Identifying structural changes with unsupervised machine learning methods

Unsupervised machine learning methods are used to identify structural changes using the melting point transition in classical molecular dynamics simulations as an example application of the approach. Dimensionality reduction and clustering methods are applied to instantaneous radial distributions of atomic configurations from classical molecular dynamics simulations of metallic systems over a large temperature range. Principal component analysis is used to dramatically reduce the dimensionality of the feature space across the samples using an orthogonal linear transformation that preserves the statistical variance of the data under the condition that the new feature space is linearly independent. From there, k-means clustering is used to partition the samples into solid and liquid phases through a criterion motivated by the geometry of the reduced feature space of the samples, allowing for an estimation of the melting point transition. This pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the radial distributions are different for each phase and easily distinguishable by humans. The transition temperature estimates derived from this machine learning approach produce comparable results to other methods on similarly small system sizes. These results show that machine learning approaches can be applied to structural changes in physical systems

Nitric Oxide Bioavailability and Its Potential Relevance to the Variation in Susceptibility to the Renal and Vascular Complications in Patients With Type 2 Diabetes

OBJECTIVE—We compared the renal and systemic vascular (renovascular) response to a reduction of bioavailable nitric oxide (NO) in type 2 diabetic patients without nephropathy and of African and Caucasian heritage. RESEARCH DESIGN AND METHODS—Under euglycemic conditions, renal blood flow was determined by a constant infusion of paraminohippurate and changes in blood pressure and renal vascular resistance estimated before and after an infusion of l-Ng-monomethyl-l-arginine. RESULTS—In the African-heritage group, there was a significant fall in renal blood flow (Δ−46.0 ml/min per 1.73 m(2); P < 0.05) and rise in systolic blood pressure (Δ10.0 mmHg [95% CI 2.3–17.9]; P = 0.017), which correlated with an increase in renal vascular resistance (r(2) = 0.77; P = 0.004). CONCLUSIONS—The renal vasoconstrictive response associated with NO synthase inhibition in this study may be of relevance to the observed vulnerability to renal injury in patients of African heritage

Infections Complicating Orthotopic Liver Transplantation: A Study Emphasizing Graft-Related Septicemia

In 93 recipients of 102 orthotopic liver homografts, the incidence of bacteremia or fungemia exceeded 70%. The graft itself was usually an entry site for systemic infection after both immunologic and nonimmunologic parenchymal injury, especially if there was defective biliary drainage. The role of the homograft itself as the special infectious risk factor has prompted increased use of defunctionalized jejunal Roux limbs to reduce graft contamination. It has also stimulated very aggressive postoperative diagnostic efforts to rule out remedial mechanical complications of the transplant. © 1976, American Medical Association. All rights reserved