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1. Discovering Electric Vehicle Charging Locations Based on Clustering Techniques Applied to Vehicular Mobility Datasets

Author

Elmer Magsino, Francis Miguel M. Espiritu, and Kerwin D. Go

Subjects
clustering techniques, vehicular mobility datasets, silhouette and Calinski–Harabasz indices, spatiotemporal stable network characteristics, Geography (General), G1-922
Abstract

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as the deployment of electric vehicle (EV) charging stations. As more EVs are plying today’s roads, the driving anxiety is minimized with the presence of sufficient charging stations. By correctly extracting the various transportation parameters from a given dataset, one can design an adequate and adaptive EV charging network that can provide comfort and convenience for the movement of people and goods from one point to another. In this study, we determined the possible EV charging station locations based on an urban city’s vehicular capacity distribution obtained from taxi and ride-hailing mobility GPS traces. To achieve this, we first transformed the dynamic vehicular environment based on vehicular capacity into its equivalent urban single snapshot. We then obtained the various traffic zone distributions by initially utilizing k-means clustering to allow flexibility in the total number of wanted traffic zones in each dataset. In each traffic zone, iterative clustering techniques employing Density-based Spatial Clustering of Applications with Noise (DBSCAN) or clustering by fast search and find of density peaks (CFS) revealed various area separation where EV chargers were needed. Finally, to find the exact location of the EV charging station, we last ran k-means to locate centroids, depending on the constraint on how many EV chargers were needed. Extensive simulations revealed the strengths and weaknesses of the clustering methods when applied to our datasets. We utilized the silhouette and Calinski–Harabasz indices to measure the validity of cluster formations. We also measured the inter-station distances to understand the closeness of the locations of EV chargers. Our study shows how CFS + k-means clustering techniques are able to pinpoint EV charger locations. However, when utilizing DBSCAN initially, the results did not present any notable outcome.

Published
2024
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2. Comparison of sporadic and familial behavioral variant frontotemporal dementia (FTD) in a North American cohort

Author

Heuer, Hilary W, Wang, P, Rascovsky, K, Wolf, A, Appleby, B, Bove, J, Bordelon, Y, Brannelly, P, Brushaber, DE, Caso, C, Coppola, G, Dickerson, B, Dickinson, S, Domoto‐Reilly, K, Faber, K, Ferrall, J, Fields, J, Fishman, A, Fong, J, Foroud, T, Forsberg, LK, Gearhart, D, Ghazanfari, B, Ghoshal, N, Goldman, J, Graff‐Radford, J, Graff‐Radford, N, Grant, I, Grossman, M, Haley, D, Hsiung, G‐Y, Huey, E, Irwin, D, Jones, D, Kantarci, K, Karydas, A, Kaufer, D, Kerwin, D, Knopman, D, Kornak, J, Kramer, JH, Kraft, R, Kremers, WK, Kukull, W, Litvan, I, Ljubenkov, P, Mackenzie, IR, Maldonado, M, Manoochehri, M, McGinnis, S, McKinley, E, Mendez, MF, Miller, BL, Onyike, C, Pantelyat, A, Pearlman, R, Petrucelli, L, Potter, M, Rademakers, R, Ramos, EM, Rankin, KP, Roberson, ED, Rogalski, E, Sengdy, P, Shaw, L, Syrjanen, J, Tartaglia, MC, Tatton, N, Taylor, J, Toga, A, Trojanowski, J, Weintraub, S, Wong, B, Wszolek, Z, Boeve, BF, Rosen, HJ, Boxer, AL, and consortia, on behalf of the ARTFL and LEFFTDS

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Aging, Behavioral and Social Science, Rare Diseases, Alzheimer's Disease Related Dementias (ADRD), Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Genetics, Neurodegenerative, Dementia, Clinical Research, Acquired Cognitive Impairment, Brain Disorders, Frontotemporal Dementia (FTD), Neurological, Age Factors, Aged, Brain, C9orf72 Protein, Female, Frontotemporal Dementia, Genetic Predisposition to Disease, Humans, Male, Middle Aged, Mutation, Neuropsychological Tests, North America, Progranulins, tau Proteins, bvFTD, C9orf72, clinical trials, frontotemporal dementia, genetics, GRN, MAPT, ARTFL and LEFFTDS consortia, Geriatrics, Clinical sciences, Biological psychology
Abstract

IntroductionBehavioral variant frontotemporal dementia (bvFTD) may present sporadically or due to an autosomal dominant mutation. Characterization of both forms will improve understanding of the generalizability of assessments and treatments.MethodsA total of 135 sporadic (s-bvFTD; mean age 63.3 years; 34% female) and 99 familial (f-bvFTD; mean age 59.9; 48% female) bvFTD participants were identified. f-bvFTD cases included 43 with known or presumed chromosome 9 open reading frame 72 (C9orf72) gene expansions, 28 with known or presumed microtubule-associated protein tau (MAPT) mutations, 14 with known progranulin (GRN) mutations, and 14 with a strong family history of FTD but no identified mutation.ResultsParticipants with f-bvFTD were younger and had earlier age at onset. s-bvFTD had higher total Neuropsychiatric Inventory Questionnaire (NPI-Q) scores due to more frequent endorsement of depression and irritability.Discussionf-bvFTD and s-bvFTD cases are clinically similar, suggesting the generalizability of novel biomarkers, therapies, and clinical tools developed in either form to the other.

Published
2020

3. Active lifestyles moderate clinical outcomes in autosomal dominant frontotemporal degeneration

Author

Casaletto, KB, Staffaroni, AM, Wolf, A, Appleby, B, Brushaber, D, Coppola, G, Dickerson, B, Domoto‐Reilly, K, Elahi, FM, Fields, J, Fong, JC, Forsberg, L, Ghoshal, N, Graff‐Radford, N, Grossman, M, Heuer, HW, Hsiung, G‐Y, Huey, ED, Irwin, D, Kantarci, K, Kaufer, D, Kerwin, D, Knopman, D, Kornak, J, Kramer, JH, Litvan, I, Mackenzie, IR, Mendez, M, Miller, B, Rademakers, R, Ramos, EM, Rascovsky, K, Roberson, ED, Syrjanen, JA, Tartaglia, MC, Weintraub, S, Boeve, B, Boxer, AL, Rosen, H, Yaffe, K, and Study, the ARTFL LEFFTDS

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Basic Behavioral and Social Science, Rare Diseases, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Neurodegenerative, Clinical Research, Brain Disorders, Prevention, Frontotemporal Dementia (FTD), Behavioral and Social Science, Biomedical Imaging, Alzheimer's Disease Related Dementias (ADRD), Alzheimer's Disease, Neurosciences, Acquired Cognitive Impairment, Dementia, Neurological, Aged, Atrophy, Cognition, Exercise, Female, Frontotemporal Lobar Degeneration, Humans, Leisure Activities, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, cognitive activity, cognitive reserve, exercise, frontotemporal dementia, physical activity, ARTFL/LEFFTDS Study, Clinical Sciences, Geriatrics, Clinical sciences, Biological psychology
Abstract

IntroductionLeisure activities impact brain aging and may be prevention targets. We characterized how physical and cognitive activities relate to brain health for the first time in autosomal dominant frontotemporal lobar degeneration (FTLD).MethodsA total of 105 mutation carriers (C9orf72/MAPT/GRN) and 69 non-carriers reported current physical and cognitive activities at baseline, and completed longitudinal neurobehavioral assessments and brain magnetic resonance imaging (MRI) scans.ResultsGreater physical and cognitive activities were each associated with an estimated >55% slower clinical decline per year among dominant gene carriers. There was also an interaction between leisure activities and frontotemporal atrophy on cognition in mutation carriers. High-activity carriers with frontotemporal atrophy (-1 standard deviation/year) demonstrated >two-fold better cognitive performances per year compared to their less active peers with comparable atrophy rates.DiscussionActive lifestyles were associated with less functional decline and moderated brain-to-behavior relationships longitudinally. More active carriers "outperformed" brain volume, commensurate with a cognitive reserve hypothesis. Lifestyle may confer clinical resilience, even in autosomal dominant FTLD.

Published
2020

4. Discovering Electric Vehicle Charging Locations Based on Clustering Techniques Applied to Vehicular Mobility Datasets.

Author

Magsino, Elmer, Espiritu, Francis Miguel M., and Go, Kerwin D.

Subjects
ELECTRIC vehicle charging stations, K-means clustering, PHYSICAL distribution of goods, CENTROID, SMARTPHONES, ELECTRIC vehicles
Abstract

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as the deployment of electric vehicle (EV) charging stations. As more EVs are plying today's roads, the driving anxiety is minimized with the presence of sufficient charging stations. By correctly extracting the various transportation parameters from a given dataset, one can design an adequate and adaptive EV charging network that can provide comfort and convenience for the movement of people and goods from one point to another. In this study, we determined the possible EV charging station locations based on an urban city's vehicular capacity distribution obtained from taxi and ride-hailing mobility GPS traces. To achieve this, we first transformed the dynamic vehicular environment based on vehicular capacity into its equivalent urban single snapshot. We then obtained the various traffic zone distributions by initially utilizing k-means clustering to allow flexibility in the total number of wanted traffic zones in each dataset. In each traffic zone, iterative clustering techniques employing Density-based Spatial Clustering of Applications with Noise (DBSCAN) or clustering by fast search and find of density peaks (CFS) revealed various area separation where EV chargers were needed. Finally, to find the exact location of the EV charging station, we last ran k-means to locate centroids, depending on the constraint on how many EV chargers were needed. Extensive simulations revealed the strengths and weaknesses of the clustering methods when applied to our datasets. We utilized the silhouette and Calinski–Harabasz indices to measure the validity of cluster formations. We also measured the inter-station distances to understand the closeness of the locations of EV chargers. Our study shows how CFS + k-means clustering techniques are able to pinpoint EV charger locations. However, when utilizing DBSCAN initially, the results did not present any notable outcome. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Prognostic relevance of gait-related cognitive functions for dementia conversion in amnestic mild cognitive impairment

