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5. CANreduce-SP—adding psychological support to web-based adherence-focused guided self-help for cannabis users: study protocol for a three-arm randomized control trial

Author

Mestre-Pintó, J I, Fonseca, F, Schaub, Michael P; https://orcid.org/0000-0002-8375-4005, Baumgartner, Christian; https://orcid.org/0000-0002-5570-7607, Alias-Ferri, M, Torrens, M, Mestre-Pintó, J I, Fonseca, F, Schaub, Michael P; https://orcid.org/0000-0002-8375-4005, Baumgartner, Christian; https://orcid.org/0000-0002-5570-7607, Alias-Ferri, M, and Torrens, M

Abstract

Background: Cannabis is the most-frequently used illicit drug in Europe. Over the last few years in Spain, treatment demand has increased, yet most cannabis users do not seek treatment despite the related problems. A web-based self-help tool, like CANreduce 2.0, could help these users to control their consumption. Methods: This study protocol describes a three-arm randomized controlled trial (RCT) comparing the effectiveness of three approaches, in terms of reducing cannabis use among problematic cannabis users, the first two treatment arms including the Spanish version of CANreduce 2.0 (an adherence-focused, guidance-enhanced, web-based self-help tool) (1) with and (2) without psychological support; and the third group (3) treatment as usual (TAU). Study hypotheses will be tested concerning the primary outcome: change in the number of days of cannabis use over the previous week, comparing assessments at 6 weeks and 3 and 6 months follow-up between groups and against baseline. Secondary outcomes related to cannabis use will be tested similarly. Mental disorders will be explored as predictors of adherence and outcomes. Analyses will be performed on an intention-to-treat basis, then verified by complete case analyses. Discussion: This study will test how effective the Spanish version of CANreduce 2.0 (CANreduce-SP) is at reducing both the frequency and quantity of cannabis use in problematic users and whether adding psychological support increases its effectiveness. Trial registration: This trial is registered with the Clinical Trials Protocol Registration and Results System (PRS) number: NCT04517474 . Registered 18 August 2020, (Archived by archive.is https://archive.is/N1Y64 ). The project commenced in November 2020 and recruitment is anticipated to end by November 2022. Keywords: Adherence; CANreduce; Cannabis use disorder; Cognitive behavioural therapy; Guidance; Psychological support; Randomized controlled trial; Reducing cannabis; Self-help tool

Published
2022

6. Comparing a mindfulness- and CBT-based guided self-help Internet- and mobile-based intervention against a waiting list control condition as treatment for adults with frequent cannabis use: a randomized controlled trial of CANreduce 3.0

Author

Dey, Michelle; https://orcid.org/0000-0002-4300-1877, Wenger, Andreas; https://orcid.org/0000-0003-4818-4681, Baumgartner, Christian; https://orcid.org/0000-0002-5570-7607, Herrmann, Ute, Augsburger, Mareike; https://orcid.org/0000-0002-6564-0717, Haug, Severin; https://orcid.org/0000-0002-6539-5045, Malischnig, Doris; https://orcid.org/0000-0002-9630-0547, Schaub, Michael P; https://orcid.org/0000-0002-8375-4005, Dey, Michelle; https://orcid.org/0000-0002-4300-1877, Wenger, Andreas; https://orcid.org/0000-0003-4818-4681, Baumgartner, Christian; https://orcid.org/0000-0002-5570-7607, Herrmann, Ute, Augsburger, Mareike; https://orcid.org/0000-0002-6564-0717, Haug, Severin; https://orcid.org/0000-0002-6539-5045, Malischnig, Doris; https://orcid.org/0000-0002-9630-0547, and Schaub, Michael P; https://orcid.org/0000-0002-8375-4005

Abstract

Background: Though Internet- and mobile-based interventions (IMIs) and mindfulness-based interventions (generally delivered in-situ) appear effective for people with substance use disorders, IMIs incorporating mindfulness are largely missing, including those targeting frequent cannabis use. Methods: This paper details the protocol for a three-arm randomized controlled trial comparing a mindfulness-based self-help IMI (arm 1) and cognitive-behavioral therapy (CBT)-based self-help IMI (arm 2) versus being on a waiting list (arm 3) in their effectiveness reducing cannabis use in frequent cannabis users. Predictors of retention, adherence and treatment outcomes will be identified and similarities between the two active intervention arms explored. Both active interventions last six weeks and consist of eight modules designed to reduce cannabis use and common mental health symptoms. With a targeted sample size of n = 210 per treatment arm, data will be collected at baseline immediately before program use is initiated; at six weeks, immediately after program completion; and at three and six months post baseline assessment to assess the retention of any gains achieved during treatment. The primary outcome will be number of days of cannabis use over the preceding 30 days. Secondary outcomes will include further measures of cannabis use and use of other substances, changes in mental health symptoms and mindfulness, client satisfaction, intervention retention and adherence, and adverse effects. Data analysis will follow ITT principles and primarily employ (generalized) linear mixed models. Discussion: This RCT will provide important insights into the effectiveness of an IMI integrating mindfulness to reduce cannabis use in frequent cannabis users. Trial registration: International Standard Randomized Controlled Trial Number Registry: ISRCTN14971662 ; date of registration: 09/09/2021. Keywords: Cannabis; Cognitive-behavioral therapy; Internet-based intervention; Mindfulness; Ra

Published
2022

7. Developing and testing the effectiveness of a novel online integrated treatment for problem gambling and tobacco smoking: a protocol for an open-label randomized controlled trial

