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Article ; Online: Big data in Earth science: Emerging practice and promise.

Vance, Tiffany C / Huang, Thomas / Butler, Kevin A

2024 Volume 383, Issue 6688, Page(s) eadh9607

Abstract: Improvements in the number and resolution of Earth- and satellite-based sensors coupled with finer-resolution models have resulted in an explosion in the volume of Earth science data. This data-rich environment is changing the practice of Earth science, ... ...

Abstract	Improvements in the number and resolution of Earth- and satellite-based sensors coupled with finer-resolution models have resulted in an explosion in the volume of Earth science data. This data-rich environment is changing the practice of Earth science, extending it beyond discovery and applied science to new realms. This Review highlights recent big data applications in three subdisciplines-hydrology, oceanography, and atmospheric science. We illustrate how big data relate to contemporary challenges in science: replicability and reproducibility and the transition from raw data to information products. Big data provide unprecedented opportunities to enhance our understanding of Earth's complex patterns and interactions. The emergence of digital twins enables us to learn from the past, understand the current state, and improve the accuracy of future predictions.
Language	English
Publishing date	2024-03-15
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	128410-1
ISSN	1095-9203 ; 0036-8075
ISSN (online)	1095-9203
ISSN	0036-8075
DOI	10.1126/science.adh9607
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Article ; Online: Privacy-preserving datasets of eye-tracking samples with applications in XR.

David-John, Brendan / Butler, Kevin / Jain, Eakta

IEEE transactions on visualization and computer graphics

2023 Volume PP

Abstract: Virtual and mixed-reality (XR) technology has advanced significantly in the last few years and will enable the future of work, education, socialization, and entertainment. Eye-tracking data is required for supporting novel modes of interaction, animating ...

Abstract	Virtual and mixed-reality (XR) technology has advanced significantly in the last few years and will enable the future of work, education, socialization, and entertainment. Eye-tracking data is required for supporting novel modes of interaction, animating virtual avatars, and implementing rendering or streaming optimizations. While eye tracking enables many beneficial applications in XR, it also introduces a risk to privacy by enabling re-identification of users. We applied privacy definitions of it-anonymity and plausible deniability (PD) to datasets of eye-tracking samples and evaluated them against the state-of-the-art differential privacy (DP) approach. Two VR datasets were processed to reduce identification rates while minimizing the impact on the performance of trained machine-learning models. Our results suggest that both PD and DP mechanisms produced practical privacy-utility trade-offs with respect to re-identification and activity classification accuracy, while k-anonymity performed best at retaining utility for gaze prediction.
Language	English
Publishing date	2023-02-22
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2023.3247048
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Article ; Online: Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience.

Wilson, Ethan / Ibragimov, Azim / Proulx, Michael J / Tetali, Sai Deep / Butler, Kevin / Jain, Eakta

IEEE transactions on visualization and computer graphics

2024 Volume 30, Issue 5, Page(s) 2257–2268

Abstract: Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks [14]. The state of our knowledge about currently existing privacy ... ...

Abstract	Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks [14]. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as prediction error, and black-box threat models. We propose that for interactive VR applications, it is essential to consider user-centric notions of utility and a variety of threat models. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications that incorporate subjective user experience and task performance metrics. We evaluate selected privacy mechanisms using this methodology and find that re-identification accuracy can be decreased to as low as 14% while maintaining a high usability score and reasonable task performance. Finally, we elucidate three threat scenarios (black-box, black-box with exemplars, and white-box) and assess how well the different privacy mechanisms hold up to these adversarial scenarios. This work advances the state of the art in VR privacy by providing a methodology for end-to-end assessment of the risk of re-identification attacks and potential mitigating solutions. f.
Language	English
Publishing date	2024-04-19
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2024.3372032
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Article ; Online: Cannabis Use Characteristics Associated with Self-Reported Cognitive Function in a Nationally Representative U.S. sample.