Author

Tuena, C, Maestri, S, Serino, S, Pedroli, E, Stramba-Badiale, M, Riva, G, Silbert, L, Lind, B, Crissey, R, Kaye, J, Carter, R, Dolen, S, Quinn, J, Schneider, L, Pawluczyk, S, Becerra, M, Teodoro, L, Dagerman, K, Spann, B, Brewer, J, Fleisher, A, Vanderswag, H, Ziolkowski, J, Heidebrink, J, Zbizek-Nulph, L, Lord, J, Albers, C, Petersen, R, Mason, S, Knopman, D, Johnson, K, Villanueva-Meyer, J, Pavlik, V, Pacini, N, Lamb, A, Kass, J, Doody, R, Shibley, V, Chowdhury, M, Rountree, S, Dang, M, Stern, Y, Honig, L, Mintz, A, Ances, B, Morris, J, Winkfield, D, Carroll, M, Stobbs-Cucchi, G, Oliver, A, Creech, M, Mintun, M, Schneider, S, Geldmacher, D, Love, M, Griffith, R, Clark, D, Brockington, J, Marson, D, Grossman, H, Goldstein, M, Greenberg, J, Mitsis, E, Shah, R, Lamar, M, Samuels, P, Duara, R, Greig-Custo, M, Rodriguez, R, Albert, M, Onyike, C, Farrington, L, Rudow, S, Brichko, R, Kielb, S, Smith, A, Raj, B, Fargher, K, Sadowski, M, Wisniewski, T, Shulman, M, Faustin, A, Rao, J, Castro, K, Ulysse, A, Chen, S, Doraiswamy, P, Petrella, J, James, O, Wong, T, Borges-Neto, S, Karlawish, J, Wolk, D, Vaishnavi, S, Clark, C, Arnold, S, Smith, C, Jicha, G, Khouli, R, Raslau, F, Lopez, O, Oakley, M, Simpson, D, Porsteinsson, A, Martin, K, Kowalski, N, Keltz, M, Goldstein, B, Makino, K, Ismail, M, Brand, C, Thai, G, Pierce, A, Yanez, B, Sosa, E, Witbracht, M, Kelley, B, Nguyen, T, Womack, K, Mathews, D, Quiceno, M, Levey, A, Lah, J, Hajjar, I, Burns, J, Swerdlow, R, Brooks, W, Silverman, D, Kremen, S, Apostolova, L, Tingus, K, Lu, P, Bartzokis, G, Woo, E, Teng, E, Graff-Radford, N, Parfitt, F, Poki-Walker, K, Farlow, M, Hake, A, Matthews, B, Brosch, J, Herring, S, van Dyck, C, Mecca, A, Good, S, Macavoy, M, Carson, R, Varma, P, Chertkow, H, Vaitekunas, S, Hosein, C, Black, S, Stefanovic, B, Heyn, C, Hsiung, G, Kim, E, Mudge, B, Sossi, V, Feldman, H, Assaly, M, Finger, E, Pasternak, S, Rachinsky, I, Kertesz, A, Drost, D, Rogers, J, Grant, I, Muse, B, Rogalski, E, Robson, J, Mesulam, M, Kerwin, D, Wu, C, Johnson, N, Lipowski, K, Weintraub, S, Bonakdarpour, B, Pomara, N, Hernando, R, Sarrael, A, Rosen, H, Miller, B, Weiner, M, Perry, D, Turner, R, Reynolds, B, Mccann, K, Poe, J, Marshall, G, Sperling, R, Yesavage, J, Taylor, J, Chao, S, Coleman, J, White, J, Lane, B, Rosen, A, Tinklenberg, J, Belden, C, Atri, A, Clark, K, Zamrini, E, Sabbagh, M, Killiany, R, Stern, R, Mez, J, Kowall, N, Budson, A, Obisesan, T, Ntekim, O, Wolday, S, Khan, J, Nwulia, E, Nadarajah, S, Lerner, A, Ogrocki, P, Tatsuoka, C, Fatica, P, Fletcher, E, Maillard, P, Olichney, J, Decarli, C, Carmichael, O, Bates, V, Capote, H, Rainka, M, Borrie, M, Lee, T, Bartha, R, Johnson, S, Asthana, S, Carlsson, C, Perrin, A, Burke, A, Scharre, D, Kataki, M, Tarawneh, R, Hart, D, Zimmerman, E, Celmins, D, Miller, D, Ponto, L, Smith, K, Koleva, H, Shim, H, Nam, K, Schultz, S, Williamson, J, Craft, S, Cleveland, J, Yang, M, Sink, K, Ott, B, Drake, J, Tremont, G, Daiello, L, Ritter, A, Bernick, C, Munic, D, O'Connelll, A, Mintzer, J, Wiliams, A, Masdeu, J, Shi, J, Garcia, A, Newhouse, P, Potkin, S, Salloway, S, Malloy, P, Correia, S, Kittur, S, Pearlson, G, Blank, K, Anderson, K, Flashman, L, Seltzer, M, Hynes, M, Santulli, R, Relkin, N, Chiang, G, Lee, A, Lin, M, Ravdin, L, Tuena C., Maestri S., Serino S., Pedroli E., Stramba-Badiale M., Riva G., Silbert L. C., Lind B., Crissey R., Kaye J. A., Carter R., Dolen S., Quinn J., Schneider L. S., Pawluczyk S., Becerra M., Teodoro L., Dagerman K., Spann B. M., Brewer J., Fleisher A., Vanderswag H., Ziolkowski J., Heidebrink J. L., Zbizek-Nulph L., Lord J. L., Albers C. S., Petersen R., Mason S. S., Knopman D., Johnson K., Villanueva-Meyer J., Pavlik V., Pacini N., Lamb A., Kass J. S., Doody R. S., Shibley V., Chowdhury M., Rountree S., Dang M., Stern Y., Honig L. S., Mintz A., Ances B., Morris J. C., Winkfield D., Carroll M., Stobbs-Cucchi G., Oliver A., Creech M. L., Mintun M. A., Schneider S., Geldmacher D., Love M. N., Griffith R., Clark D., Brockington J., Marson D., Grossman H., Goldstein M. A., Greenberg J., Mitsis E., Shah R. C., Lamar M., Samuels P., Duara R., Greig-Custo M. T., Rodriguez R., Albert M., Onyike C., Farrington L., Rudow S., Brichko R., Kielb S., Smith A., Raj B. A., Fargher K., Sadowski M., Wisniewski T., Shulman M., Faustin A., Rao J., Castro K. M., Ulysse A., Chen S., Doraiswamy P. M., Petrella J. R., James O., Wong T. Z., Borges-Neto S., Karlawish J. H., Wolk D. A., Vaishnavi S., Clark C. M., Arnold S. E., Smith C. D., Jicha G. A., Khouli R. E., Raslau F. D., Lopez O. L., Oakley M. A., Simpson D. M., Porsteinsson A. P., Martin K., Kowalski N., Keltz M., Goldstein B. S., Makino K. M., Ismail M. S., Brand C., Thai G., Pierce A., Yanez B., Sosa E., Witbracht M., Kelley B., Nguyen T., Womack K., Mathews D., Quiceno M., Levey A. I., Lah J. J., Hajjar I., Burns J. M., Swerdlow R. H., Brooks W. M., Silverman D. H. S., Kremen S., Apostolova L., Tingus K., Lu P. H., Bartzokis G., Woo E., Teng E., Graff-Radford N. R., Parfitt F., Poki-Walker K., Farlow M. R., Hake A. M., Matthews B. R., Brosch J. R., Herring S., van Dyck C. H., Mecca A. P., Good S. P., MacAvoy M. G., Carson R. E., Varma P., Chertkow H., Vaitekunas S., Hosein C., Black S., Stefanovic B., Heyn C., Hsiung G. -Y. R., Kim E., Mudge B., Sossi V., Feldman H., Assaly M., Finger E., Pasternak S., Rachinsky I., Kertesz A., Drost D., Rogers J., Grant I., Muse B., Rogalski E., Robson J., Mesulam M. -M., Kerwin D., Wu C. -K., Johnson N., Lipowski K., Weintraub S., Bonakdarpour B., Pomara N., Hernando R., Sarrael A., Rosen H. J., Miller B. L., Weiner M. W., Perry D., Turner R. S., Reynolds B., MCCann K., Poe J., Marshall G. A., Sperling R. A., Johnson K. A., Yesavage J., Taylor J. L., Chao S., Coleman J., White J. D., Lane B., Rosen A., Tinklenberg J., Belden C. M., Atri A., Clark K. A., Zamrini E., Sabbagh M., Killiany R., Stern R., Mez J., Kowall N., Budson A. E., Obisesan T. O., Ntekim O. E., Wolday S., Khan J. I., Nwulia E., Nadarajah S., Lerner A., Ogrocki P., Tatsuoka C., Fatica P., Fletcher E., Maillard P., Olichney J., DeCarli C., Carmichael O., Bates V., Capote H., Rainka M., Borrie M., Lee T. -Y., Bartha R., Johnson S., Asthana S., Carlsson C. M., Perrin A., Burke A., Scharre D. W., Kataki M., Tarawneh R., Hart D., Zimmerman E. A., Celmins D., Miller D. D., Ponto L. L. B., Smith K. E., Koleva H., Shim H., Nam K. W., Schultz S. K., Williamson J. D., Craft S., Cleveland J., Yang M., Sink K. M., Ott B. R., Drake J., Tremont G., Daiello L. A., Drake J. D., Ritter A., Bernick C., Munic D., O'Connelll A., Mintzer J., Wiliams A., Masdeu J., Shi J., Garcia A., Newhouse P., Potkin S., Salloway S., Malloy P., Correia S., Kittur S., Pearlson G. D., Blank K., Anderson K., Flashman L. A., Seltzer M., Hynes M. L., Santulli R. B., Relkin N., Chiang G., Lee A., Lin M., Ravdin L., Tuena, C, Maestri, S, Serino, S, Pedroli, E, Stramba-Badiale, M, Riva, G, Silbert, L, Lind, B, Crissey, R, Kaye, J, Carter, R, Dolen, S, Quinn, J, Schneider, L, Pawluczyk, S, Becerra, M, Teodoro, L, Dagerman, K, Spann, B, Brewer, J, Fleisher, A, Vanderswag, H, Ziolkowski, J, Heidebrink, J, Zbizek-Nulph, L, Lord, J, Albers, C, Petersen, R, Mason, S, Knopman, D, Johnson, K, Villanueva-Meyer, J, Pavlik, V, Pacini, N, Lamb, A, Kass, J, Doody, R, Shibley, V, Chowdhury, M, Rountree, S, Dang, M, Stern, Y, Honig, L, Mintz, A, Ances, B, Morris, J, Winkfield, D, Carroll, M, Stobbs-Cucchi, G, Oliver, A, Creech, M, Mintun, M, Schneider, S, Geldmacher, D, Love, M, Griffith, R, Clark, D, Brockington, J, Marson, D, Grossman, H, Goldstein, M, Greenberg, J, Mitsis, E, Shah, R, Lamar, M, Samuels, P, Duara, R, Greig-Custo, M, Rodriguez, R, Albert, M, Onyike, C, Farrington, L, Rudow, S, Brichko, R, Kielb, S, Smith, A, Raj, B, Fargher, K, Sadowski, M, Wisniewski, T, Shulman, M, Faustin, A, Rao, J, Castro, K, Ulysse, A, Chen, S, Doraiswamy, P, Petrella, J, James, O, Wong, T, Borges-Neto, S, Karlawish, J, Wolk, D, Vaishnavi, S, Clark, C, Arnold, S, Smith, C, Jicha, G, Khouli, R, Raslau, F, Lopez, O, Oakley, M, Simpson, D, Porsteinsson, A, Martin, K, Kowalski, N, Keltz, M, Goldstein, B, Makino, K, Ismail, M, Brand, C, Thai, G, Pierce, A, Yanez, B, Sosa, E, Witbracht, M, Kelley, B, Nguyen, T, Womack, K, Mathews, D, Quiceno, M, Levey, A, Lah, J, Hajjar, I, Burns, J, Swerdlow, R, Brooks, W, Silverman, D, Kremen, S, Apostolova, L, Tingus, K, Lu, P, Bartzokis, G, Woo, E, Teng, E, Graff-Radford, N, Parfitt, F, Poki-Walker, K, Farlow, M, Hake, A, Matthews, B, Brosch, J, Herring, S, van Dyck, C, Mecca, A, Good, S, Macavoy, M, Carson, R, Varma, P, Chertkow, H, Vaitekunas, S, Hosein, C, Black, S, Stefanovic, B, Heyn, C, Hsiung, G, Kim, E, Mudge, B, Sossi, V, Feldman, H, Assaly, M, Finger, E, Pasternak, S, Rachinsky, I, Kertesz, A, Drost, D, Rogers, J, Grant, I, Muse, B, Rogalski, E, Robson, J, Mesulam, M, Kerwin, D, Wu, C, Johnson, N, Lipowski, K, Weintraub, S, Bonakdarpour, B, Pomara, N, Hernando, R, Sarrael, A, Rosen, H, Miller, B, Weiner, M, Perry, D, Turner, R, Reynolds, B, Mccann, K, Poe, J, Marshall, G, Sperling, R, Yesavage, J, Taylor, J, Chao, S, Coleman, J, White, J, Lane, B, Rosen, A, Tinklenberg, J, Belden, C, Atri, A, Clark, K, Zamrini, E, Sabbagh, M, Killiany, R, Stern, R, Mez, J, Kowall, N, Budson, A, Obisesan, T, Ntekim, O, Wolday, S, Khan, J, Nwulia, E, Nadarajah, S, Lerner, A, Ogrocki, P, Tatsuoka, C, Fatica, P, Fletcher, E, Maillard, P, Olichney, J, Decarli, C, Carmichael, O, Bates, V, Capote, H, Rainka, M, Borrie, M, Lee, T, Bartha, R, Johnson, S, Asthana, S, Carlsson, C, Perrin, A, Burke, A, Scharre, D, Kataki, M, Tarawneh, R, Hart, D, Zimmerman, E, Celmins, D, Miller, D, Ponto, L, Smith, K, Koleva, H, Shim, H, Nam, K, Schultz, S, Williamson, J, Craft, S, Cleveland, J, Yang, M, Sink, K, Ott, B, Drake, J, Tremont, G, Daiello, L, Ritter, A, Bernick, C, Munic, D, O'Connelll, A, Mintzer, J, Wiliams, A, Masdeu, J, Shi, J, Garcia, A, Newhouse, P, Potkin, S, Salloway, S, Malloy, P, Correia, S, Kittur, S, Pearlson, G, Blank, K, Anderson, K, Flashman, L, Seltzer, M, Hynes, M, Santulli, R, Relkin, N, Chiang, G, Lee, A, Lin, M, Ravdin, L, Tuena C., Maestri S., Serino S., Pedroli E., Stramba-Badiale M., Riva G., Silbert L. C., Lind B., Crissey R., Kaye J. A., Carter R., Dolen S., Quinn J., Schneider L. S., Pawluczyk S., Becerra M., Teodoro L., Dagerman K., Spann B. M., Brewer J., Fleisher A., Vanderswag H., Ziolkowski J., Heidebrink J. L., Zbizek-Nulph L., Lord J. L., Albers C. S., Petersen R., Mason S. S., Knopman D., Johnson K., Villanueva-Meyer J., Pavlik V., Pacini N., Lamb A., Kass J. S., Doody R. S., Shibley V., Chowdhury M., Rountree S., Dang M., Stern Y., Honig L. S., Mintz A., Ances B., Morris J. C., Winkfield D., Carroll M., Stobbs-Cucchi G., Oliver A., Creech M. L., Mintun M. A., Schneider S., Geldmacher D., Love M. N., Griffith R., Clark D., Brockington J., Marson D., Grossman H., Goldstein M. A., Greenberg J., Mitsis E., Shah R. C., Lamar M., Samuels P., Duara R., Greig-Custo M. T., Rodriguez R., Albert M., Onyike C., Farrington L., Rudow S., Brichko R., Kielb S., Smith A., Raj B. A., Fargher K., Sadowski M., Wisniewski T., Shulman M., Faustin A., Rao J., Castro K. M., Ulysse A., Chen S., Doraiswamy P. M., Petrella J. R., James O., Wong T. Z., Borges-Neto S., Karlawish J. H., Wolk D. A., Vaishnavi S., Clark C. M., Arnold S. E., Smith C. D., Jicha G. A., Khouli R. E., Raslau F. D., Lopez O. L., Oakley M. A., Simpson D. M., Porsteinsson A. P., Martin K., Kowalski N., Keltz M., Goldstein B. S., Makino K. M., Ismail M. S., Brand C., Thai G., Pierce A., Yanez B., Sosa E., Witbracht M., Kelley B., Nguyen T., Womack K., Mathews D., Quiceno M., Levey A. I., Lah J. J., Hajjar I., Burns J. M., Swerdlow R. H., Brooks W. M., Silverman D. H. S., Kremen S., Apostolova L., Tingus K., Lu P. H., Bartzokis G., Woo E., Teng E., Graff-Radford N. R., Parfitt F., Poki-Walker K., Farlow M. R., Hake A. M., Matthews B. R., Brosch J. R., Herring S., van Dyck C. H., Mecca A. P., Good S. P., MacAvoy M. G., Carson R. E., Varma P., Chertkow H., Vaitekunas S., Hosein C., Black S., Stefanovic B., Heyn C., Hsiung G. -Y. R., Kim E., Mudge B., Sossi V., Feldman H., Assaly M., Finger E., Pasternak S., Rachinsky I., Kertesz A., Drost D., Rogers J., Grant I., Muse B., Rogalski E., Robson J., Mesulam M. -M., Kerwin D., Wu C. -K., Johnson N., Lipowski K., Weintraub S., Bonakdarpour B., Pomara N., Hernando R., Sarrael A., Rosen H. J., Miller B. L., Weiner M. W., Perry D., Turner R. S., Reynolds B., MCCann K., Poe J., Marshall G. A., Sperling R. A., Johnson K. A., Yesavage J., Taylor J. L., Chao S., Coleman J., White J. D., Lane B., Rosen A., Tinklenberg J., Belden C. M., Atri A., Clark K. A., Zamrini E., Sabbagh M., Killiany R., Stern R., Mez J., Kowall N., Budson A. E., Obisesan T. O., Ntekim O. E., Wolday S., Khan J. I., Nwulia E., Nadarajah S., Lerner A., Ogrocki P., Tatsuoka C., Fatica P., Fletcher E., Maillard P., Olichney J., DeCarli C., Carmichael O., Bates V., Capote H., Rainka M., Borrie M., Lee T. -Y., Bartha R., Johnson S., Asthana S., Carlsson C. M., Perrin A., Burke A., Scharre D. W., Kataki M., Tarawneh R., Hart D., Zimmerman E. A., Celmins D., Miller D. D., Ponto L. L. B., Smith K. E., Koleva H., Shim H., Nam K. W., Schultz S. K., Williamson J. D., Craft S., Cleveland J., Yang M., Sink K. M., Ott B. R., Drake J., Tremont G., Daiello L. A., Drake J. D., Ritter A., Bernick C., Munic D., O'Connelll A., Mintzer J., Wiliams A., Masdeu J., Shi J., Garcia A., Newhouse P., Potkin S., Salloway S., Malloy P., Correia S., Kittur S., Pearlson G. D., Blank K., Anderson K., Flashman L. A., Seltzer M., Hynes M. L., Santulli R. B., Relkin N., Chiang G., Lee A., Lin M., and Ravdin L.

