Comparative Study of Tumor Targeting and Biodistribution of pH (Low) Insertion Peptides (pHLIP® Peptides) Conjugated with Different Fluorescent Dyes

Purpose Acidification of extracellular space promotes tumor development, progression, and invasiveness. pH (low) insertion peptides (pHLIP® peptides) belong to the class of pH-sensitive membrane peptides, which target acidic tumors and deliver imaging and/or therapeutic agents to cancer cells within tumors. Procedures Ex vivo fluorescent imaging of tissue and organs collected at various time points after administration of different pHLIP® variants conjugated with fluorescent dyes of various polarity was performed. Methods of multivariate statistical analyses were employed to establish classification between fluorescently labeled pHLIP® variants in multidimensional space of spectral parameters. Results The fluorescently labeled pHLIP® variants were classified based on their biodistribution profile and ability of targeting of primary tumors. Also, submillimeter-sized metastatic lesions in lungs were identified by ex vivo imaging after intravenous administration of fluorescent pHLIP® peptide. Conclusions Different cargo molecules conjugated with pHLIP® peptides can alter biodistribution and tumor targeting. The obtained knowledge is essential for the design of novel pHLIP®-based diagnostic and therapeutic agents targeting primary tumors and metastatic lesions

Statistical Problems in Wireless Sensor Networks.

Wireless sensor networks (WSN) are a new technology with many applications, including environmental monitoring, surveillance, and health care. The dissertation concentrates on two critical aspects of a WSN: network design and information fusion. Our design strategy minimizes the overall network cost, explicitly incorporates sensor capabilities, and maintains coverage and connectivity constraints necessary for successful network operation. A new algorithm for local correction of sensor decisions, Local Vote Decision Fusion, is developed for the problems of target detection, localization, and tracking, and extended to multiple targets. The methodology is tested in simulations and on two case studies - an experiment involving tracking people and a project of tracking zebras. The local correction algorithm is further developed into a general framework for performance improvement for spatially correlated classifiers.Ph.D.StatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/63701/1/nkatenka_1.pd

Decreased Growth and Increased Shell Disease in Early Benthic Phase Homarus Americanus in Response to Elevated CO\u3csub\u3e2\u3c/sub\u3e

Marine calcifiers, especially those in larval and juvenile stages, are thought to be most vulnerable to ocean acidification (OA) due to the effects of carbon dioxide (CO2) on growth and calcification. However, recent evidence in lobsters is contradictory. We monitored molting activity, length, and weight in early benthic phase Homarus americanus (Milne-Edwards 1837) over 90 to 120 d under 3 targeted CO2 partial pressures ( pCO2; 400, 1000, and 2000 µatm) to determine how elevated CO2 affects growth at this life stage. Lobsters exposed to higher pCO2 over that 90 to 120 d period exhibited altered intermolt period length and decreased growth increments (length and weight). Lobsters in the elevated CO2 treatments were also more susceptible to shell disease. These results suggest juvenile lobsters may remain smaller, and thus more susceptible to predation, for a longer period of time and may be more susceptible to disease in a high CO2 ocean

Assortative mixture of English parts of speech

Network data analysis is an emerging area of study that applies quantitative analysis to complex data from a variety of application fields. Methods used in network data analysis enable visualization of relational data in the form of graphs and also yield descriptive characteristics and predictive graph models. This paper presents an application of network data analysis to the authorship attribution problem. Specifically, we show how a representation of text as a word graph produces the well documented feature sets used in authorship attribution tasks such as the word frequency model and the part-of-speech (POS) bigram model. Analysis of these models along with word graph characteristics provides insights into the English language. Particularly, analysis of the nominal assortative mixture of parts of speech, a statistic that measures the tendency of words of the same POS in the word network to be connected by an edge, reveals regular structural properties of English grammar