A Polynomial-time Algorithm for Outerplanar Diameter Improvement

The Outerplanar Diameter Improvement problem asks, given a graph $G$ and an integer $D$, whether it is possible to add edges to $G$ in a way that the resulting graph is outerplanar and has diameter at most $D$. We provide a dynamic programming algorithm that solves this problem in polynomial time. Outerplanar Diameter Improvement demonstrates several structural analogues to the celebrated and challenging Planar Diameter Improvement problem, where the resulting graph should, instead, be planar. The complexity status of this latter problem is open.Comment: 24 page

Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and Sparsity Constraints

Objective: The purpose of this manuscript is to accelerate cardiac diffusion tensor imaging (CDTI) by integrating low-rankness and compressed sensing. Methods: Diffusion-weighted images exhibit both transform sparsity and low-rankness. These properties can jointly be exploited to accelerate CDTI, especially when a phase map is applied to correct for the phase inconsistency across diffusion directions, thereby enhancing low-rankness. The proposed method is evaluated both ex vivo and in vivo, and is compared to methods using either a low-rank or sparsity constraint alone. Results: Compared to using a low-rank or sparsity constraint alone, the proposed method preserves more accurate helix angle features, the transmural continuum across the myocardium wall, and mean diffusivity at higher acceleration, while yielding significantly lower bias and higher intraclass correlation coefficient. Conclusion: Low-rankness and compressed sensing together facilitate acceleration for both ex vivo and in vivo CDTI, improving reconstruction accuracy compared to employing either constraint alone. Significance: Compared to previous methods for accelerating CDTI, the proposed method has the potential to reach higher acceleration while preserving myofiber architecture features which may allow more spatial coverage, higher spatial resolution and shorter temporal footprint in the future.Comment: 11 pages, 16 figures, published on IEEE Transactions on Biomedical Engineerin

Population Pharmacokinetics and Pharmacodynamics of Extended-Infusion Piperacillin and Tazobactam in Critically Ill Children

The study objective was to evaluate the population pharmacokinetics and pharmacodynamics of extended-infusion piperacillintazobactam in children hospitalized in an intensive care unit. Seventy-two serum samples were collected at steady state from 12 patients who received piperacillin-tazobactam at 100/12.5 mg/kg of body weight every 8 h infused over 4 h. Population pharmacokinetic analyses were performed using NONMEM, and Monte Carlo simulations were performed to estimate the piperacillin pharmacokinetic profiles for dosing regimens of 80 to 100 mg/kg of the piperacillin component given every 6 to 8 h and infused over 0.5, 3, or 4 h. The probability of target attainment (PTA) for a cumulative percentage of the dosing interval that the drug concentration exceeds the MIC under steady-state pharmacokinetic conditions (TMIC) of\u3e50% was calculated at MICs ranging from 0.25 to 64 mg/liter. The mean ± standard deviation (SD) age, weight, and estimated glomerular filtration rate were 5 ± 3 years, 17 ± 6.2 kg, and 118 ± 41 ml/min/1.73m2, respectively. A one-compartment model with zero-order input and first-order elimination best fit the pharmacokinetic data for both drugs. Weight was significantly associated with piperacillin clearance, and weight and sex were significantly associated with tazobactam clearance. Pharmacokinetic parameters (mean ± SD) for piperacillin and tazobactam were as follows: clearance, 0.22 ± 0.07 and 0.19 ± 0.07 liter/h/kg, respectively; volume of distribution, 0.43 ± 0.16 and 0.37 ± 0.14 liter/kg, respectively. All extended-infusion regimens achieved PTAs of\u3e90% at MICs of/liter. Only the 3-h infusion regimens given every 6 h achieved PTAs of\u3e90% at an MIC of 32 mg/liter. For susceptible bacterial pathogens, piperacillin-tazobactam doses of\u3e80/10 mg/kg given every 8 h and infused over 4 h achieve adequate pharmacodynamic exposures in critically ill children

Multi-institution analysis of racial disparity among African- American men eligible for prostate cancer active surveillance

There is a significant controversy on whether race should be a factor in considering active surveillance for low-risk prostate cancer. To address this question, we analyzed a multi-institution database to assess racial disparity between African-American and White-American men with low risk prostate cancer who were eligible for active surveillance but underwent radical prostatectomy. A retrospective analysis of prospectively collected clinical, pathologic and oncologic outcomes of men with low-risk prostate cancer from seven tertiary care institutions that underwent radical prostatectomy from 2003–2014 were used to assess potential racial disparity. Of the 333 (14.8%) African-American and 1923 (85.2%) White-American men meeting active surveillance criteria, African-American men were found to be slightly younger (57.5 vs 58.5 years old; p = 0.01) and have higher BMI (29.3 v 27.9; p \u3c 0.01), pre-op PSA (5.2 v 4.7; p \u3c 0.01), and maximum percentage cancer on biopsy (15.1% v 13.6%; p \u3c 0.01) compared to White-American men. Univariate and multivariate analysis demonstrated similar rates of upgrading, upstaging, positive surgical margin, and biochemical recurrence between races. These results suggest that single institution studies recommending more stringent AS enrollment criteria for AA men with a low-risk prostate cancer may not capture the complete oncologic landscape due to institutional variability in cancer outcomes. Since all seven institutions demonstrated no significant racial disparity, current active surveillance eligibility should not be modified based upon race until a prospective study has been completed. © Dinizo et al

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

<p>Abstract</p> <p>Background</p> <p>Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance.</p> <p>Results</p> <p>We present a cluster-number-based ensemble clustering algorithm, called <it>MULTI-K</it>, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple <it>k</it>-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the <it>entropy-plot </it>to control the separation of singletons or small clusters. MULTI-K, unlike the simple <it>k</it>-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets.</p> <p>Conclusion</p> <p>The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.</p