Abstract

Background: Increasing research suggests that gait abnormalities can be a risk factor for Alzheimer’s Disease (AD). Notably, there is growing evidence highlighting this risk factor in individuals with amnestic Mild Cognitive Impairment (aMCI), however further studies are needed. The aim of this study is to analyze cognitive tests results and brain-related measures over time in aMCI and examine how the presence of gait abnormalities (neurological or orthopedic) or normal gait affects these trends. Additionally, we sought to assess the significance of gait and gait-related measures as prognostic indicators for the progression from aMCI to AD dementia, comparing those who converted to AD with those who remained with a stable aMCI diagnosis during the follow-up. Methods: Four hundred two individuals with aMCI from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were included. Robust linear mixed-effects models were used to study the impact of gait abnormalities on a comprehensive neuropsychological battery over 36 months while controlling for relevant medical variables at baseline. The impact of gait on brain measures was also investigated. Lastly, the Cox proportional-hazards model was used to explore the prognostic relevance of abnormal gait and neuropsychological associated tests. Results: While controlling for relevant covariates, we found that gait abnormalities led to a greater decline over time in attention (DSST) and global cognition (MMSE). Intriguingly, psychomotor speed (TMT-A) and divided attention (TMT-B) declined uniquely in the abnormal gait group. Conversely, specific AD global cognition tests (ADAS-13) and auditory-verbal memory (RAVLT immediate recall) declined over time independently of gait profile. All the other cognitive tests were not significantly affected by time or by gait profile. In addition, we found that ventricles size increased faster in the abnormal gait group compared to the normal gait group. In terms of prognosis, abno

