ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / This paper addresses the research conducted at U.C. Berkeley Space Sciences Laboratory, Center for Extreme Ultra Violet Astrophysics between 1994 and 1995 on the NASA EUVE ion-orbit satellite. It includes the results from conducting a scientific analysis called a prognostic analysis completed on all satellite subsystem equipment. A prognostic analysis uses equipment analog telemetry to measure equipment remaining usable life. The analysis relates equipment transient behavior, often referred to as "cannot duplicates" in a variety of industries caused from accelerated aging to the equipment end-of-life with certainty. The analysis was confirmed by using proprietary, pattern recognition software by Lockheed Martin personnel Lockheed Martin personnel completed an exploration into the application of statistical pattern recognition methods to identify the behavior caused from accelerated aging that experts in probability reliability analysis claims cannot exist. Both visual and statistical methods were successful in detecting suspect accelerated aging and this behavior was related to equipment end of life with certainty. The long-term objective of this research was to confirm that satellite subsystem equipment failures could be predicted so that satellite subsystem and payload engineering personnel could be allocated for only the time that equipment failures were predicted to occur, lowering the cost of mission operations. This research concluded that satellite subsystem equipment remaining usable life could be measured and equipment failures could be predicted with certainty so that engineering support for mission operations could be greatly reduced.

The absence of gold standard criteria for mild cognitive impairment (MCI) impedes the comparison of research findings and the development of primary and secondary prevention strategies addressing the possible conversion to dementia. The objective of Study 1 was to compare the predictive ability of different MCI models as markers for incipient dementia in a longitudinal population-based Canadian sample. The utility of well-documented MCI criteria using data from persons who underwent a clinical examination in the second wave of the Canadian Study of Health and Aging (CSHA) was examined. Demographic characteristics, average neuropsychological test performance, and prevalence and conversion rates were calculated for each classification. Receiver operating characteristic (ROC) analyses were employed to assess the predictive power of each cognitive classification. The highest prevalence and conversion rates were associated with case definitions of multiple-domain MCI. The only diagnostic criteria to significantly predict dementia five years later was the Cognitive Impairment, No Dementia (CIND) Type 2 case definition. It is estimated that more restrictive MCI case definitions fail to address the varying temporal increases in decline across different cognitive domains in the progression from normal cognitive functioning to dementia. Using data from the CSHA, the objective of Study 2 was to elucidate the clinical correlates that best differentiate between cognitive classifications. A machine learning algorithm was used to identify the symptoms that best discriminated between: 1) not cognitively impaired (NCI) and CIND; 2) CIND & demented; and 3) converting and non-converting CIND participants. Poor retrieval was consistently a significant predictor of greater cognitive impairment across all three questions. While interactions with other predictors were noted when differentiating CIND from NCI and demented from non-demented participants, retrieval was the sole predictor of conversion to dementia over five years. Importantly, the limited specificity and predictive values of the respective algorithms caution against their use as clinical markers of CIND, dementia, or conversion. Rather, it is recommended that the predictors serve as markers for ongoing monitoring and assessment. Overall, the results of both studies suggest that the architecture of pathological cognitive decline to dementia may not be captured by a single set of diagnostic criteria.

The absence of gold standard criteria for mild cognitive impairment (MCI) impedes the comparison of research findings and the development of primary and secondary prevention strategies addressing the possible conversion to dementia. The objective of Study 1 was to compare the predictive ability of different MCI models as markers for incipient dementia in a longitudinal population-based Canadian sample. The utility of well-documented MCI criteria using data from persons who underwent a clinical examination in the second wave of the Canadian Study of Health and Aging (CSHA) was examined. Demographic characteristics, average neuropsychological test performance, and prevalence and conversion rates were calculated for each classification. Receiver operating characteristic (ROC) analyses were employed to assess the predictive power of each cognitive classification. The highest prevalence and conversion rates were associated with case definitions of multiple-domain MCI. The only diagnostic criteria to significantly predict dementia five years later was the Cognitive Impairment, No Dementia (CIND) Type 2 case definition. It is estimated that more restrictive MCI case definitions fail to address the varying temporal increases in decline across different cognitive domains in the progression from normal cognitive functioning to dementia. Using data from the CSHA, the objective of Study 2 was to elucidate the clinical correlates that best differentiate between cognitive classifications. A machine learning algorithm was used to identify the symptoms that best discriminated between: 1) not cognitively impaired (NCI) and CIND; 2) CIND & demented; and 3) converting and non-converting CIND participants. Poor retrieval was consistently a significant predictor of greater cognitive impairment across all three questions. While interactions with other predictors were noted when differentiating CIND from NCI and demented from non-demented participants, retrieval was the sole predictor of conversion to dementia over five years. Importantly, the limited specificity and predictive values of the respective algorithms caution against their use as clinical markers of CIND, dementia, or conversion. Rather, it is recommended that the predictors serve as markers for ongoing monitoring and assessment. Overall, the results of both studies suggest that the architecture of pathological cognitive decline to dementia may not be captured by a single set of diagnostic criteria.