A functional regression analysis of vessel source level measurements from the Enhancing Cetacean Habitat and Observation (ECHO) database.

Measurements of the source levels of 9880 passes of 3188 different large commercial ships from the Enhancing Cetacean Habitat and Observation (ECHO) program database were used to investigate the dependencies of vessel underwater noise emissions on several vessel design parameters and operating conditions. Trends in the dataset were analyzed using functional regression analysis, which is an extension of standard regression analysis and represents a response variable (decidecade band source level) as a continuous function of a predictor variable (frequency). The statistical model was applied to source level data for six vessel categories: cruise ships, container ships, bulk carriers, tankers, tugs, and vehicle carriers. Depending on the frequency band and category, the functional regression model explained approximately 25%-50% of the variance in the ECHO dataset. The two main operational parameters, speed through water and actual draft, were the predictors most strongly correlated with source levels in all of the vessel categories. Vessel size (represented via length overall) was the design parameter with the strongest correlation to underwater radiated noise for three categories of vessels (bulkers, containers, and tankers). Other design parameters that were investigated (engine revolutions per minute, engine power, design speed, and vessel age) had weaker but nonetheless significant correlations with source levels.

Developing oxygen-enhanced magnetic resonance imaging as a prognostic biomarker of radiation response.

Oxygen-Enhanced Magnetic Resonance Imaging (OE-MRI) techniques were evaluated as potential non-invasive predictive biomarkers of radiation response. Semi quantitative blood-oxygen level dependent (BOLD) and tissue oxygen level dependent (TOLD) contrast, and quantitative responses of relaxation rates (ΔR1 and ΔR2*) to an oxygen breathing challenge during hypofractionated radiotherapy were applied. OE-MRI was performed on subcutaneous Dunning R3327-AT1 rat prostate tumors (n=25) at 4.7 T prior to each irradiation (2F × 15 Gy) to the gross tumor volume. Response to radiation, while inhaling air or oxygen, was assessed by tumor growth delay measured up to four times the initial irradiated tumor volume (VQT). Radiation-induced hypoxia changes were confirmed using a double hypoxia marker assay. Inhaling oxygen during hypofractionated radiotherapy significantly improved radiation response. A correlation was observed between the difference in the 2nd and 1st ΔR1 (ΔΔR1) and VQT for air breathing rats. The TOLD response before the 2nd fraction showed a moderate correlation with VQT for oxygen breathing rats. The correlations indicate useful prognostic factors to predict tumor response to hypofractionation and could readily be applied for patient stratification and personalized radiotherapy treatment planning.

Tumor radio-sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats.

PURPOSE: Radiation therapy is one of the most common treatments in the fight against prostate cancer, since it is used to control the tumor (early stages), to slow its progression, and even to control pain (metastasis). Although many factors (e.g., tumor oxygenation) are known to influence treatment efficacy, radiotherapy doses and fractionation schedules are often prescribed according to the principle "one-fits-all," with little personalization. Therefore, the authors aim at predicting the outcome of radiation therapy a priori starting from morphologic and functional information to move a step forward in the treatment customization. METHODS: The authors propose a two-step protocol to predict the effects of radiation therapy on individual basis. First, one macroscopic mathematical model of tumor evolution was trained on tumor volume progression, measured by caliper, of eighteen Dunning R3327-AT1 bearing rats. Nine rats inhaled 100% O2 during irradiation (oxy), while the others were allowed to breathe air. Second, a supervised learning of the weight and biases of two feedforward neural networks was performed to predict the radio-sensitivity (target) from the initial volume and oxygenation-related information (inputs) for each rat group (air and oxygen breathing). To this purpose, four MRI-based indices related to blood and tissue oxygenation were computed, namely, the variation of signal intensity ΔSI in interleaved blood oxygen level dependent and tissue oxygen level dependent (IBT) sequences as well as changes in longitudinal ΔR1 and transverse ΔR2(*) relaxation rates. RESULTS: An inverse correlation of the radio-sensitivity parameter, assessed by the model, was found with respect the ΔR2(*) (-0.65) for the oxy group. A further subdivision according to positive and negative values of ΔR2(*) showed a larger average radio-sensitivity for the oxy rats with ΔR2(*)<0 and a significant difference in the two distributions (p < 0.05). Finally, a leave-one-out procedure yielded a radio-sensitivity error lower than 20% in both neural networks. CONCLUSIONS: While preliminary, these specific results suggest that subjects affected by the same pathology can benefit differently from the same irradiation modalities and support the usefulness of IBT in discriminating between different responses.

Mathematical modeling of tumor response to radiation: radio-sensitivity correlation with BOLD, TOLD, ΔR1 and ΔR2* investigated in large Dunning R3327-AT1 rat prostate tumors.

Tumor response to radiation therapy can vary highly across patients. Several factors, both tumor- and environment-specific, can influence the radio-sensitivity, one of the most well-known being hypoxia. In this work, we investigated possible correlations between the radio-sensitivity parameters determined by means of a simple mathematical model of tumor volume evolution, and the MRI-based indicators of oxygenation in Dunning R3327-AT1 rats. Prior to irradiation the rats were subjected to an oxygen-breathing challenge, which was evaluated by MRI. The tumors were administered a single irradiation dose (30 Gy), while breathing air or oxygen. Despite a poor fitting performance, the model was able to identify two different tumor volume regression patterns. Moreover, the radio-sensitivity of the oxygen-breathing group was found to correlate with the variation of the transverse relaxation rate ΔR2* (-0.89). This suggests that MRI-based indices of tumor oxygenation may provide information about radio-sensitivity.

Chain melting temperature estimation for phosphatidyl cholines by quantum mechanically derived quantitative structure property relationships.

Geometries for 62 phosphatidylcholines (PC) were optimized using the AM1 semiempirical quantum mechanical method. Results obtained from these calculations were used to calculate 463 descriptors for each molecule. Quantitative Structure Property Relationships (QSPR) were developed from these descriptors to predict chain melting temperatures (Tm) for the 41 PCs in the training set. After screening each QSPR for statistical validity, the Tm values predicted by each statistically valid QSPR were compared to corresponding Tm values extracted from the literature. The most predictive, chemically meaningful QSPR provided Tm values which agreed with literature values to within experimental error. This QSPR was used to predict Tm values for the remaining 21 PCs to provide external validation for the model. These values also agreed with literature values to within experimental error. The descriptor developed by the final QSPR was the second order average information content, a topological information-theoretical descriptor.