An analysis of appraised values and actual transaction prices in the US CMBS market

Thesis (S.M. in Real Estate Development)--Massachusetts Institute of Technology, Dept. of Architecture, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 76-77).This thesis addresses the characteristics of transaction-based indices and appraisal-based indices and compares the difference between appraisal and transaction price in the United States Commercial Mortgage-Backed Securities (CMBS) market. The examination is based on the transaction database of Real Capital Analytics, Inc (RCA). A hedonic regression model is applied to data for the period 2000-2006 to produce national indexes at the all-property, office and retail levels. The hedonic model examines the relationship between appraised value or transaction price and NOI, property characteristics, and time. The results are used to create price and appraisal indices. Moreover, the results also prove that multivariate regression analysis is a cost-effective statistical procedure for estimating property values in a time-varying approach. Despite the characteristics influence on price, the relationship between transaction and appraisal behavior is demonstrated in this article. The transaction-based index reflects the timing and changes of market price more accurately and effectively than appraisal-based index does during the examination period. Comparing two appraisal indices, the one without transactions (refinancing) is less volatile than the one with transactions (sale). The underlying reason is appraisers have more pressure when there is a transaction occurred comparing with only for refinancing deal. Therefore, they will appraise those properties with transactions higher than refinancing ones. In addition, after comparing appraisal index without transaction (refinancing) and transaction index (sale), I learn that transaction index for sure leads appraisal index at least one period due to its lagging issue. Therefore, we can predict appraisal index return based on transaction index.(cont.) These findings are very important for investors when valuing their investments. Those constructed indices can be used to track market trends and to support tradable commercial property price derivatives in the near future.by Yili Zhong Dolan.S.M.in Real Estate Developmen

Sensitivity of Mitochondrial Transcription and Resistance of RNA Polymerase II Dependent Nuclear Transcription to Antiviral Ribonucleosides

Ribonucleoside analogues have potential utility as anti-viral, -parasitic, -bacterial and -cancer agents. However, their clinical applications have been limited by off target effects. Development of antiviral ribonucleosides for treatment of hepatitis C virus (HCV) infection has been hampered by appearance of toxicity during clinical trials that evaded detection during preclinical studies. It is well established that the human mitochondrial DNA polymerase is an off target for deoxyribonucleoside reverse transcriptase inhibitors. Here we test the hypothesis that triphosphorylated metabolites of therapeutic ribonucleoside analogues are substrates for cellular RNA polymerases. We have used ribonucleoside analogues with activity against HCV as model compounds for therapeutic ribonucleosides. We have included ribonucleoside analogues containing 2′-C-methyl, 4′-methyl and 4′-azido substituents that are non-obligate chain terminators of the HCV RNA polymerase. We show that all of the anti-HCV ribonucleoside analogues are substrates for human mitochondrial RNA polymerase (POLRMT) and eukaryotic core RNA polymerase II (Pol II) in vitro. Unexpectedly, analogues containing 2′-C-methyl, 4′-methyl and 4′-azido substituents were inhibitors of POLRMT and Pol II. Importantly, the proofreading activity of TFIIS was capable of excising these analogues from Pol II transcripts. Evaluation of transcription in cells confirmed sensitivity of POLRMT to antiviral ribonucleosides, while Pol II remained predominantly refractory. We introduce a parameter termed the mitovir (mitochondrial dysfunction caused by antiviral ribonucleoside) score that can be readily obtained during preclinical studies that quantifies the mitochondrial toxicity potential of compounds. We suggest the possibility that patients exhibiting adverse effects during clinical trials may be more susceptible to damage by nucleoside analogs because of defects in mitochondrial or nuclear transcription. The paradigm reported here should facilitate development of ribonucleosides with a lower potential for toxicity

The value of diffusion-weighted imaging in assessing the ADC changes of tissues adjacent to breast carcinoma

