Our group has shown that numerous factors can influence how tissue engineered tendon constructs respond to in vitro mechanical stimulation. Although one study showed that stimulating mesenchymal stem cell (MSC)–collagen sponge constructs significantly increased construct linear stiffness and repair biomechanics, a second study showed no such effect when a collagen gel replaced the sponge. While these results suggest that scaffold material impacts the response of MSCs to mechanical stimulation, a well-designed intra-animal study was needed to directly compare the effects of type-I collagen gel versus type-I collagen sponge in regulating MSC response to a mechanical stimulus. Eight constructs from each cell line ($n=8$ cell lines) were created in specially designed silicone dishes. Four constructs were created by seeding MSCs on a type-I bovine collagen sponge, and the other four were formed by seeding MSCs in a purified bovine collagen gel. In each dish, two cell-sponge and two cell-gel constructs from each line were then mechanically stimulated once every $5min$ to a peak strain of 2.4%, for $8h∕day$ for 2 weeks. The other dish remained in an incubator without stimulation for 2 weeks. After 14 days, all constructs were failed to determine mechanical properties. Mechanical stimulation significantly improved the linear stiffness $(0.048±0.009$ versus $0.015±0.004$; $mean±SEM$ (standard error of the mean ) N/mm) and linear modulus ($0.016±0.004$ versus $0.005±0.001$; $mean±SEM$ MPa) of cell-sponge constructs. However, the same stimulus produced no such improvement in cell-gel construct properties. These results confirm that collagen sponge rather than collagen gel facilitates how cells respond to a mechanical stimulus and may be the scaffold of choice in mechanical stimulation studies to produce functional tissue engineered structures.

The potential of successful cognitive radio networks operating in TV White Spaces (and other future bands re-allocated for unlicensed operation) has led to significant upsurge of interest in their design optimization - particularly those that are cross-layer in nature, involving both MAC protocols as well as physical layer aspects such as channel sensing. Typically, these seek to optimize a network-level metric (notably, aggregate throughput) of secondary (cognitive) network subject to interference constraints on the primary. In turn, this requires suitable sensing by cognitive users to detect availability of primary channels (currently unused by the protected incumbents) for opportunistic usage. To date, most studies have used largely hypothetical assumptions regarding channel idleness and resulting spectrum availability due to primary user dynamics. For example, idleness of channels over any spectrum are typically assumed to be an independent and identically distributed Bernoulli sequence. In contrast, nearly all real-time measurements suggest that channel idleness is frequency dependent, i.e., the probability that a channel is idle depends on the channel location. Cognitive radio research thus increasingly needs more realistic and validated models for channel idleness as the foundation of credible cross-layer analysis; this is the primary contribution of our work. We use two sets of real-time measurements conducted in disparate geographic locations over four distinct time intervals to show that channel idleness is appropriately modeled as independent but non-identical (i.n.i.d.) Bernoulli variables characterized by p_i, the probability of idleness for the i-th channel. We validate that Beta distribution can be used for modeling the variations in channel idleness probabilities; the Beta distribution parameters are estimated from the data to produce the best model fit. Based on the validated i.n.i.d. model, we build a predictive model by computing the availability probability of k channels, i.e, P{N_{idle} = k}, where N_{idle} denotes the number of idle channels over the spectrum of N channels. However, the combinatorial complexity inherent in the computation of P{N_{idle} = k} suggests the need for efficient approximations. We accomplish this by classifying idleness of channels based on the magnitude of p_i, and propose a novel Poisson-normal approximation for computing P{N_{idle} = k}. For validation, the distribution obtained from our technique is compared with the exact distribution and normal approximation using the approximation error criterion.

The problem tackled is the determination of sample size for a given level and power in the context of a simple linear regression model. The standard approach deals with planned experiments in which the predictor X is observed for a number n of times and the corresponding observations on the response variable Y are to be drawn. The statistic that is used is built on the least squares’ estimator of the slope parameter. Its conditional distribution given the data on the predictor X is utilized for sample size calculations. This is problematic. The sample size n is already presaged and the data on X is fixed. In unplanned experiments, in which both X and Y are to be sampled simultaneously, we do not have data on the predictor X yet. This conundrum has been discussed in several papers and books with no solution proposed. We overcome the problem by determining the exact unconditional distribution of the test statistic in the unplanned case. We have provided tables of critical values for given levels of significance following the exact distribution. In addition, we show that the distribution of the test statistic depends only on the effect size, which is defined precisely in the paper.

Systematic Biopsy (SBx) has been and continues to be the standard staple for detecting prostate cancer. The more expensive MRI guided biopsy (MRITBx) is a better way of detecting cancer. The prostatectomy can provide an accurate condition of the prostate. The goal is to assess how reliable SBx and MRITBx are vis à vis prostatectomy. Graded Gleason scores are used for comparison. Cohen’s Kappa index and logistic regression after binarization of the graded Gleason scores are some of the methods used to achieve our goals. Machine learning methods, such as classification trees, are employed to improve predictability clinically. The Cohen’s Kappa index is 0.31 for SBx versus prostatectomy, which means a fair agreement. The index is 0.34 for MRITBx versus prostatectomy, which again means a fair agreement. A direct comparison of SBx versus prostatectomy via binarized graded scores gives sensitivity 0.83 and specificity 0.50. On the other hand, a direct comparison of MRITBx versus prostatectomy gives sensitivity 0.78 and specificity 0.67, putting MRITBx on a higher level of accuracy. The SBx and MRITBx do not yet match the findings of prostatectomy completely, but they are useful. We have developed new biomarkers, considering other pieces of information from the patients, to improve the accuracy of SBx and MRITBx. From a clinical point of view, we provide a prediction model for prostatectomy Gleason grades using classification tree methodology.