Author

Bilevicius, Elena, Single, Alanna, Baumgartner, Christian; https://orcid.org/0000-0002-5570-7607, Bui, Van, Kempe, Tyler, Schaub, Michael P; https://orcid.org/0000-0002-8375-4005, Stewart, Sherry H, MacKillop, James, Hodgins, David C, Wardell, Jeffrey D, O’Connor, Roisin, Read, Jennifer, Hadjistavropoulos, Heather, Sundstrom, Christopher, Keough, Matthew T, Bilevicius, Elena, Single, Alanna, Baumgartner, Christian; https://orcid.org/0000-0002-5570-7607, Bui, Van, Kempe, Tyler, Schaub, Michael P; https://orcid.org/0000-0002-8375-4005, Stewart, Sherry H, MacKillop, James, Hodgins, David C, Wardell, Jeffrey D, O’Connor, Roisin, Read, Jennifer, Hadjistavropoulos, Heather, Sundstrom, Christopher, and Keough, Matthew T

Abstract

Background Gambling and tobacco smoking are highly comorbid among North American adults. However, there is a paucity of treatment options that are integrated (i.e. targeting both gambling and tobacco smoking simultaneously), accessible, and evidence based. Methods The aim of this two-arm open-label randomized controlled trial is to examine the effectiveness of an online, self-guided integrated treatment for problem gambling and tobacco smoking. A target sample of 214 participants will be recruited and be randomized into either an 8-week integrated or gambling only control condition. Both conditions will consist of seven online modules following cognitive behavioural therapy and motivational interviewing principles. Our three primary outcomes are (1) the number of days gambled, (2) money spent on gambling activities, and (3) time spent in gambling activities. Secondary outcomes include gambling disorder symptoms, cigarette use, and nicotine dependence symptoms. Assessments will be completed at baseline, at completion (i.e. 8 weeks from baseline), and at follow-up (i.e. 24 weeks from baseline). Generalized linear mixed modelling will be used to evaluate our primary and secondary outcomes. We expect that participants receiving online integrated treatment will show larger reductions in gambling relative to those receiving a control gambling only intervention. We further hypothesize that reductions in smoking will mediate these group differences. Discussion The rates of problem gambling and tobacco smoking are high in North America; yet, the treatment options for both are limited, with no integrated treatments available. If supported, our pilot study will be a cost-effective and accessible way to improve treatments for co-occurring problem gambling and tobacco use.