<p>Abstract</p> <p>Background</p> <p>To define a threshold value of apparent diffusion coefficient (ADC) with which malignant breast lesions can be distinguished from benign lesions, and to evaluate the ADC change of peri-tumor tissue in breast carcinoma by echo planar-diffusion weighted imaging (EPI-DWI).</p> <p>Methods</p> <p>57 breast lesions were scanned by routine MRI and EPI-DWI. The ADC values were compared between malignant and benign lesions. The sensitivity and specificity of EPI-DWI and the threshold ADC value were evaluated by Receiver Operating Characteristic curve (ROC). The ADC values of malignant lesion and layered peri-tumor tissues (from innermost layer 1 to outermost layer 4 with 5 mm every layer) in different directions were compared and the ADC values among different layers were compared.</p> <p>Results</p> <p>The ADC value of 35 malignant lesions was statistically lower than that of 22 benign lesions (P < 0.05). In ROC curve, the threshold value was 1.24 +/- 0.25*10E-3 mm²/s (b = 500) or 1.20 +/- 0.25*10E-3 mm²/s (b = 1000). The ADC value of malignant lesions was statistically lower than that of peri-tumor tissues in different directions (P < 0.05). For peri-tumor tissues, the ADC values increased gradually from layer 1 to layer 4 and there was a significant difference between the ADC values of layer 1 and layer 2 (P < 0.05); while from layer 2 outwards, there was no statistical difference among different layers.</p> <p>Conclusion</p> <p>ADC value was a sensitive and specific parameter that could help to differentiate benign and malignant breast lesions. ADC changes in tissues adjacent to breast carcinoma could be detected by EPI-DWI, which made EPI-DWI a promising method for helping to determine surgical scope of breast carcinoma.</p

Development of hysteresis analysis as a model-independent approach to assess temporal dissociation in pharmacokinetics and pharmacodynamics

Pharmacodynamic hysteresis is a loop, typically anti-clockwise, in the effect-concentration profile, resulting from temporal dissociation between elaboration of effect and changes in drug concentration. Hysteresis behavior impairs recovery of reliable numerical estimates of pharmacodynamic parameters. Existing methods for analyzing data with a hysteresis have focused on integrated pharmacokinetic-pharmacodynamic modeling, which is based on hypothetical constructs and unverifiable assumptions. The work presented herein was aimed at developing and evaluating model-independent metrics to quantify time delays in pharmacokinetic/pharmacodynamic systems. Relationships between various descriptors of hysteresis morphology and parameters associated with distributional (pharmacokinetic) or effect (pharmacodynamic) delays were explored in silico. The ratio of x- versus y-coordinate of the hysteresis centroid was identified through simulation studies as the most useful descriptor in characterizing pharmacokinetic delays. Utility of this metric was demonstrated with mined data examining distribution of methotrexate into human brain, and indicated that hysteresis analysis can provide robust and sensitive results when traditional parametric approaches fail. The utility of hysteresis analysis in pharmacodynamic experiments was examined with simulated data generated from a multiplicity of commonly-encountered pharmacokinetic-pharmacodynamic systems, including systems with delayed distribution to the receptor target (effect-compartment systems) and delayed elaboration of effect after binding of the drug to the target (indirect response systems). In addition, the influence of the shape of the effect-versus concentration relationship (linear, hyperbolic, or sigmoidal) on hysteresis analysis was explored. Results of these experiments indicated that hysteresis analysis is most useful when the effect-concentration relationship is linear. Consistent with observations for nonlinear pharmacokinetic systems, nonlinearities in the effect-concentration profile resulted in nonlinear relationships between hysteresis descriptors and model parameters, with a consequent loss in analytical sensitivity and specificity. Results obtained with simulated data were confirmed with data mined from 12 published reports. Taken together, the results of this project indicate that the hysteresis centroid provides information useful in quantifying delays in pharmacokinetic/pharmacodynamic systems without need for underlying assumptions. In particular, the centroid is useful for hypotheses-testing, rather than descriptive, purposes. As with non-compartmental approaches for pharmacokinetic analysis based on statistical moment theory, hysteresis analysis appears to be useful only for linear systems

Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation

Considering the battery-failure-induced catastrophic events reported frequently, the early fault warning of batteries is essential to the safety of electric vehicles (EVs). Motivated by this, a novel data-driven method for early-stage battery-fault warning is proposed in this paper by the fusion of the short-text mining and the grey correlation. In particular, the short-text mining approach is exploited to identify the fault information recorded in the maintenance and service documents and further to analyze the categories of battery faults in EVs statistically. The grey correlation algorithm is employed to build the relevance between the vehicle states and typical battery faults, which contributes to extracting the key features of corresponding failures. A key fault-prediction model of electric buses based on big data is then established on the key feature variables. Different selections of kernel functions and hyperparameters are scrutinized to optimize the performance of warning. The proposed method is validated with real-world data acquired from electric buses in operation. Results suggest that the constructed prediction model can effectively predict the faults and carry out the desired early fault warning