Published
2023

6. Safety and efficacy of tilavonemab in progressive supranuclear palsy: a phase 2, randomised, placebo-controlled trial

Author

Hoglinger, G. U., Litvan, I., Mendonca, N., Wang, D., Zheng, H., Rendenbach-Mueller, B., Lon, H. -K., Jin, Z., Fisseha, N., Budur, K., Gold, M., Ryman, D., Florian, H., Ahmed, A., Aiba, I., Albanese, Alberto, Bertram, K., Bordelon, Y., Bower, J., Brosch, J., Claassen, D., Colosimo, C., Corvol, J. -C., Cudia, P., Daniele, Antonio, Defebvre, L., Driver-Dunckley, E., Duquette, A., Eleopra, R., Eusebio, A., Fung, V., Geldmacher, D., Golbe, L., Grandas, F., Hall, D., Hatano, T., Honig, L., Hui, J., Kerwin, D., Kikuchi, A., Kimber, T., Kimura, T., Kumar, R., Ljubenkov, P., Lorenzl, S., Ludolph, A., Mari, Z., Mcfarland, N., Meissner, W., Mir Rivera, P., Mochizuki, H., Morgan, J., Munhoz, R., Nishikawa, N., O`sullivan, J., Oeda, T., Oizumi, H., Onodera, O., Ory-Magne, F., Peckham, E., Postuma, R., Quattrone, A., Quinn, J., Ruggieri, S., Sarna, J., Schulz, P. E., Slevin, J., Tagliati, M., Wile, D., Wszolek, Z., Xie, T., Zesiewicz, T., Albanese A. (ORCID:0000-0002-5864-0006), Hoglinger, G. U., Litvan, I., Mendonca, N., Wang, D., Zheng, H., Rendenbach-Mueller, B., Lon, H. -K., Jin, Z., Fisseha, N., Budur, K., Gold, M., Ryman, D., Florian, H., Ahmed, A., Aiba, I., Albanese, Alberto, Bertram, K., Bordelon, Y., Bower, J., Brosch, J., Claassen, D., Colosimo, C., Corvol, J. -C., Cudia, P., Daniele, Antonio, Defebvre, L., Driver-Dunckley, E., Duquette, A., Eleopra, R., Eusebio, A., Fung, V., Geldmacher, D., Golbe, L., Grandas, F., Hall, D., Hatano, T., Honig, L., Hui, J., Kerwin, D., Kikuchi, A., Kimber, T., Kimura, T., Kumar, R., Ljubenkov, P., Lorenzl, S., Ludolph, A., Mari, Z., Mcfarland, N., Meissner, W., Mir Rivera, P., Mochizuki, H., Morgan, J., Munhoz, R., Nishikawa, N., O`sullivan, J., Oeda, T., Oizumi, H., Onodera, O., Ory-Magne, F., Peckham, E., Postuma, R., Quattrone, A., Quinn, J., Ruggieri, S., Sarna, J., Schulz, P. E., Slevin, J., Tagliati, M., Wile, D., Wszolek, Z., Xie, T., Zesiewicz, T., and Albanese A. (ORCID:0000-0002-5864-0006)

Abstract

Background: Progressive supranuclear palsy is a neurodegenerative disorder associated with tau protein aggregation. Tilavonemab (ABBV-8E12) is a monoclonal antibody that binds to the N-terminus of human tau. We assessed the safety and efficacy of tilavonemab for the treatment of progressive supranuclear palsy. Methods: We did a phase 2, multicentre, randomised, placebo-controlled, double-blind study at 66 hospitals and clinics in Australia, Canada, France, Germany, Italy, Japan, Spain, and the USA. Participants (aged ≥40 years) diagnosed with possible or probable progressive supranuclear palsy who were symptomatic for less than 5 years, had a reliable study partner, and were able to walk five steps with minimal assistance, were randomly assigned (1:1:1) by interactive response technology to tilavonemab 2000 mg, tilavonemab 4000 mg, or matching placebo administered intravenously on days 1, 15, and 29, then every 28 days through to the end of the 52-week treatment period. Randomisation was done by the randomisation specialist of the study sponsor, who did not otherwise participate in the study. The sponsor, investigators, and participants were unaware of treatment allocations. The primary endpoint was the change from baseline to week 52 in the Progressive Supranuclear Palsy Rating Scale (PSPRS) total score in the intention-to-treat population. Adverse events were monitored in participants who received at least one dose of study drug. Prespecified interim futility criteria were based on a model-based effect size of 0 or lower when 60 participants had completed the 52-week treatment period and 0·12 or lower when 120 participants had completed the 52-week treatment period. This study is registered at ClinicalTrials.gov, number NCT02985879. Findings: Between Dec 12, 2016, and Dec 31, 2018, 466 participants were screened, 378 were randomised. The study was terminated on July 3, 2019, after prespecified futility criteria were met at the second interim analysis. A total of 3

Published
2021

7. Atmospheric chemistry of linear perfluorinated aldehydes: Dissociation kinetics of [C.sub.n][F.sub.2n+1]CO radicals

Author

Waterland, Robert L. and Dobbs, Kerwin D.

Subjects
Aldehydes -- Chemical properties, Atmospheric chemistry -- Research, Fluorine compounds -- Chemical properties, Oxidation-reduction reaction -- Research, Chemicals, plastics and rubber industries
Abstract

Ab initio quantum chemistry calculations of the decarbonylation kinetics of [C.sub.n][F.sub.2n+1]CO radicals are reported. The results have shown that [C.sub.n][F.sub.2n+1]CO radicals have a strong tendency to decompose in order to give [C.sub.n][F.sub.2n+1] and CO under atmospheric conditions.

Published
2007

9. Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer’s disease (MCI and dementia) in primary care: a review and synthesis

Author

Liss, J. L., primary, Seleri Assunção, S., additional, Cummings, J., additional, Atri, A., additional, Geldmacher, D. S., additional, Candela, S. F., additional, Devanand, D. P., additional, Fillit, H. M., additional, Susman, J., additional, Mintzer, J., additional, Bittner, T., additional, Brunton, S. A., additional, Kerwin, D. R., additional, Jackson, W. C., additional, Small, G. W., additional, Grossberg, G. T., additional, Clevenger, C. K., additional, Cotter, V., additional, Stefanacci, R., additional, Wise‐Brown, A., additional, and Sabbagh, M. N., additional

Published
2021
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10. A Raman spectroscopy study of the separation of hydrofluorocarbons using zeolites

Author

Crawford, Michael K., Dobbs, Kerwin D., Smalley, Robert J., Corbin, David R., Maliszewskyj, Nicholas, Udovic, Terrence J., Cavanagh, Richard R., Rush, John J., and Grey, Clare P.

Subjects
Raman spectroscopy -- Usage, Hydrofluorocarbons -- Research, Zeolites -- Research, Chemicals, plastics and rubber industries
Abstract

The separation of the hydrofluorocarbon (HFC)-134a from its isomer HFC-134via the differential surface adsorption on the zeolite NaX was investigated using Raman spectroscopy. The trans-gauche population ratio for HFC-134 adsorbed on NaX was found to decrease by up to 20-fold compared to the gas phase. The conformational ratio was also found to change with the HFC loading, with the polar gauche conformer being more preferred when the separation factor is maximum.

Published
1999

11. A blood-based signature of cerebrospinal fluid A beta(1-42) status

Author

Goudey, B., Fung, B.J., Schieber, C., Faux, N.G., Weiner, M.W., Aisen, P., Petersen, R., Jack, C.R., Jagust, W., Trojanowki, J.Q., Toga, A.W., Beckett, L., Green, R.C., Saykin, A.J., Morris, J., Shaw, L.M., Kaye, J., Quinn, J., Silbert, L., Lind, B., Carter, R., Dolen, S., Schneider, L.S., Pawluczyk, S., Beccera, M., Teodoro, L., Spann, B.M., Brewer, J., Vanderswag, H., Fleisher, A., Heidebrink, J.L., Lord, J.L., Mason, S.S., Albers, C.S., Knopman, D., Johnson, K., Doody, R.S., Villanueva-Meyer, J., Chowdhury, M., Rountree, S., Dang, M., Stern, Y., Honig, L.S., Bell, K.L., Ances, B., Morris, J.C., Carroll, M., Creech, M.L., Franklin, E., Mintun, M.A., Schneider, S., Oliver, A., Marson, D., Griffth, R., Clark, D., Geldmacher, D., Brockington, J., Roberson, E., Natelson Love, M., Grossman, H., Mitsis, E., Shah, R.C., deToledo-Morrell, L., Duara, R., Varon, D., Greig, M.T., Roberts, P., Albert, M., Onyike, C., D'Agostino, D., Kielb, S., Galvin, J.E., Cerbone, B., Michel, C.A., Pogorelec, D.M., Rusinek, H., Leon, M.J. de, Glodzik, L., De Santi, S., Doraiswamy, P.M., Petrella, J.R., Borges-Neto, S., Wong, T.Z., Coleman, E., Smith, C.D., Jicha, G., Hardy, P., Sinha, P., Oates, E., Conrad, G., Porsteinsson, A.P., Goldstein, B.S., Martin, K., Makino, K.M., Ismail, M.S., Brand, C., Mulnard, R.A., Thai, G., McAdams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Levey, A.I., Lah, J.J., Cellar, J.S., Burns, J.M., Swerdlow, R.H., Brooks, W.M., Apostolova, L., Tingus, K., Woo, E., Silverman, D.H.S., Lu, P.H., Bartzokis, G., Graff-Radford, N.R., Parftt, F., Kendall, T., Johnson, H., Farlow, M.R., Hake, A.M., Matthews, B.R., Brosch, J.R., Herring, S., Hunt, C., Dyck, .H. van, Carson, R.E., MacAvoy, M.G., Varma, P., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, Ging-Yuek Robin, Feldman, H., Mudge, B., Assaly, M., Finger, E., Pasternack, S., Rachisky, I., Trost, D., Kertesz, A., Bernick, C., Munic, D., Mesulam, M.-M., Lipowski, K., Weintraub, S., Bonakdarpour, B., Kerwin, D., Wu, C.-K., Johnson, N., Sadowsky, C., Villena, T., Turner, R.S., Reynolds, B., Sperling, R.A., Johnson, K.A., Marshall, G., Yesavage, J., Taylor, J.L., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M.N., Belden, C.M., Jacobson, S.A., Sirrel, S.A., Kowall, N., Killiany, R., Budson, A.E., Norbash, A., Johnson, P.L., Obisesan, T.O., Wolday, S., Allard, J., Lerner, A., Ogrocki, P., Tatsuoka, C., Fatica, P., Fletcher, E., Maillard, P., Olichney, J., DeCarli, C., Carmichael, O., Kittur, S., Borrie, M., Lee, T.-Y., Bartha, R., Johnson, S., Asthana, S., Carlsson, C.M., Potkin, S.G., Preda, A., Nguyen, D., Tariot, P., Burke, A., Trncic, N., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, D.W., Kataki, M., Adeli, A., Zimmerman, E.A., Celmins, D., Brown, A.D., Pearlson, G.D., Blank, K., Anderson, K., Flashman, L.A., Seltzer, M., Hynes, M.L., Santulli, R.B., Sink, K.M., Gordineer, L., Williamson, J.D., Garg, P., Watkins, F., Ott, B.R., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, H.J., Miller, B.L., Perry, D., Mintzer, J., Spicer, K., Bachman, D., Pomara, N., Hernando, R., Sarrael, A., Relkin, N., Chaing, G., Lin, M., Ravdin, L., Smith, A., Raj, B.A., Fargher, K., Saykin, A., Nho, K., Kling, M., Toledo, J., Shaw, L., Trojanowski, J., Farrer, L., Kastsenmueller, G., Arnold, M., Wishart, D., Wurtz, P., Bhattcharyya, S., Duijin, C. van, Mangravite, L., Han, X., Hankemeier, T., Fiehn, O., Barupal, D., Thiele, I., Heinken, A., Meikle, P., Price, N., Funk, C., Jia, W., Kueider-Paisley, A., Tenebaum, J., Black, C., Moseley, A., Thompson, W., Mahmoudiandehkorki, S., Baillie, R., Welsh-Bohmer, K., Plassman, B., and Epidemiology