Published
2020

8. International Society for Therapeutic Ultrasound Conference 2016

Author

Fowlkes, Brian, Ghanouni, Pejman, Sanghvi, Narendra, Coussios, Constantin, Lyon, Paul C., Gray, Michael, Mannaris, Christophoros, Victor, Marie de Saint, Stride, Eleanor, Cleveland, Robin, Carlisle, Robert, Wu, Feng, Middleton, Mark, Gleeson, Fergus, Aubry, Jean-Franҫois, Pauly, Kim Butts, Moonen, Chrit, Vortman, Jacob, Sharabi, Shirley, Daniels, Dianne, Last, David, Guez, David, Levy, Yoav, Volovick, Alexander, Grinfeld, Javier, Rachmilevich, Itay, Amar, Talia, Zibly, Zion, Mardor, Yael, Harnof, Sagi, Plaksin, Michael, Weissler, Yoni, Shoham, Shy, Kimmel, Eitan, Naor, Omer, Farah, Nairouz, Paeng, Dong-Guk, Xu, Zhiyuan, Snell, John, Quigg, Anders H., Eames, Matthew, Jin, Changzhu, Everstine, Ashli C., Sheehan, Jason P., Lopes, Beatriz S., Kassell, Neal, Looi, Thomas, Khokhlova, Vera, Mougenot, Charles, Hynynen, Kullervo, Drake, James, Slayton, Michael, Amodei, Richard C., Compton, Keegan, McNelly, Ashley, Latt, Daniel, Kearney, John, Melodelima, David, Dupre, Aurelien, Chen, Yao, Perol, David, Vincenot, Jeremy, Chapelon, Jean-Yves, Rivoire, Michel, Guo, Wei, Ren, Guoxin, Shen, Guofeng, Neidrauer, Michael, Zubkov, Leonid, Weingarten, Michael S., Margolis, David J., Lewin, Peter A., McDannold, Nathan, Sutton, Jonathan, Vykhodtseva, Natalia, Livingstone, Margaret, Kobus, Thiele, Zhang, Yong-Zhi, Schwartz, Michael, Huang, Yuexi, Lipsman, Nir, Jain, Jennifer, Chapman, Martin, Sankar, Tejas, Lozano, Andres, Yeung, Robert, Damianou, Christakis, Papadopoulos, Nikolaos, Brokman, Omer, Zadicario, Eyal, Brenner, Ori, Castel, David, Wu, Shih-Ying, Grondin, Julien, Zheng, Wenlan, Heidmann, Marc, Karakatsani, Maria Eleni, Sánchez, Carlos J. Sierra, Ferrera, Vincent, Konofagou, Elisa E., Yiannakou, Marinos, Cho, HongSeok, Lee, Hwayoun, Han, Mun, Choi, Jong-Ryul, Lee, Taekwan, Ahn, Sanghyun, Chang, Yongmin, Park, Juyoung, Ellens, Nicholas, Partanen, Ari, Farahani, Keyvan, Airan, Raag, Carpentier, Alexandre, Canney, Michael, Vignot, Alexandre, Lafon, Cyril, Delattre, Jean-yves, Idbaih, Ahmed, Odéen, Henrik, Bolster, Bradley, Jeong, Eun Kee, Parker, Dennis L., Gaur, Pooja, Feng, Xue, Fielden, Samuel, Meyer, Craig, Werner, Beat, Grissom, William, Marx, Michael, Weber, Hans, Taviani, Valentina, Hargreaves, Brian, Tanaka, Jun, Kikuchi, Kentaro, Ishijima, Ayumu, Azuma, Takashi, Minamihata, Kosuke, Yamaguchi, Satoshi, Nagamune, Teruyuki, Sakuma, Ichiro, Takagi, Shu, Santin, Mathieu D., Marsac, Laurent, Maimbourg, Guillaume, Monfort, Morgane, Larrat, Benoit, François, Chantal, Lehéricy, Stéphane, Tanter, Mickael, Samiotaki, Gesthimani, Wang, Shutao, Acosta, Camilo, Feinberg, Eliza R., Kovacs, Zsofia I., Tu, Tsang-Wei, Papadakis, Georgios Z., Reid, William C., Hammoud, Dima A., Frank, Joseph A., Kovacs, Zsofia i., Kim, Saejeong, Jikaria, Neekita, Bresler, Michele, Qureshi, Farhan, Xia, Jingjing, Tsui, Po-Shiang, Liu, Hao-Li, Plata, Juan C., Sveinsson, Bragi, Salgaonkar, Vasant A., Adams, Matthew, Diederich, Chris, Ozhinsky, Eugene, Bucknor, Matthew D., Rieke, Viola, Mikhail, Andrew, Severance, Lauren, Negussie, Ayele H., Wood, Bradford, de Greef, Martijn, Schubert, Gerald, Ries, Mario, Poorman, Megan E., Dockery, Mary, Chaplin, Vandiver, Dudzinski, Stephanie O., Spears, Ryan, Caskey, Charles, Giorgio, Todd, Costa, Marcia M., Papaevangelou, Efthymia, Shah, Anant, Rivens, Ian, Box, Carol, Bamber, Jeff, ter Haar, Gail, Burks, Scott R., Nagle, Matthew, Nguyen, Ben, Milo, Blerta, Le, Nhan M., Song, Shaozhen, Zhou, Kanheng, Nabi, Ghulam, Huang, Zhihong, Ben-Ezra, Shmuel, Rosen, Shani, Mihcin, Senay, Strehlow, Jan, Karakitsios, Ioannis, Le, Nhan, Schwenke, Michael, Demedts, Daniel, Prentice, Paul, Haase, Sabrina, Preusser, Tobias, Melzer, Andreas, Mestas, Jean-Louis, Chettab, Kamel, Gomez, Gustavo Stadthagen, Dumontet, Charles, Werle, Bettina, Marquet, Fabrice, Bour, Pierre, Vaillant, Fanny, Amraoui, Sana, Dubois, Rémi, Ritter, Philippe, Haïssaguerre, Michel, Hocini, Mélèze, Bernus, Olivier, Quesson, Bruno, Livneh, Amit, Adam, Dan, Robin, Justine, Arnal, Bastien, Fink, Mathias, Pernot, Mathieu, Khokhlova, Tatiana D., Schade, George R., Wang, Yak-Nam, Kreider, Wayne, Simon, Julianna, Starr, Frank, Karzova, Maria, Maxwell, Adam, Bailey, Michael R., Lundt, Jonathan E., Allen, Steven P., Sukovich, Jonathan