 ± 0.03), the logistic regression and the linear discriminant (both best with trochanteric datasets, F1 = 0.65 ± 0.03 and F1 = 0.67 ± 0.03, respectively). A permutation of these classifiers comprised the best three performers for four out of five anatomical datasets. After averaging across all the anatomical datasets, the score for the best performer, the boosted trees, was F1 = 0.63 ± 0.03 for All-features dataset, F1 = 0.52 ± 0.05 for the no-MRI dataset, and F1 = 0.48 ± 0.06 for the no-FRAX dataset. Data Conclusion: Of many classifiers, the RUS-boosted trees, the logistic regression, and the linear discriminant are best for predicting osteoporotic fracture. Both MRI and FRAX independently add value in identifying osteoporotic fractures. The femoral head, greater trochanter, and inter-trochanter anatomical regions within the proximal femur yielded better F1-scores for the best three classifiers. Level of Evidence: 2 Technical Efficacy: Stage 2