Subjects
Male, 0301 basic medicine, Apolipoprotein E, Oncology, medicine.medical_specialty, Amyloid, Amyloid beta, lcsh:Medicine, Article, 03 medical and health sciences, Apolipoproteins E, 0302 clinical medicine, Cerebrospinal fluid, Alzheimer Disease, Predictive Value of Tests, Internal medicine, medicine, Humans, Dementia, Cognitive decline, lcsh:Science, Aged, Aged, 80 and over, Amyloid beta-Peptides, Multidisciplinary, biology, Chemokine CCL26, business.industry, lcsh:R, Alzheimer’s Disease Metabolomics Consortium, Alzheimer’s Disease Neuroimaging Initiative, medicine.disease, Peptide Fragments, 3. Good health, 030104 developmental biology, biology.protein, Chromogranin A, Female, lcsh:Q, Alzheimer's disease, business, Biomarkers, 030217 neurology & neurosurgery, Alzheimer's Disease Neuroimaging Initiative
Abstract

It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy.

Published
2019
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12. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

Author

Young, A. L., Marinescu, R. V., Oxtoby, N. P., Bocchetta, M., Yong, K., Firth, N. C., Cash, D. M., Thomas, D. L., Dick, K. M., Cardoso, J., van Swieten, J., Borroni, B., Galimberti, D., Masellis, M., Tartaglia, M. C., Rowe, J. B., Graff, C., Tagliavini, F., Frisoni, G. B., Laforce, R., Finger, E., de Mendonca, A., Sorbi, S., Warren, J. D., Crutch, S., Fox, N. C., Ourselin, S., Schott, J. M., Rohrer, J. D., Alexander, D. C., Andersson, C., Archetti, S., Arighi, A., Benussi, L., Binetti, G., Black, S., Cosseddu, M., Fallstrom, M., Ferreira, C., Fenoglio, C., Freedman, M., Fumagalli, G. G., Gazzina, S., Ghidoni, R., Grisoli, M., Jelic, V., Jiskoot, L., Keren, R., Lombardi, G., Maruta, C., Meeter, L., Mead, S., van Minkelen, R., Nacmias, B., Oijerstedt, L., Padovani, A., Panman, J., Pievani, M., Polito, C., Premi, E., Prioni, S., Rademakers, R., Redaelli, V., Rogaeva, E., Rossi, G., Rossor, M., Scarpini, E., Tang-Wai, D., Thonberg, H., Tiraboschi, P., Verdelho, A., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., Jagust, W., Trojanowki, J. Q., Toga, A. W., Beckett, L., Green, R. C., Saykin, A. J., Morris, J., Shaw, L. M., Khachaturian, Z., Sorensen, G., Kuller, L., Raichle, M., Paul, S., Davies, P., Fillit, H., Hefti, F., Holtzman, D., Mesulam, M. M., Potter, W., Snyder, P., Schwartz, A., Montine, T., Thomas, R. G., Donohue, M., Walter, S., Gessert, D., Sather, T., Jiminez, G., Harvey, D., Bernstein, M., Thompson, P., Schuff, N., Borowski, B., Gunter, J., Senjem, M., Vemuri, P., Jones, D., Kantarci, K., Ward, C., Koeppe, R. A., Foster, N., Reiman, E. M., Chen, K., Mathis, C., Landau, S., Cairns, N. J., Householder, E., Taylor-Reinwald, L., Lee, V., Korecka, M., Figurski, M., Crawford, K., Neu, S., Foroud, T. M., Potkin, S., Shen, L., Faber, K., Kim, S., Nho, K., Thal, L., Buckholtz, N., Albert, M., Frank, R., Hsiao, J., Kaye, J., Quinn, J., Lind, B., Carter, R., Dolen, S., Schneider, L. S., Pawluczyk, S., Beccera, M., Teodoro, L., Spann, B. M., Brewer, J., Vanderswag, H., Fleisher, A., Heidebrink, J. L., Lord, J. L., Mason, S. S., Albers, C. S., Knopman, D., Johnson, K., Doody, R. S., Villanueva-Meyer, J., Chowdhury, M., Rountree, S., Dang, M., Stern, Y., Honig, L. S., Bell, K. L., Ances, B., Carroll, M., Leon, S., Mintun, M. A., Schneider, S., Oliver, A., Marson, D., Griffith, R., Clark, D., Geldmacher, D., Brockington, J., Roberson, E., Grossman, H., Mitsis, E., de Toledo-Morrell, L., Shah, R. C., Duara, R., Varon, D., Greig, M. T., Roberts, P., Onyike, C., D'Agostino, D., Kielb, S., Galvin, J. E., Cerbone, B., Michel, C. A., Rusinek, H., de Leon, M. J., Glodzik, L., De Santi, S., Doraiswamy, P. M., Petrella, J. R., Wong, T. Z., Arnold, S. E., Karlawish, J. H., Wolk, D., Smith, C. D., Jicha, G., Hardy, P., Sinha, P., Oates, E., Conrad, G., Lopez, O. L., Oakley, M. A., Simpson, D. M., Porsteinsson, A. P., Goldstein, B. S., Martin, K., Makino, K. M., Ismail, M. S., Brand, C., Mulnard, R. A., Thai, G., Mc-Adams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Diaz-Arrastia, R., King, R., Weiner, M., Martin-Cook, K., Devous, M., Levey, A. I., Lah, J. J., Cellar, J. S., Burns, J. M., Anderson, H. S., Swerdlow, R. H., Apostolova, L., Tingus, K., Woo, E., Silverman, D. H., P. H., Lu, Bartzokis, G., Graff-Radford, N. R., Parfitt, F., Kendall, T., Johnson, H., Farlow, M. R., Hake, A. M., Matthews, B. R., Herring, S., Hunt, C., van Dyck, C. H., Carson, R. E., Macavoy, M. G., Chertkow, H., Bergman, H., Hosein, C., Stefanovic, B., Caldwell, C., Hsiung, G. -Y. R., Feldman, H., Mudge, B., Assaly, M., Kertesz, A., Rogers, J., Bernick, C., Munic, D., Kerwin, D., Mesulam, M. -M., Lipowski, K., C. -K., Wu, Johnson, N., Sadowsky, C., Martinez, W., Villena, T., Turner, R. S., Reynolds, B., Sperling, R. A., Johnson, K. A., Marshall, G., Frey, M., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M. N., Belden, C. M., Jacobson, S. A., Sirrel, S. A., Kowall, N., Killiany, R., Budson, A. E., Norbash, A., Johnson, P. L., Allard, J., Lerner, A., Ogrocki, P., Hudson, L., Fletcher, E., Carmichael, O., Olichney, J., Decarli, C., Kittur, S., Borrie, M., Lee, T. -Y., Bartha, R., Johnson, S., Asthana, S., Carlsson, C. M., Potkin, S. G., Preda, A., Nguyen, D., Tariot, P., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, D. W., Kataki, M., Adeli, A., Zimmerman, E. A., Celmins, D., Brown, A. D., Pearlson, G. D., Blank, K., Anderson, K., Santulli, R. B., Kitzmiller, T. J., Schwartz, E. S., Sink, K. M., Williamson, J. D., Garg, P., Watkins, F., Ott, B. R., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, H. J., Miller, B. L., Mintzer, J., Spicer, K., Bachman, D., Pasternak, S., Rachinsky, I., Drost, D., Pomara, N., Hernando, R., Sarrael, A., Schultz, S. K., Ponto, L. L. B., Shim, H., Smith, K. E., Relkin, N., Chaing, G., Raudin, L., Smith, A., Fargher, K., Raj, B. A., Neylan, T., Grafman, J., Davis, M., Morrison, R., Hayes, J., Finley, S., Friedl, K., Fleischman, D., Arfanakis, K., James, O., Massoglia, D., Fruehling, J. J., Harding, S., Peskind, E. R., Petrie, E. C., Li, G., Yesavage, J. A., Taylor, J. L., and Furst, A. J.

Published
2018

14. Highly Quantum Efficient Phosphorescent Sky Blue Emitters Based on Diastereomeric Iridium(III) Complexes of Atropisomeric 5-Aryl-4H-1,2,4-triazole Ligands

Author

Will J. Marshall, Lois M. Bryman, Joseph Buriak, Troy C. Gehret, Kyung-Ho Park, Steve G. Zane, Thomas H. Scholz, Jeffrey S. Meth, Jerald Feldman, Charles D. McLaren, Kerwin D. Dobbs, and Giang D. Vo

Subjects
Atropisomer, Ligand, Aryl, Organic Chemistry, Triazole, Diastereomer, chemistry.chemical_element, Photochemistry, Inorganic Chemistry, chemistry.chemical_compound, chemistry, Polymer chemistry, Iridium, Physical and Theoretical Chemistry, Homoleptic, Phosphorescence
Abstract

Homoleptic fac-IrIIIL3 complexes of 5-aryl-4H-1,2,4-triazole ligands are sky blue emitters. When unsymmetrically substituted, the triazole ligands exhibit atropisomerism, and upon cyclometalation to Ir(III) a mixture of diastereomers is formed. We have isolated and structurally characterized all four possible diastereomers of the fac-IrIIIL3 complex formed upon cyclometalation of an atropisomeric 5-aryl-4H-1,2,4-triazole ligand onto Ir(III). The phosphorescent blue emitting materials reported herein are among the most efficient to date, with quantum efficiencies above 95%.

