Directive 02-14: Tax Obligations of Persons Purchasing Cigarettes in Interstate Commerce for which the Massachusetts Cigarette Excise Has Not Been Paid

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.</p

The Marker State Space (MSS) Method for Classifying Clinical Samples

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications. © 2013 Fallon et al

Unusual Variation in the Branching Pattern of the Unpaired Arteries of the Abdominal Aorta

Demonstrated typical anatomy of the human abdominal aorta shows that the unpaired branches the superior and inferior mesenteric arteries and the celiac trunk diverge from the aorta anteriorly, superior to the renal arteries, with slight angular variations. Presented is an example of an unusual variant of the spatial relationships of the aortic branches. While the renal arteries branch in typical fashion, the positions of the superior mesenteric artery and the celiac trunk are laterally placed on the left of the abdominal aorta. This creates a pattern where there is a close spatial relationship between the superior mesenteric artery and the left renal artery, and an extended common hepatic artery. Not only do these variants demonstrate an unusual developmental pattern, they may also present a challenge to surgeons attempting transplantation of abdominal organs such as the liver

A NORMAL COORDINATE ANALYSIS OF THE INTERMOLECULAR VIBRATIONS OF LIQUID WATER.

$^{1}$ A. H. Narten, M. D. Danford and H. A. Levy, ONRL-3997, September 1966.Author Institution: Department of Physics, Kansas State UniversityCalculations of the frequencies of hindered rotation and translation expected in liquid water have been made on the basis of a simple $C_{2\nu}$ model of one water molecule tetrahedrally hydrogen bonded to four nearest neighbors. These calculations have been extended to include the effects of the distribution of nearest neighbor hydrogen bond distances as determined by Narten, Danford and Levy from X-ray diffraction $measurements.^{1}$ The results of these calculations will be presented and related to the observed infrared spectrum of liquid water

Comparison of performance between methods.

*<p>The software did not calculate an average sensitivity and specificity for MSS in 10-fold cross validation because its does not separately calculate those parameters in each cross validation split.</p

Assigning patient classes and classifying marker states.

<p>(A) Thresholding the data. Representative data for 21 samples are presented, in which each point represents a patient sample measurement for Marker 1 (left) or Marker 2 (right). A threshold (dashed line) was applied to each marker. Values above the threshold are converted to 1 and values below the threshold are converted to 0. (B) Possible states. Each column represents a unique state for panels of 1, 2, or 3 markers. (C) Determining marker states for each patient. The data from both Marker 1 and Marker 2 are presented for each of the 21 patients, along with their respective thresholds (horizontal lines). The thresholded data are below the column graph. Each sample has a particular marker state (0,0; 0,1; 1,0; or 1,1). (D) State classification. Each state is classified as either case or control based on whether cancer or non-cancer samples have a greater number of occurrences in that state. The “true positives” are the cancer samples that occupy case states, and the “true negatives” are the non-cancer samples that occupy control states. These values are used to calculate the sensitivity and specificity for the panel.</p

Determining optimal thresholds for a two-marker panel.

<p>(A) Scanning thresholds. Three different thresholds are depicted for Marker 1 (left) and Marker 2 (right), with the resulting conversion to 1s and 0s for each threshold, followed by the sensitivities and specificities for each marker at each threshold. (B) Determining the best combination of thresholds. All possible combinations of thresholds were assembled for the two-marker panel, resulting in nine combinations. Based on the results from panel A, the numbers of cancer and non-cancer samples that occupy each state were determined for each combination, from which the sensitivity and specificity could be calculated for each combination. The combination of thresholds giving the best performance (in this case threshold 2 for Marker 1 and threshold 2 for Marker 2) is selected.</p

Test set marker states and patient classifications.

<p>The same marker panel, thresholds, and classification rules as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065905#pone-0065905-g004" target="_blank">figure 4</a> were applied to the one-third of the total samples that were separated as a test set. (A) Occupancy of the marker states in the test set. (B) Individual sample classifications in the test set.</p

Training set marker states and patient classifications.

<p>(A) Training set marker states. The eight possible marker states for the three indicated markers are shown, followed by the numbers of case and control samples in each state and the categorization of each state. *State 2 was unoccupied by categorized as a control state because of similarity to other control states. The lower panel shows condensed marker states, in which X indicates either 0 or 1. (B) Individual sample classifications. Each column represents an individual patient sample, and the first three rows indicate results from the indicated markers. A yellow square indicates the sample was above the threshold for that marker, and black indicates below. The blue lines indicate the state in which each sample was classified.</p