R., Hall, Timothy, Xu, Zhen, May, Philip, Lin, Daniel W., Constans, Charlotte, Deffieux, Thomas, Aubry, Jean-Francois, Park, Eun-Joo, Ahn, Yun Deok, Kang, Soo Yeon, Park, Dong-Hyuk, Lee, Jae Young, Vidal-Jove, J., Perich, E., Ruiz, A., Jaen, A., Eres, N., del Castillo, M. Alvarez, Myers, Rachel, Kwan, James, Coviello, Christian, Rowe, Cliff, Crake, Calum, Finn, Sean, Jackson, Edward, Pouliopoulos, Antonios, Li, Caiqin, Tinguely, Marc, Tang, Meng-Xing, Garbin, Valeria, Choi, James J., Folkes, Lisa, Stratford, Michael, Nwokeoha, Sandra, Li, Tong, Farr, Navid, D’Andrea, Samantha, Gravelle, Kayla, Chen, Hong, Lee, Donghoon, Hwang, Joo Ha, Tardoski, Sophie, Ngo, Jacqueline, Gineyts, Evelyne, Roux, Jean-Pau, Clézardin, Philippe, Conti, Allegra, Magnin, Rémi, Gerstenmayer, Matthieu, Lux, François, Tillement, Olivier, Mériaux, Sébastien, Penna, Stefania Della, Romani, Gian Luca, Dumont, Erik, Sun, Tao, Power, Chanikarn, Miller, Eric, Sapozhnikov, Oleg, Tsysar, Sergey, Yuldashev, Petr V., Svet, Victor, Li, Dongli, Pellegrino, Antonio, Petrinic, Nik, Siviour, Clive, Jerusalem, Antoine, Yuldashev, Peter V., Cunitz, Bryan W., Dunmire, Barbrina, Inserra, Claude, Guedra, Matthieu, Mauger, Cyril, Gilles, Bruno, Solovchuk, Maxim, Sheu, Tony W. H., Thiriet, Marc, Zhou, Yufeng, Neufeld, Esra, Baumgartner, Christian, Payne, Davnah, Kyriakou, Adamos, Kuster, Niels, Xiao, Xu, McLeod, Helen, Dillon, Christopher, Payne, Allison, Khokhova, Vera A., Sinilshchikov, Ilya, Andriyakhina, Yulia, Rybyanets, Andrey, Shvetsova, Natalia, Berkovich, Alex, Shvetsov, Igor, Shaw, Caroline J., Civale, John, Giussani, Dino, Lees, Christoph, Ozenne, Valery, Toupin, Solenn, Salgaonkar, Vasant, Kaye, Elena, Monette, Sebastien, Maybody, Majid, Srimathveeravalli, Govindarajan, Solomon, Stephen, Gulati, Amitabh, Bezzi, Mario, Jenne, Jürgen W., Lango, Thomas, Müller, Michael, Sat, Giora, Tanner, Christine, Zangos, Stephan, Günther, Matthias, Dinh, Au Hoang, Niaf, Emilie, Bratan, Flavie, Guillen, Nicolas, Souchon, Rémi, Lartizien, Carole, Crouzet, Sebastien, Rouviere, Olivier, Han, Yang, Payen, Thomas, Palermo, Carmine, Sastra, Steve, Olive, Kenneth, van Breugel, Johanna M., van den Bosch, Maurice A., Fellah, Benjamin, Le Bihan, Denis, Hernandez-Garcia, Luis, Cain, Charles A., Lyka, Erasmia, Elbes, Delphine, Li, Chunhui, Tamano, Satoshi, Jimbo, Hayato, Yoshizawa, Shin, Fujiwara, Keisuke, Itani, Kazunori, Umemura, Shin-ichiro, Stoianovici, Dan, Zaini, Zulfadhli, Takagi, Ryo, Zong, Shenyan, Watkins, Ron, Pascal-Tenorio, Aurea, Jones, Peter, Butts-Pauly, Kim, Bouley, Donna, Chen, Yazhu, Lin, Chung-Yin, Hsieh, Han-Yi, Wei, Kuo-Chen, Garnier, Camille, Renault, Gilles, Seifabadi, Reza, Wilson, Emmanuel, Eranki, Avinash, Kim, Peter, Lübke, Dennis, Huber, Peter, Georgii, Joachim, Dresky, Caroline V., Haller, Julian, Yarmolenko, Pavel, Sharma, Karun, Celik, Haydar, Li, Guofeng, Qiu, Weibao, Zheng, Hairong, Tsai, Meng-Yen, Chu, Po-Chun, Webb, Taylor, Vyas, Urvi, Walker, Matthew, Zhong, Jidan, Waspe, Adam C., Hodaie, Mojgan, Yang, Feng-Yi, Huang, Sin-Luo, Zur, Yuval, Assif, Benny, Aurup, Christian, Kamimura, Hermes, Carneiro, Antonio A., Rothlübbers, Sven, Schwaab, Julia, Houston, Graeme, Azhari, Haim, Weiss, Noam, Sosna, Jacob, Goldberg, S. Nahum, Barrere, Victor, Jang, Kee W., Lewis, Bobbi, Wang, Xiaotong, Suomi, Visa, Edwards, David, Larrabee, Zahary, Hananel, Arik, Rafaely, Boaz, Debbiny, Rasha Elaimy, Dekel, Carmel Zeltser, Assa, Michael, Menikou, George, Mouratidis, Petros, Pineda-Pardo, José A., de Pedro, Marta Del Álamo, Martinez, Raul, Hernandez, Frida, Casas, Silvia, Oliver, Carlos, Pastor, Patricia, Vela, Lidia, Obeso, Jose, Greillier, Paul, Zorgani, Ali, Catheline, Stefan, Solovov, Vyacheslav, Vozdvizhenskiy, Michael O., Orlov, Andrew E., Wu, Chueh-Hung, Sun, Ming-Kuan, Shih, Tiffany T., Chen, Wen-Shiang, Prieur, Fabrice, Pillon, Arnaud, Cartron, Valerie, Cebe, Patrick, Chansard, Nathalie, Lafond, Maxime, Seya, Pauline Muleki, Bera, Jean-Christophe, Boissenot, Tanguy, Fattal, Elias, Bordat, Alexandre, Chacun, Helene, Guetin, Claire, Tsapis, Nicolas, Maruyama, Kazuo, Unga, Johan, Suzuki, Ryo, Fant, Cécile, Rogez, Bernadette, Afadzi, Mercy, Myhre, Ola Finneng, Vea, Siri, Bjørkøy, Astrid, Yemane, Petros Tesfamichael, van Wamel, Annemieke, Berg, Sigrid, Hansen, Rune, Angelsen, Bjørn, and Davies, Catharina

