A working formulation for the standardization of nomenclature and for clinical staging of chronic dysfunction in lung allografts. International Society for Heart and Lung Transplantation

A clinically applicable, standardized method for assessing functional results after lung transplantation is required to evaluate factors that may affect long-term outcome, to permit comparison of results from different centers, and to evaluate results of clinical trials. An ad hoc working group was established under the auspices of the International Society for Heart and Lung Transplantation for the purpose of developing such a clinically applicable system. Chronic allograft dysfunction is often associated with pathologic findings of obliterative bronchiolitis, the cause of which has not been defined. Physiologically, such dysfunction is associated with obstructive airways disease. The group concluded that the forced expiratory volume in 1 second was the most reliable and consistent indicator of graft dysfunction, excluding other identifiable causes. The term bronchiolitis obliterans syndrome was adopted to describe such dysfunction, recognizing that there may or may not be pathologic evidence of bronchiolitis obliterans present. Four stages of bronchiolitis obliterans syndrome were defined, each with two subcategories to indicate whether pathologic evidence of obliterative bronchiolitis had been identified. This working formulation will require reappraisal in the future to reassess its practical application and to make such adjustments as may seem appropriate.     

STABULUS: A technique for finding stable sets in large graphs with tabu search

Numerical experiments with tabu search have been carried out for constructing independent sets in large graphs. We present some variations on the independent set problem and discuss the results obtained by the tabu search technique. As for graph coloring, this method seems to be a very efficient heuristic procedure.     

A Lagrangian-based score for assessing the quality of pairwise constraints in semi-supervised clustering

Clustering algorithms help identify homogeneous subgroups from data. In some cases, additional information about the relationship among some subsets of the data exists. When using a semi-supervised clustering algorithm, an expert may provide additional information to constrain the solution based on that knowledge and, in doing so, guide the algorithm to a more useful and meaningful solution. Such additional information often takes the form of a cannot-link constraint (i.e., two data points cannot be part of the same cluster) or a must-link constraint (i.e., two data points must be part of the same cluster). A key challenge for users of such constraints in semi-supervised learning algorithms, however, is that the addition of inaccurate or conflicting constraints can decrease accuracy and little is known about how to detect whether expert-imposed constraints are likely incorrect. In the present work, we introduce a method to score each must-link and cannot-link pairwise constraint as likely incorrect. Using synthetic experimental examples and real data, we show that the resulting impact score can successfully identify individual constraints that should be removed or revised.

     

Bioinspired methodology for artificial olfaction

Artificial olfaction is a potential tool for noninvasive chemical monitoring. Application of "electronic noses" typically involves recognition of "pretrained" chemicals, while long-term operation and generalization of training to allow chemical classification of "unknown" analytes remain challenges. The latter analytical capability is critically important, as it is unfeasible to pre-expose the sensor to every analyte it might encounter. Here, we demonstrate a biologically inspired approach where the recognition and generalization problems are decoupled and resolved in a hierarchical fashion. Analyte composition is refined in a progression from general (e.g., target is a hydrocarbon) to precise (e.g., target is ethane), using highly optimized response features for each step. We validate this approach using a MEMS-based chemiresistive microsensor array. We show that this approach, a unique departure from existing methodologies in artificial olfaction, allows the recognition module to better mitigate sensor-aging effects and to better classify unknowns, enhancing the utility of chemical sensors for real-world applications.