Published
2015
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15. Quality Assessment and Prediction of Philippine Mangoes: A Convolutional Neural Network Approach

Author

Robert Kerwin D. Billiones, Francisco Emmanuel T. Munsayac, Annamitz A. Rapliza, Nilo T. Bugtai, Renann G. Baldovino, and Carlos Matthew P. Cases

Subjects
Production line, Background subtraction, Speedup, General Computer Science, Artificial neural network, Computer science, business.industry, media_common.quotation_subject, Feature extraction, General Engineering, Sorting, Pattern recognition, Convolutional neural network, Quality (business), Artificial intelligence, General Agricultural and Biological Sciences, business, media_common
Abstract

Philippines is one of the world’s leading exporter of mangoes. The country produces many varieties of mangoes, one of which is the ‘Carabao’ mango. Several metric tons of mangoes are produced, and these have to be checked for defects before entering the market. With recent advances in technology, it has become efficient and relatively easy to use for these applications. The objective of this paper is to present a non-destructive method to check the quality of mangoes using computer vision (CV) and convolutional neural network (CNN) with a minimal number of samples. An experimental setup was created to simulate a production line. A webcam was used for capturing images of the mangoes, while a mini computer was used for controlling the peripherals. As basis for categorizing the mangoes as either good or bad, the Philippine National Standard (PNS) for mangoes was used. A basic background subtraction algorithm was used to extract the mango’s image. With these extracted images, a 2-category network was trained, and the achieved classification accuracy was 97.21%. The goal of having a high accuracy in classifying mangoes was achieved. There are multiple paths to explore in the future, including additional feature extraction methods, different neural networks, and hardware improvements, in order to speed up the sorting process. Moreover, it may be necessary to be able to identify mangoes with only slight defects to be used for other products, such as dried mangoes, to reduce product wastage.

Published
2019
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16. Reply to 'comment on 'atmospheric chemistry of linear perfluorinated aldehydes: dissociation kinetics of [C.sub.n][F.sub.2n+1]CO

Author

Waterland, Robert L. and Dobbs, Kerwin D.

Subjects
Aldehydes -- Chemical properties, Decomposition (Chemistry) -- Research, Atmospheric chemistry -- Research, Chemicals, plastics and rubber industries
Abstract

A reply to critical comment in support of strong decomposing tendency of [C.sub.n][F.sub.2n+1]CO radicals to give [C.sub.n][F.sub.2n+1] and CO under atmospheric conditions is presented.

Published
2008

17. The fourth industrial fluid properties simulation challenge

Author

Fiona H. Case, John Brennan, Anne Chaka, Kerwin D. Dobbs, Daniel G. Friend, Peter A. Gordon, Jonathan D. Moore, Raymond D. Mountain, James D. Olson, Richard B. Ross, Martin Schiller, Vincent K. Shen, and Eric A. Stahlberg

Subjects
General Chemical Engineering, General Physics and Astronomy, Physical and Theoretical Chemistry
Published
2008
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18. The third industrial fluid properties simulation challenge

Author

Fiona H. Case, John Brennan, Anne Chaka, Kerwin D. Dobbs, Daniel G. Friend, David Frurip, Peter A. Gordon, Jonathan Moore, Raymond D. Mountain, James Olson, Richard B. Ross, Martin Schiller, and Vincent K. Shen

Subjects
General Chemical Engineering, General Physics and Astronomy, Physical and Theoretical Chemistry
Published
2007
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19. Atmospheric Chemistry of Linear Perfluorinated Aldehydes: Dissociation Kinetics of CnF2n+1CO Radicals

Author

Robert L. Waterland and Kerwin D. Dobbs

Subjects
Arrhenius equation, Chemistry, Radical, Kinetics, Inorganic chemistry, Decarbonylation, Ab initio, Medicinal chemistry, Quantum chemistry, symbols.namesake, Atmospheric chemistry, Yield (chemistry), symbols, Physical and Theoretical Chemistry
Abstract

Linear perfluorinated aldehydes (PFALs, CnF2n+1CHO) are important intermediate species in the atmospheric oxidation pathway of many polyfluorinated compounds. PFALs can be further oxidized in the gas phase to give perfluorinated carboxylic acids (PFCAs, CnF2n+1C(O)OH, n ) 6, 12) which have been detected in animal tissues and at low parts per billion levels in human blood sera. In this paper, we report ab initio quantum chemistry calculations of the decarbonylation kinetics of C nF2n+1CO radicals. Our results show that CnF2n+1CO radicals have a strong tendency to decompose to give CnF2n+1 and CO under atmospheric conditions: the Arrhenius activation energies for decarbonylation of CF3CO, C2F5CO, and C3F7CO obtained using PMP4/ 6-311++G(2d,p) are 8.8, 6.6, and 5.8 kcal/mol, respectively, each of which is about 5 kcal/mol lower than the barrier for the corresponding nonfluorinated radicals. The lowering of the barrier for decarbonylation of CnF2n+1CO relative to that of CnH2n+1CO is well explained by electron withdrawal by F atoms that serve to weaken the critical C-CO bond. These results have important implications for the atmospheric fate of PFALs and the atmospheric pathways to PFCAs. The main effect of decarbonylation of CnF2n+1CO is to decrease the molar yield of CnF2n+1C(O)OH; if 100% of the CnF2n+1CO decompose, the yield of CnF2n+1C(O)OH must be zero. There is considerable scope for additional experimental and theoretical studies.

Published
2007
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20. A Raman Spectroscopy Study of the Separation of Hydrofluorocarbons Using Zeolites

Author

Richard R. Cavanagh, Kerwin D. Dobbs, Clare P. Grey, Nicholas Maliszewskyj, Terrence J. Udovic, Michael Crawford, John J. Rush, Robert J. Smalley, and David R. Corbin

Subjects
education.field_of_study, Chemistry, Population, Analytical chemistry, Ozone depletion, Surfaces, Coatings and Films, symbols.namesake, Adsorption, Materials Chemistry, symbols, Physical chemistry, Polar, Physical and Theoretical Chemistry, Zeolite, education, Raman spectroscopy, Conformational isomerism, Raman scattering
Abstract

Hydrofluorocarbons (HFCs), such as HFC-134a (CF3CFH2), are replacing chlorofluorocarbons (CFCs) in many applications because of their low ozone depletion potentials. The more complex chemical syntheses of HFCs, however, may not provide the molecular species desired with sufficient purity, leading to the need to separate reaction products. An important example is provided by the separation of the isomers HFC-134a and HFC-134 (CF2HCF2H) over the faujisitic zeolite NaX (Na86Al86Si106O384). An understanding of the fundamental mechanism controlling this separation involves the unusual aspect that HFC-134 can exist as either a trans or gauche conformer; a key factor is that the trans conformer is nonpolar whereas the gauche conformer is highly polar. Here we report the results of a Raman scattering study which show the trans-gauche population ratio for HFC-134 adsorbed on NaX is decreased as much as 20-fold compared to the gas phase. Furthermore, the conformational ratio changes with HFC loading, the polar gauc...

Published
1999
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21. Association of common genetic variants in GPCPD1 with scaling of visual cortical surface area in humans

Author

Bakken, TE, Roddey, JC, Djurovic, S, Akshoomoff, N, Amaral, DG, Bloss, CS, Casey, BJ, Chang, L, Ernst, TM, Gruen, JR, Jernigan, TL, Kaufmann, WE, Kenet, T, Kennedy, DN, Kuperman, JM, Murray, SS, Sowell, ER, Rimol, LM, Mattingsdal, M, Melle, I, Agartz, I, Andreassen, OA, Schork, NJ, Dale, AM, Alzheimer’s Disease Neuroimaging Initiative, Pediatric Imaging, Neurocognition, Genetics Study, Weiner, M, Aisen, P, Petersen, R, Jack, CR, Jr, Jagust, W, Trojanowki, JQ, Toga, AW, Beckett, L, Green, RC, Saykin, AJ, Morris, J, Liu, E, Montine, T, Gamst, A, Thomas, RG, Donohue, M, Walter, S, Gessert, D, Sather, T, Harvey, D, Kornak, J, Dale, A, Bernstein, M, Felmlee, J, Fox, N, Thompson, P, Schuff, N, Alexander, G, DeCarli, C, Bandy, D, Koeppe, RA, Foster, N, Reiman, EM, Chen, K, Mathis, C, Cairns, NJ, Taylor-Reinwald, L, Shaw, L, Lee, VM, Korecka, M, Crawford, K, Neu, S, Foroud, TM, Potkin, S, Shen, L, Kachaturian, Z, Frank, R, Snyder, PJ, Molchan, S, Kaye, J, Quinn, J, Lind, B, Dolen, S, Schneider, LS, Pawluczyk, S, Spann, BM, Brewer, J, Vanderswag, H, Heidebrink, JL, Lord, JL, Johnson, K, Doody, RS, Villanueva-Meyer, J, Chowdhury, M, Stern, Yaakov, Honig, LS, Bell, KL, Morris, JC, Ances, B, Carroll, M, Leon, S, Mintun, MA, Schneider, S, Marson, D, Griffith, R, Clark, D, Grossman, H, Mitsis, E, Romirowsky, A, deToledo-Morrell, L, Shah, RC, Duara, R, Varon, D, Roberts, P, Albert, M, Onyike, C, Kielb, S, Rusinek, H, de, Leon, MJ, Glodzik, L, De, Santi, S, Doraiswamy, PM, Petrella, JR, Coleman, RE, Arnold, SE, Karlawish, JH, Wolk, D, Smith, CD, Jicha, G, Hardy, P, Lopez, OL, Oakley, M, Simpson, DM, Porsteinsson, AP, Goldstein, BS, Martin, K, Makino, KM, Ismail, MS, Brand, C, Mulnard, RA, Thai, G, Mc-Adams-Ortiz, C, Womack, K, Mathews, D, Quiceno, M, Diaz-Arrastia, R, King, R, Martin-Cook, K, DeVous, M, Levey, AI, Lah, JJ, Cellar, JS, Burns, JM, Anderson, HS, Swerdlow, RH, Apostolova, L, Lu, PH, Bartzokis, G, Silverman, DH, Graff-Radford, NR, Parfitt, F, Johnson, H, Farlow, MR, Hake, AM, Matthews, BR, Herring, S, van, Dyck, CH, Carson, RE, MacAvoy, MG, Chertkow, H, Bergman, H, Hosein, C, Black, S, Stefanovic, B, Caldwell, C, Ging-Yuek, Hsiung, R, Feldman, H, Mudge, B, Assaly, M, Kertesz, A, Rogers, J, Trost, D, Bernick, C, Munic, D, Kerwin, D, Mesulam, MM, Lipowski, K, Wu, CK, Johnson, N, Sadowsky, C, Martinez, W, Villena, T, Turner, RS, Reynolds, B, Sperling, RA, Johnson, KA, Marshall, G, Frey, M, Yesavage, J, Taylor, JL, Lane, B, Rosen, A, Tinklenberg, J, Sabbagh, M, Belden, C, Jacobson, S, Kowall, N, Killiany, R, Budson, AE, Norbash, A, Johnson, PL, Obisesan, TO, Wolday, S, Bwayo, SK, Lerner, A, Hudson, L, Ogrocki, P, Fletcher, E, Carmichael, O, Olichney, J, Kittur, S, Borrie, M, Lee, TY, Bartha, R, Johnson, S, Asthana, S, Carlsson, CM, Potkin, SG, Preda, A, Nguyen, D, Tariot, P, Fleisher, A, Reeder, S, Bates, V, Capote, H, Rainka, M, Scharre, DW, Kataki, M, Zimmerman, EA, Celmins, D, Brown, AD, Pearlson, GD, Blank, K, Anderson, K, Santulli, RB, Schwartz, ES, Sink, KM, Williamson, JD, Garg, P, Watkins, F, Ott, BR, Querfurth, H, Tremont, G, Salloway, S, Malloy, P, Correia, S, Rosen, HJ, Miller, BL, Mintzer, J, Longmire, CF, Spicer, K, Finger, E, Rachinsky, I, Drost, D, Jernigan, T, McCabe, C, Grant, E, Ernst, T, Kuperman, J, Chung, Y, Murray, S, Bloss, C, Darst, B, Pritchett, L, Saito, A, Amaral, D, DiNino, M, Eyngorina, B, Sowell, E, Houston, S, Soderberg, L, Kaufmann, W, van, Zijl, P, Rizzo-Busack, H, Javid, M, Mehta, N, Ruberry, E, Powers, A, Rosen, B, Gebhard, N, Manigan, H, Frazier, J, Kennedy, D, Yakutis, L, Hill, M, Gruen, J, Bosson-Heenan, J, and Carlson, H