Subjects
Meeting Abstracts
Published
2017

9. MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm

Author

Adilson Mendes Ricardo, Christian Baumgartner, Armin Graber, Fabio Ribeiro Cerqueira, and Alcione de Paiva Oliveira

Subjects
0301 basic medicine, Artificial neural network, Proteomics, Phosphoproteomics, Proteome, Computer science, Peptide/protein identification, ProteinProphet, Peptide, Tandem mass spectrometry, Biochemistry, law.invention, 03 medical and health sciences, Cost sensitive classification, Structural Biology, law, Tandem Mass Spectrometry, Shotgun proteomics, Sensitivity (control systems), Databases, Protein, Molecular Biology, Data mining, Probability, chemistry.chemical_classification, Applied Mathematics, Research, A protein, Computer Science Applications, Task (computing), Identification (information), 030104 developmental biology, chemistry, Venn diagram, Protein identification, Neural Networks, Computer, DNA microarray, Peptides, Algorithm, Algorithms, Software
Abstract

Background This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable. The target-decoy database strategy is largely used for error estimation in PSM assessment. However, in general, that strategy does not account for sensitivity. Results In a previous study, we proposed the method MUMAL that applies an artificial neural network to effectively generate a model to classify PSMs using decoy hits with increased sensitivity. Nevertheless, the present approach shows that the sensitivity can be further improved with the use of a cost matrix associated with the learning algorithm. We also demonstrate that using a threshold selector algorithm for probability adjustment leads to more coherent probability values assigned to the PSMs. Our new approach, termed MUMAL2, provides a two-fold contribution to shotgun proteomics. First, the increase in the number of correctly interpreted spectra in the peptide level augments the chance of identifying more proteins. Second, the more appropriate PSM probability values that are produced by the threshold selector algorithm impact the protein inference stage performed by programs that take probabilities into account, such as ProteinProphet. Our experiments demonstrate that MUMAL2 reached around 15% of improvement in sensitivity compared to the best current method. Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the probabilities generated by our model are in fact appropriate. Finally, Venn diagrams comparing MUMAL2 with the best current method show that the number of exclusive peptides found by our method was nearly 4-fold higher, which directly impacts the proteome coverage. Conclusions The inclusion of a cost matrix and a probability threshold selector algorithm to the learning task further improves the target-decoy database analysis for identifying peptides, which optimally contributes to the challenging task of protein level identification, resulting in a powerful computational tool for shotgun proteomics.

Published
2016

10. MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.

Author

Cerqueira, Fabio Ribeiro, Ricardo, Adilson Mendes, de Paiva Oliveira, Alcione, Graber, Armin, and Baumgartner, Christian

Subjects
PROTEOMICS, MOLECULAR biology, ARTIFICIAL neural networks, TANDEM mass spectrometry, SENSITIVITY & specificity (Statistics)
Abstract

Background: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable. The target-decoy database strategy is largely used for error estimation in PSM assessment. However, in general, that strategy does not account for sensitivity. Results: In a previous study, we proposed the method MUMAL that applies an artificial neural network to effectively generate a model to classify PSMs using decoy hits with increased sensitivity. Nevertheless, the present approach shows that the sensitivity can be further improved with the use of a cost matrix associated with the learning algorithm. We also demonstrate that using a threshold selector algorithm for probability adjustment leads to more coherent probability values assigned to the PSMs. Our new approach, termed MUMAL2, provides a two-fold contribution to shotgun proteomics. First, the increase in the number of correctly interpreted spectra in the peptide level augments the chance of identifying more proteins. Second, the more appropriate PSM probability values that are produced by the threshold selector algorithm impact the protein inference stage performed by programs that take probabilities into account, such as ProteinProphet. Our experiments demonstrate that MUMAL2 reached around 15% of improvement in sensitivity compared to the best current method. Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the probabilities generated by our model are in fact appropriate. Finally, Venn diagrams comparing MUMAL2 with the best current method show that the number of exclusive peptides found by our method was nearly 4-fold higher, which directly impacts the proteome coverage. Conclusions: The inclusion of a cost matrix and a probability threshold selector algorithm to the learning task further improves the target-decoy database analysis for identifying peptides, which optimally contributes to the challenging task of protein level identification, resulting in a powerful computational tool for shotgun proteomics. [ABSTRACT FROM AUTHOR]

Published
2016
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11. Simulation and evaluation of freeze-thaw cryoablation scenarios for the treatment of cardiac arrhythmias.

Author

Handler, Michael, Fischer, Gerald, Seger, Michael, Kienast, Roland, Hanser, Friedrich, and Baumgartner, Christian

Abstract

Background: Cardiac cryoablation is a minimally invasive procedure to treat cardiac arrhythmias by cooling cardiac tissues responsible for the cardiac arrhythmia to freezing temperatures. Although cardiac cryoablation offers a gentler treatment than radiofrequency ablation, longer interventions and higher recurrence rates reduce the clinical acceptance of this technique. Computer models of ablation scenarios allow for a closer examination of temperature distributions in the myocardium and evaluation of specific effects of applied freeze-thaw protocols in a controlled environment. Methods: In this work multiple intervention scenarios with two freeze-thaw cycles were simulated with varying durations and starting times of the interim thawing phase using a finite element model verified by in-vivo measurements and data from literature. To evaluate the effects of different protocols, transmural temperature distributions and iceball dimensions were compared over time. Cryoadhesion durations of the applicator were estimated in the interimthawing phase with varying thawing phase starting times. In addition, the increase of cooling rates was compared between the freezing phases, and the thawing rates of interim thawing phases were analyzed over transmural depth. Results: It could be shown that the increase of cooling rate, the regions undergoing additional phase changes and depths of selected temperatures depend on the chosen ablation protocol. Only small differences of the estimated cryoadhesion duration were found for ablation scenarios with interim thawing phase start after 90 s freezing. Conclusions: By the presented model a quantification of effects responsible for cell death is possible, allowing for the analysis and optimization of cryoablation scenarios which contribute to a higher clinical acceptance of cardiac cryoablation. [ABSTRACT FROM AUTHOR]

Published
2015
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12. International Society for Therapeutic Ultrasound Conference 2016: Tel Aviv, Israel. 14-18 March, 2016

Author

Fowlkes, Brian, Ghanouni, Pejman, Sanghvi, Narendra, Coussios, Constantin, Lyon, Paul C., Gray, Michael, Mannaris, Christophoros, Victor, Marie de Saint, Stride, Eleanor, Cleveland, Robin, Carlisle, Robert, Wu, Feng, Middleton, Mark, Gleeson, Fergus, Aubry, Jean-Franҫois, Pauly, Kim Butts, Moonen, Chrit, Vortman, Jacob, Sharabi, Shirley, Daniels, Dianne, Last, David, Guez, David, Levy, Yoav, Volovick, Alexander, Grinfeld, Javier, Rachmilevich, Itay, Amar, Talia, Zibly, Zion, Mardor, Yael, Harnof, Sagi, Plaksin, Michael, Weissler, Yoni, Shoham, Shy, Kimmel, Eitan, Naor, Omer, Farah, Nairouz, Paeng, Dong-Guk, Xu, Zhiyuan, Snell, John, Quigg, Anders H., Eames, Matthew, Jin, Changzhu, Everstine, Ashli C., Sheehan, Jason P., Lopes, Beatriz S., Kassell, Neal, Looi, Thomas, Khokhlova, Vera, Mougenot, Charles, Hynynen, Kullervo, Drake, James, Slayton, Michael, Amodei, Richard C., Compton, Keegan, McNelly, Ashley, Latt, Daniel, Kearney, John, Melodelima, David, Dupre, Aurelien, Chen, Yao, Perol, David, Vincenot, Jeremy, Chapelon, Jean-Yves, Rivoire, Michel, Guo, Wei, Ren, Guoxin, Shen, Guofeng, Neidrauer, Michael, Zubkov, Leonid, Weingarten, Michael S., Margolis, David J., Lewin, Peter A., McDannold, Nathan, Sutton, Jonathan, Vykhodtseva, Natalia, Livingstone, Margaret, Kobus, Thiele, Zhang, Yong-Zhi, Schwartz, Michael, Huang, Yuexi, Lipsman, Nir, Jain, Jennifer, Chapman, Martin, Sankar, Tejas, Lozano, Andres, Yeung, Robert, Damianou, Christakis, Papadopoulos, Nikolaos, Brokman, Omer, Zadicario, Eyal, Brenner, Ori, Castel, David, Wu, Shih-Ying, Grondin, Julien, Zheng, Wenlan, Heidmann, Marc, Karakatsani, Maria Eleni, Sánchez, Carlos J. Sierra, Ferrera, Vincent, Konofagou, Elisa E., Yiannakou, Marinos, Cho, HongSeok, Lee, Hwayoun, Han, Mun, Choi, Jong-Ryul, Lee, Taekwan, Ahn, Sanghyun, Chang, Yongmin, Park, Juyoung, Ellens, Nicholas, Partanen, Ari, Farahani, Keyvan, Airan, Raag, Carpentier, Alexandre, Canney, Michael, Vignot, Alexandre, Lafon, Cyril, Delattre, Jean-yves, Idbaih, Ahmed, Odéen, Henrik, Bolster, Bradley, Jeong, Eun Kee, Parker, Dennis L., Gaur, Pooja, Feng, Xue, Fielden, Samuel, Meyer, Craig, Werner, Beat, Grissom, William, Marx, Michael, Weber, Hans, Taviani, Valentina, Hargreaves, Brian, Tanaka, Jun, Kikuchi, Kentaro, Ishijima, Ayumu, Azuma, Takashi, Minamihata, Kosuke, Yamaguchi, Satoshi, Nagamune, Teruyuki, Sakuma, Ichiro, Takagi, Shu, Santin, Mathieu D., Marsac, Laurent, Maimbourg, Guillaume, Monfort, Morgane, Larrat, Benoit, François, Chantal, Lehéricy, Stéphane, Tanter, Mickael, Samiotaki, Gesthimani, Wang, Shutao, Acosta, Camilo, Feinberg, Eliza R., Kovacs, Zsofia I., Tu, Tsang-Wei, Papadakis, Georgios Z., Reid, William C., Hammoud, Dima A., Frank, Joseph A., Kovacs, Zsofia i., Kim, Saejeong, Jikaria, Neekita, Bresler, Michele, Qureshi, Farhan, Xia, Jingjing, Tsui, Po-Shiang, Liu, Hao-Li, Plata, Juan C., Sveinsson, Bragi, Salgaonkar, Vasant A., Adams, Matthew, Diederich, Chris, Ozhinsky, Eugene, Bucknor, Matthew D., Rieke, Viola, Mikhail, Andrew, Severance, Lauren, Negussie, Ayele H., Wood, Bradford, de Greef, Martijn, Schubert, Gerald, Ries, Mario, Poorman, Megan E., Dockery, Mary, Chaplin, Vandiver, Dudzinski, Stephanie O., Spears, Ryan, Caskey, Charles, Giorgio, Todd, Costa, Marcia M., Papaevangelou, Efthymia, Shah, Anant, Rivens, Ian, Box, Carol, Bamber, Jeff, ter Haar, Gail, Burks, Scott R., Nagle, Matthew, Nguyen, Ben, Milo, Blerta, Le, Nhan M., Song, Shaozhen, Zhou, Kanheng, Nabi, Ghulam, Huang, Zhihong, Ben-Ezra, Shmuel, Rosen, Shani, Mihcin, Senay, Strehlow, Jan, Karakitsios, Ioannis, Le, Nhan, Schwenke, Michael, Demedts, Daniel, Prentice, Paul, Haase, Sabrina, Preusser, Tobias, Melzer, Andreas, Mestas, Jean-Louis, Chettab, Kamel, Gomez, Gustavo Stadthagen, Dumontet, Charles, Werle, Bettina, Marquet, Fabrice, Bour, Pierre, Vaillant, Fanny, Amraoui, Sana, Dubois, Rémi, Ritter, Philippe, Haïssaguerre, Michel, Hocini, Mélèze, Bernus, Olivier, Quesson, Bruno, Livneh, Amit, Adam, Dan, Robin, Justine, Arnal, Bastien, Fink, Mathias, Pernot, Mathieu, Khokhlova, Tatiana D., Schade, George R., Wang, Yak-Nam, Kreider, Wayne, Simon, Julianna, Starr, Frank, Karzova, Maria, Maxwell, Adam, Bailey, Michael R., Lundt, Jonathan E., Allen, Steven P., Sukovich, Jonathan R., Hall, Timothy, Xu, Zhen, May, Philip, Lin, Daniel