Subjects
anatomy & histology, pathology [Visual Cortex], Adult, Diagnostic Imaging, Male, Linkage disequilibrium, Visual perception, genetic structures, Adolescent, Genotype, Imaging genetics, methods [Diagnostic Imaging], Single-nucleotide polymorphism, Genome-wide association study, Saccharomyces cerevisiae, Biology, Polymorphism, Single Nucleotide, Cohort Studies, methods [Brain Mapping], pathology [Brain], Cortex (anatomy), Genetic variation, Medicine and Health Sciences, medicine, Humans, genetics [Phosphoric Diester Hydrolases], Aged, Visual Cortex, Genetics, Brain Mapping, Multidisciplinary, Models, Genetic, Phosphoric Diester Hydrolases, metabolism [Saccharomyces cerevisiae], Brain, Genetic Variation, Genomics, Middle Aged, Biological Sciences, Visual cortex, medicine.anatomical_structure, Female, Genome-Wide Association Study
Abstract

Visual cortical surface area varies two- to threefold between human individuals, is highly heritable, and has been correlated with visual acuity and visual perception. However, it is still largely unknown what specific genetic and environmental factors contribute to normal variation in the area of visual cortex. To identify SNPs associated with the proportional surface area of visual cortex, we performed a genome-wide association study followed by replication in two independent cohorts. We identified one SNP (rs6116869) that replicated in both cohorts and had genome-wide significant association ( P combined = 3.2 × 10 −8 ). Furthermore, a metaanalysis of imputed SNPs in this genomic region identified a more significantly associated SNP (rs238295; P = 6.5 × 10 −9 ) that was in strong linkage disequilibrium with rs6116869. These SNPs are located within 4 kb of the 5′ UTR of GPCPD1 , glycerophosphocholine phosphodiesterase GDE1 homolog ( Saccharomyces cerevisiae ), which in humans, is more highly expressed in occipital cortex compared with the remainder of cortex than 99.9% of genes genome-wide. Based on these findings, we conclude that this common genetic variation contributes to the proportional area of human visual cortex. We suggest that identifying genes that contribute to normal cortical architecture provides a first step to understanding genetic mechanisms that underlie visual perception.

Published
2012

22. Generative FDG-PET and MRI model of aging and disease progression in Alzheimer's disease.

Author

Alzheimer's Disease Neuroimaging Initiative, Weiner, M., Aisen, P., Petersen, R., Jack CR.<Suffix>Jr</Suffix>, Jagust, W., Trojanowki, JQ., Toga, AW., Beckett, L., Green, RC., Saykin, AJ., Morris, J., Liu, E., Montine, T., Gamst, A., Thomas, RG., Donohue, M., Walter, S., Gessert, D., Sather, T., Harvey, D., Kornak, J., Dale, A., Bernstein, M., Felmlee, J., Fox, N., Thompson, P., Schuff, N., DeCarli, C., Bandy, D., Koeppe, RA., Foster, N., Reiman, EM., Chen, K., Mathis, C., Cairns, NJ., Taylor-Reinwald, L., Shaw, L., Lee, VM., Korecka, M., Crawford, K., Neu, S., Foroud, TM., Potkin, S., Shen, L., Kachaturian, Z., Frank, R., Snyder, PJ., Molchan, S., Kaye, J., Quinn, J., Lind, B., Dolen, S., Schneider, LS., Pawluczyk, S., Spann, BM., Brewer, J., Vanderswag, H., Heidebrink, JL., Lord, JL., Johnson, K., Doody, RS., Villanueva-Meyer, J., Chowdhury, M., Stern, Y., Honig, LS., Bell, KL., Morris, JC., Ances, B., Carroll, M., Leon, S., Mintun, MA., Schneider, S., Marson, D., Griffith, R., Clark, D., Grossman, H., Mitsis, E., Romirowsky, A., deToledo-Morrell, L., Shah, RC., Duara, R., Varon, D., Roberts, P., Albert, M., Onyike, C., Kielb, S., Rusinek, H., de Leon MJ., Glodzik, L., De Santi, S., Doraiswamy, P., Petrella, JR., Coleman, R., Arnold, SE., Karlawish, JH., Wolk, D., Smith, CD., Jicha, G., Hardy, P., Lopez, OL., Oakley, M., Simpson, DM., Porsteinsson, AP., Goldstein, BS., Martin, K., Makino, KM., Ismail, M., Brand, C., Mulnard, RA., Thai, G., Mc-Adams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Diaz-Arrastia, R., King, R., Martin-Cook, K., DeVous, M., Levey, AI., Lah, JJ., Cellar, JS., Burns, JM., Anderson, HS., Swerdlow, RH., Apostolova, L., Lu, PH., Bartzokis, G., Silverman, DH., Graff-Radford, NR., Parfitt, F., Johnson, H., Farlow, MR., Hake, AM., Matthews, BR., Herring, S., van Dyck CH., Carson, RE., MacAvoy, MG., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, GY., Feldman, H., Mudge, B., Assaly, M., Kertesz, A., Rogers, J., Trost, D., Bernick, C., Munic, D., Kerwin, D., Mesulam, MM., Lipowski, K., Wu, CK., Johnson, N., Sadowsky, C., Martinez, W., Villena, T., Turner, RS., Reynolds, B., Sperling, RA., Johnson, KA., Marshall, G., Frey, M., Yesavage, J., Taylor, JL., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M., Belden, C., Jacobson, S., Kowall, N., Killiany, R., Budson, AE., Norbash, A., Johnson, PL., Obisesan, TO., Wolday, S., Bwayo, SK., Lerner, A., Hudson, L., Ogrocki, P., Fletcher, E., Carmichael, O., Olichney, J., Kittur, S., Borrie, M., Lee, TY., Bartha, R., Johnson, S., Asthana, S., Carlsson, CM., Potkin, SG., Preda, A., Nguyen, D., Tariot, P., Fleisher, A., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, DW., Kataki, M., Zimmerman, EA., Celmins, D., Brown, AD., Pearlson, GD., Blank, K., Anderson, K., Santulli, RB., Schwartz, ES., Sink, KM., Williamson, JD., Garg, P., Watkins, F., Ott, BR., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, HJ., Miller, BL., Mintzer, J., Longmire, CF., Spicer, K., Finger, E., Rachinsky, I., Drost, D., Dukart, J., Kherif, F., Mueller, K., Adaszewski, S., Schroeter, M.L., Frackowiak, R.S., Draganski, B., Alzheimer's Disease Neuroimaging Initiative, Weiner, M., Aisen, P., Petersen, R., Jack CR.<Suffix>Jr</Suffix>, Jagust, W., Trojanowki, JQ., Toga, AW., Beckett, L., Green, RC., Saykin, AJ., Morris, J., Liu, E., Montine, T., Gamst, A., Thomas, RG., Donohue, M., Walter, S., Gessert, D., Sather, T., Harvey, D., Kornak, J., Dale, A., Bernstein, M., Felmlee, J., Fox, N., Thompson, P., Schuff, N., DeCarli, C., Bandy, D., Koeppe, RA., Foster, N., Reiman, EM., Chen, K., Mathis, C., Cairns, NJ., Taylor-Reinwald, L., Shaw, L., Lee, VM., Korecka, M., Crawford, K., Neu, S., Foroud, TM., Potkin, S., Shen, L., Kachaturian, Z., Frank, R., Snyder, PJ., Molchan, S., Kaye, J., Quinn, J., Lind, B., Dolen, S., Schneider, LS., Pawluczyk, S., Spann, BM., Brewer, J., Vanderswag, H., Heidebrink, JL., Lord, JL., Johnson, K., Doody, RS., Villanueva-Meyer, J., Chowdhury, M., Stern, Y., Honig, LS., Bell, KL., Morris, JC., Ances, B., Carroll, M., Leon, S., Mintun, MA., Schneider, S., Marson, D., Griffith, R., Clark, D., Grossman, H., Mitsis, E., Romirowsky, A., deToledo-Morrell, L., Shah, RC., Duara, R., Varon, D., Roberts, P., Albert, M., Onyike, C., Kielb, S., Rusinek, H., de Leon MJ., Glodzik, L., De Santi, S., Doraiswamy, P., Petrella, JR., Coleman, R., Arnold, SE., Karlawish, JH., Wolk, D., Smith, CD., Jicha, G., Hardy, P., Lopez, OL., Oakley, M., Simpson, DM., Porsteinsson, AP., Goldstein, BS., Martin, K., Makino, KM., Ismail, M., Brand, C., Mulnard, RA., Thai, G., Mc-Adams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Diaz-Arrastia, R., King, R., Martin-Cook, K., DeVous, M., Levey, AI., Lah, JJ., Cellar, JS., Burns, JM., Anderson, HS., Swerdlow, RH., Apostolova, L., Lu, PH., Bartzokis, G., Silverman, DH., Graff-Radford, NR., Parfitt, F., Johnson, H., Farlow, MR., Hake, AM., Matthews, BR., Herring, S., van Dyck CH., Carson, RE., MacAvoy, MG., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, GY., Feldman, H., Mudge, B., Assaly, M., Kertesz, A., Rogers, J., Trost, D., Bernick, C., Munic, D., Kerwin, D., Mesulam, MM., Lipowski, K., Wu, CK., Johnson, N., Sadowsky, C., Martinez, W., Villena, T., Turner, RS., Reynolds, B., Sperling, RA., Johnson, KA., Marshall, G., Frey, M., Yesavage, J., Taylor, JL., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M., Belden, C., Jacobson, S., Kowall, N., Killiany, R., Budson, AE., Norbash, A., Johnson, PL., Obisesan, TO., Wolday, S., Bwayo, SK., Lerner, A., Hudson, L., Ogrocki, P., Fletcher, E., Carmichael, O., Olichney, J., Kittur, S., Borrie, M., Lee, TY., Bartha, R., Johnson, S., Asthana, S., Carlsson, CM., Potkin, SG., Preda, A., Nguyen, D., Tariot, P., Fleisher, A., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, DW., Kataki, M., Zimmerman, EA., Celmins, D., Brown, AD., Pearlson, GD., Blank, K., Anderson, K., Santulli, RB., Schwartz, ES., Sink, KM., Williamson, JD., Garg, P., Watkins, F., Ott, BR., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, HJ., Miller, BL., Mintzer, J., Longmire, CF., Spicer, K., Finger, E., Rachinsky, I., Drost, D., Dukart, J., Kherif, F., Mueller, K., Adaszewski, S., Schroeter, M.L., Frackowiak, R.S., and Draganski, B.