W., Constans, Charlotte, Deffieux, Thomas, Aubry, Jean-Francois, Park, Eun-Joo, Ahn, Yun Deok, Kang, Soo Yeon, Park, Dong-Hyuk, Lee, Jae Young, Vidal-Jove, J., Perich, E., Ruiz, A., Jaen, A., Eres, N., del Castillo, M. Alvarez, Myers, Rachel, Kwan, James, Coviello, Christian, Rowe, Cliff, Crake, Calum, Finn, Sean, Jackson, Edward, Pouliopoulos, Antonios, Li, Caiqin, Tinguely, Marc, Tang, Meng-Xing, Garbin, Valeria, Choi, James J., Folkes, Lisa, Stratford, Michael, Nwokeoha, Sandra, Li, Tong, Farr, Navid, D’Andrea, Samantha, Gravelle, Kayla, Chen, Hong, Lee, Donghoon, Hwang, Joo Ha, Tardoski, Sophie, Ngo, Jacqueline, Gineyts, Evelyne, Roux, Jean-Pau, Clézardin, Philippe, Conti, Allegra, Magnin, Rémi, Gerstenmayer, Matthieu, Lux, François, Tillement, Olivier, Mériaux, Sébastien, Penna, Stefania Della, Romani, Gian Luca, Dumont, Erik, Sun, Tao, Power, Chanikarn, Miller, Eric, Sapozhnikov, Oleg, Tsysar, Sergey, Yuldashev, Petr V., Svet, Victor, Li, Dongli, Pellegrino, Antonio, Petrinic, Nik, Siviour, Clive, Jerusalem, Antoine, Yuldashev, Peter V., Cunitz, Bryan W., Dunmire, Barbrina, Inserra, Claude, Guedra, Matthieu, Mauger, Cyril, Gilles, Bruno, Solovchuk, Maxim, Sheu, Tony W. H., Thiriet, Marc, Zhou, Yufeng, Neufeld, Esra, Baumgartner, Christian, Payne, Davnah, Kyriakou, Adamos, Kuster, Niels, Xiao, Xu, McLeod, Helen, Dillon, Christopher, Payne, Allison, Khokhova, Vera A., Sinilshchikov, Ilya, Andriyakhina, Yulia, Rybyanets, Andrey, Shvetsova, Natalia, Berkovich, Alex, Shvetsov, Igor, Shaw, Caroline J., Civale, John, Giussani, Dino, Lees, Christoph, Ozenne, Valery, Toupin, Solenn, Salgaonkar, Vasant, Kaye, Elena, Monette, Sebastien, Maybody, Majid, Srimathveeravalli, Govindarajan, Solomon, Stephen, Gulati, Amitabh, Bezzi, Mario, Jenne, Jürgen W., Lango, Thomas, Müller, Michael, Sat, Giora, Tanner, Christine, Zangos, Stephan, Günther, Matthias, Dinh, Au Hoang, Niaf, Emilie, Bratan, Flavie, Guillen, Nicolas, Souchon, Rémi, Lartizien, Carole, Crouzet, Sebastien, Rouviere, Olivier, Han, Yang, Payen, Thomas, Palermo, Carmine, Sastra, Steve, Olive, Kenneth, van Breugel, Johanna M., van den Bosch, Maurice A., Fellah, Benjamin, Le Bihan, Denis, Hernandez-Garcia, Luis, Cain, Charles A., Lyka, Erasmia, Elbes, Delphine, Li, Chunhui, Tamano, Satoshi, Jimbo, Hayato, Yoshizawa, Shin, Fujiwara, Keisuke, Itani, Kazunori, Umemura, Shin-ichiro, Stoianovici, Dan, Zaini, Zulfadhli, Takagi, Ryo, Zong, Shenyan, Watkins, Ron, Pascal-Tenorio, Aurea, Jones, Peter, Butts-Pauly, Kim, Bouley, Donna, Chen, Yazhu, Lin, Chung-Yin, Hsieh, Han-Yi, Wei, Kuo-Chen, Garnier, Camille, Renault, Gilles, Seifabadi, Reza, Wilson, Emmanuel, Eranki, Avinash, Kim, Peter, Lübke, Dennis, Huber, Peter, Georgii, Joachim, Dresky, Caroline V., Haller, Julian, Yarmolenko, Pavel, Sharma, Karun, Celik, Haydar, Li, Guofeng, Qiu, Weibao, Zheng, Hairong, Tsai, Meng-Yen, Chu, Po-Chun, Webb, Taylor, Vyas, Urvi, Walker, Matthew, Zhong, Jidan, Waspe, Adam C., Hodaie, Mojgan, Yang, Feng-Yi, Huang, Sin-Luo, Zur, Yuval, Assif, Benny, Aurup, Christian, Kamimura, Hermes, Carneiro, Antonio A., Rothlübbers, Sven, Schwaab, Julia, Houston, Graeme, Azhari, Haim, Weiss, Noam, Sosna, Jacob, Goldberg, S. Nahum, Barrere, Victor, Jang, Kee W., Lewis, Bobbi, Wang, Xiaotong, Suomi, Visa, Edwards, David, Larrabee, Zahary, Hananel, Arik, Rafaely, Boaz, Debbiny, Rasha Elaimy, Dekel, Carmel Zeltser, Assa, Michael, Menikou, George, Mouratidis, Petros, Pineda-Pardo, José A., de Pedro, Marta Del Álamo, Martinez, Raul, Hernandez, Frida, Casas, Silvia, Oliver, Carlos, Pastor, Patricia, Vela, Lidia, Obeso, Jose, Greillier, Paul, Zorgani, Ali, Catheline, Stefan, Solovov, Vyacheslav, Vozdvizhenskiy, Michael O., Orlov, Andrew E., Wu, Chueh-Hung, Sun, Ming-Kuan, Shih, Tiffany T., Chen, Wen-Shiang, Prieur, Fabrice, Pillon, Arnaud, Cartron, Valerie, Cebe, Patrick, Chansard, Nathalie, Lafond, Maxime, Seya, Pauline Muleki, Bera, Jean-Christophe, Boissenot, Tanguy, Fattal, Elias, Bordat, Alexandre, Chacun, Helene, Guetin, Claire, Tsapis, Nicolas, Maruyama, Kazuo, Unga, Johan, Suzuki, Ryo, Fant, Cécile, Rogez, Bernadette, Afadzi, Mercy, Myhre, Ola Finneng, Vea, Siri, Bjørkøy, Astrid, Yemane, Petros Tesfamichael, van Wamel, Annemieke, Berg, Sigrid, Hansen, Rune, Angelsen, Bjørn, and Davies, Catharina

Published
2017
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13. MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques.

Author

Cerqueira, Fabio R., Ferreira, Ricardo S., Oliveira, Alcione P., Gomes, Andreia P., Ramos, Humberto J.O., Graber, Armin, and Baumgartner, Christian

Subjects
MULTIVARIATE analysis, BIOMARKERS, PROTEOMICS, MASS spectrometry, AMINO acid sequence
Abstract

Background: The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry) is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs) needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. Results: Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches. Conclusion: Our approach not only enhances the computational performance, and thus the turn around time of MS-based experiments in proteomics, but also improves the information content with benefits of a higher proteome coverage. This improvement, for instance, increases the chance to identify important drug targets or biomarkers for drug development or molecular diagnostics. [ABSTRACT FROM AUTHOR]

Published
2012
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14. Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with uncertainty-based quality-control.

Author

Puyol-Antón, Esther, Ruijsink, Bram, Baumgartner, Christian F., Masci, Pier-Giorgio, Sinclair, Matthew, Konukoglu, Ender, Razavi, Reza, and King, Andrew P.

Subjects
*AUTOMATION, *CARDIOVASCULAR system, *CHEMICAL elements, *HEART, *ARTIFICIAL neural networks, *PROBABILITY theory, *QUALITY control, *TISSUES, *UNCERTAINTY, *RESEARCH bias, *MAGNETIC resonance angiography
Abstract

Background: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Native T1 mapping in particular has shown promise as a useful biomarker to support diagnostic, therapeutic and prognostic decision-making in ischaemic and non-ischaemic cardiomyopathies. Methods: Convolutional neural networks (CNNs) with Bayesian inference are a category of artificial neural networks which model the uncertainty of the network output. This study presents an automated framework for tissue characterisation from native shortened modified Look-Locker inversion recovery ShMOLLI T1 mapping at 1.5 T using a Probabilistic Hierarchical Segmentation (PHiSeg) network (PHCUMIS 119–127, 2019). In addition, we use the uncertainty information provided by the PHiSeg network in a novel automated quality control (QC) step to identify uncertain T1 values. The PHiSeg network and QC were validated against manual analysis on a cohort of the UK Biobank containing healthy subjects and chronic cardiomyopathy patients (N=100 for the PHiSeg network and N=700 for the QC). We used the proposed method to obtain reference T1 ranges for the left ventricular (LV) myocardium in healthy subjects as well as common clinical cardiac conditions. Results: T1 values computed from automatic and manual segmentations were highly correlated (r=0.97). Bland-Altman analysis showed good agreement between the automated and manual measurements. The average Dice metric was 0.84 for the LV myocardium. The sensitivity of detection of erroneous outputs was 91%. Finally, T1 values were automatically derived from 11,882 CMR exams from the UK Biobank. For the healthy cohort, the mean (SD) corrected T1 values were 926.61 (45.26), 934.39 (43.25) and 927.56 (50.36) for global, interventricular septum and free-wall respectively. Conclusions: The proposed pipeline allows for automatic analysis of myocardial native T1 mapping and includes a QC process to detect potentially erroneous results. T1 reference values were presented for healthy subjects and common clinical cardiac conditions from the largest cohort to date using T1-mapping images. [ABSTRACT FROM AUTHOR]

Published
2020
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