Abstract

The failure of current strategies to provide an explanation for controversial findings on the pattern of pathophysiological changes in Alzheimer's Disease (AD) motivates the necessity to develop new integrative approaches based on multi-modal neuroimaging data that captures various aspects of disease pathology. Previous studies using [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and structural magnetic resonance imaging (sMRI) report controversial results about time-line, spatial extent and magnitude of glucose hypometabolism and atrophy in AD that depend on clinical and demographic characteristics of the studied populations. Here, we provide and validate at a group level a generative anatomical model of glucose hypo-metabolism and atrophy progression in AD based on FDG-PET and sMRI data of 80 patients and 79 healthy controls to describe expected age and symptom severity related changes in AD relative to a baseline provided by healthy aging. We demonstrate a high level of anatomical accuracy for both modalities yielding strongly age- and symptom-severity- dependant glucose hypometabolism in temporal, parietal and precuneal regions and a more extensive network of atrophy in hippocampal, temporal, parietal, occipital and posterior caudate regions. The model suggests greater and more consistent changes in FDG-PET compared to sMRI at earlier and the inversion of this pattern at more advanced AD stages. Our model describes, integrates and predicts characteristic patterns of AD related pathology, uncontaminated by normal age effects, derived from multi-modal data. It further provides an integrative explanation for findings suggesting a dissociation between early- and late-onset AD. The generative model offers a basis for further development of individualized biomarkers allowing accurate early diagnosis and treatment evaluation.

Published
2013

24. Firmware development for modular instrumentation

Author

Kanago, Kerwin D., Stambaugh, Mark A., and Watkins, Brian D.

Subjects
Digital signal processor, Computer programming, Hewlett-Packard Co. -- Product information, HP Performance Signal Generator (Signal processor) -- Product information, Computer programming -- Product information, Firmware -- Product information, Signal processing -- Product information, Microcode -- Product information, Digital signal processors -- Product information
Abstract

Firmware Development for Modular Instrumentation THE PERFORMANCE SIGNAL GENERATOR (PSG) product line described in this issue represents an internally modular platform approach to the development of signal generators. There are […]

Published
1989

25. The fourth industrial fluid properties simulation challenge

Author

Case, Fiona H., primary, Brennan, John, additional, Chaka, Anne, additional, Dobbs, Kerwin D., additional, Friend, Daniel G., additional, Gordon, Peter A., additional, Moore, Jonathan D., additional, Mountain, Raymond D., additional, Olson, James D., additional, Ross, Richard B., additional, Schiller, Martin, additional, Shen, Vincent K., additional, and Stahlberg, Eric A., additional

Published
2008
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29. Relationship of hypertension, blood pressure, and blood pressure control with white matter abnormalities in the Women's Health Initiative Memory Study (WHIMS)-MRI trial.

Author

Kuller LH, Margolis KL, Gaussoin SA, Bryan NR, Kerwin D, Limacher M, Wassertheil-Smoller S, Williamson J, Robinson JG, Women's Health Initiative Memory Study Research Group, Kuller, Lewis H, Margolis, Karen L, Gaussoin, Sarah A, Bryan, Nick R, Kerwin, Diana, Limacher, Marian, Wassertheil-Smoller, Sylvia, Williamson, Jeff, and Robinson, Jennifer G

Abstract

This paper evaluates the relationship of blood pressure (BP) levels at Women's Health Initiative (WHI) baseline, treatment of hypertension, and white matter abnormalities among women in conjugated equine estrogen (CEE) and medroxyprogesterone acetate and CEE-alone arms. The WHI Memory Study-Magnetic Resonance Imaging (WHIMS-MRI) trial scanned 1424 participants. BP levels at baseline were significantly positively related to abnormal white matter lesion (WML) volumes. Participants treated for hypertension but who had BP > or = 140/90 mm Hg had the greatest amount of WML volumes. Women with untreated BP > or = 140/90 mm Hg had intermediate WML volumes. Abnormal WML volumes were related to hypertension in most areas of the brain and were greater in the frontal lobe than in the occipital, parietal, or temporal lobes. Level of BP at baseline was strongly related to amount of WML volumes. The results of the study reinforce the relationship of hypertension and BP control and white matter abnormalities in the brain. The evidence to date supports tight control of BP levels, especially beginning at younger and middle age as a possible and perhaps only way to prevent dementia. [ABSTRACT FROM AUTHOR]

Published
2010
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47. Erratum.

Author

Kerwin D

Abstract

[This corrects the article DOI: 10.1002/dad2.12334.]., (© 2023 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Published
2023
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48. Alzheimer's disease diagnosis and management: Perspectives from around the world.

Author

Kerwin D, Abdelnour C, Caramelli P, Ogunniyi A, Shi J, Zetterberg H, and Traber M

Abstract

Alzheimer's disease (AD) and other dementias are a global challenge. Early diagnosis is important to manage the disease. However, there are barriers to diagnosis that differ by region. Researchers from Brazil, China, Nigeria, Spain, and Sweden have identified key barriers to AD diagnosis in their countries. In Brazil, socioeconomic inequalities and poor recognition of dementia by physicians can prevent diagnosis. In China, a very large population and lack of physician training in dementia make diagnosis problematic. In Nigeria, socioeconomic inequalities and cultural stigma can stand in the way of diagnosis. In Spain, patient hesitancy and an overloaded health-care system are barriers to diagnosis. In Sweden, inconsistent use of biomarkers is a prominent barrier to diagnosis of AD. To support diagnosis, more focus is needed on education of patients and physicians, increased use of support services, and improved access to biomarkers to accurately diagnose AD., Competing Interests: Diana Kerwin is an advisory board member for Biogen, AbbVie, Eisai, and Roche, and has received research funding from AbbVie, Roche, Genentech, Inc., Global Alzheimer's Platform (GAP), Green Valley, Eisai, Eli Lilly, and the National Institute on Aging (NIA). Paulo Caramelli prepared material for Continuing Medical Education (CME) and participated as a speaker in symposia sponsored by Aché, Libbs, Nutricia, Roche, Sandoz, Torrent, and Zodiac laboratories; participates on advisory boards for Aché, Biogen, EMS, Nutricia, and Roche; has participated as principal investigator in a clinical trial from Roche; receives research funding from CNPq, Brazil. Jiong Shi is a scientific board member for Roche and Biogen. Henrik Zetterberg has served on scientific advisory boards and/or as a consultant for AbbVie, Alector, Eisai, Denali, Roche, Wave, Samumed, Siemens Healthineers, Pinteon Therapeutics, Nervgen, AZTherapies, CogRx, and Red Abbey Labs; has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, and Biogen; and is a co‐founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. Carla Abdelnour has received honoraria from Zambon, F. Hoffmann‐La Roche Ltd, and Schwabe Farma Ibérica S.A.U. in the last 3 years. Martin Traber is a full‐time employee of F. Hoffmann‐La Roche Ltd. Adesola Ogunniyi has no competing interests to declare., (© 2022 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Published
2022
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49. Nonlinear Z-score modeling for improved detection of cognitive abnormality.

Author

Kornak J, Fields J, Kremers W, Farmer S, Heuer HW, Forsberg L, Brushaber D, Rindels A, Dodge H, Weintraub S, Besser L, Appleby B, Bordelon Y, Bove J, Brannelly P, Caso C, Coppola G, Dever R, Dheel C, Dickerson B, Dickinson S, Dominguez S, Domoto-Reilly K, Faber K, Ferrall J, Fishman A, Fong J, Foroud T, Gavrilova R, Gearhart D, Ghazanfari B, Ghoshal N, Goldman J, Graff-Radford J, Graff-Radford N, Grant IM, Grossman M, Haley D, Hsiao J, Hsiung R, Huey ED, Irwin D, Jones D, Jones L, Kantarci K, Karydas A, Kaufer D, Kerwin D, Knopman D, Kraft R, Kramer J, Kukull W, Lapid M, Litvan I, Ljubenkov P, Lucente D, Lungu C, Mackenzie I, Maldonado M, Manoochehri M, McGinnis S, McKinley E, Mendez M, Miller B, Multani N, Onyike C, Padmanabhan J, Pantelyat A, Pearlman R, Petrucelli L, Potter M, Rademakers R, Ramos EM, Rankin K, Rascovsky K, Roberson ED, Rogalski-Miller E, Sengdy P, Shaw L, Staffaroni AM, Sutherland M, Syrjanen J, Tartaglia C, Tatton N, Taylor J, Toga A, Trojanowski J, Wang P, Wong B, Wszolek Z, Boeve B, Boxer A, and Rosen H

Abstract

Introduction: Conventional Z-scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these "adjusted" Z-scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z-scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency., Methods: In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance)., Results: Corrected Z-scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted-R 2 ., Discussion: Nonlinearly corrected Z-scores with respect to age, sex, and education with age-varying residual standard deviation allow for improved detection of non-normative extreme cognitive scores., (© 2019 Published by Elsevier Inc. on behalf of the Alzheimer's Association.)

Published
2019
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