Rubin-Kahana, Dafna Sara / Butler, Kevin / Hassan, Ahmed Nabeel / Sanches, Marcos / Le Foll, Bernard

Substance use & misuse

2024 , Page(s) 1–10

Abstract: Background: With increases in cannabis use and potency, there is a need to improve our understanding of the impact of use on cognitive function. Previous research indicates long-term cannabis use may have a negative effect on executive function. Few ... ...

Abstract	Background: With increases in cannabis use and potency, there is a need to improve our understanding of the impact of use on cognitive function. Previous research indicates long-term cannabis use may have a negative effect on executive function. Few studies have examined persistence of it in protracted abstinence, and there is limited evidence of predictors of worse cognitive function in current and former users. In this study, we aim to evaluate the associations between cannabis use status (current, former, and never use) and self-report cognition. Further, we investigate if cannabis use characteristics predict self-report cognitive function. Methods: Cross-sectional cannabis use data from the National Epidemiological Survey on Alcohol and Related Conditions-III (NESARC-III), a national survey ( Results: Current ( Conclusion: While prospective studies are required to confirm, findings suggest cannabis use is linked to worse cognition. There may be some limited recovery of cognition in former users and some cannabis use characteristics predict impairment. These findings add to our understanding of the cognitive impact of cannabis use. As worse cognitive function may impact relapse, findings have implications for personalization of cannabis use disorder treatment.
Language	English
Publishing date	2024-04-25
Publishing country	England
Document type	Journal Article
ZDB-ID	1310358-1
ISSN	1532-2491 ; 1082-6084
ISSN (online)	1532-2491
ISSN	1082-6084
DOI	10.1080/10826084.2024.2340975
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Article ; Online: DR-VIDAL - Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data.

Ghosh, Shantanu / Feng, Zheng / Bian, Jiang / Butler, Kevin / Prosperi, Mattia

AMIA ... Annual Symposium proceedings. AMIA Symposium

2023 Volume 2022, Page(s) 485–494

Abstract: Determining causal effects of interventions onto outcomes from real-world, observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is challenging due to underlying bias. Causal deep learning has improved over ... ...

Abstract	Determining causal effects of interventions onto outcomes from real-world, observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is challenging due to underlying bias. Causal deep learning has improved over traditional techniques for estimating individualized treatment effects (ITE). We present the Doubly Robust Variational Information-theoretic Deep Adversarial Learning (DR-VIDAL), a novel generative framework that combines two joint models of treatment and outcome, ensuring an unbiased ITE estimation even when one of the two is misspecified. DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii) an information-theoretic generative adversarial network (Info-GAN) to generate counterfactuals; (iii) a doubly robust block incorporating treatment propensities for outcome predictions. On synthetic and real-world datasets (Infant Health and Development Program, Twin Birth Registry, and National Supported Work Program), DR-VIDAL achieves better performance than other non-generative and generative methods. In conclusion, DR-VIDAL uniquely fuses causal assumptions, VAE, Info-GAN, and doubly robustness into a comprehensive, per- formant framework. Code is available at: https://github.com/Shantanu48114860/DR-VIDAL-AMIA-22 under MIT license.
MeSH term(s)	Humans ; Prognosis ; Electronic Health Records ; Causality
Language	English
Publishing date	2023-04-29
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: The Role of Dopamine D3 Receptors in Tobacco Use Disorder: A Synthesis of the Preclinical and Clinical Literature.

Butler, Kevin / Le Foll, Bernard / Di Ciano, Patricia

Current topics in behavioral neurosciences

2022

Abstract: Tobacco smoking is a significant cause of preventable morbidity and mortality globally. Current pharmacological approaches to treat tobacco use disorder (TUD) are only partly effective and novel approaches are needed. Dopamine has a well-established role ...

Abstract	Tobacco smoking is a significant cause of preventable morbidity and mortality globally. Current pharmacological approaches to treat tobacco use disorder (TUD) are only partly effective and novel approaches are needed. Dopamine has a well-established role in substance use disorders, including TUD, and there has been a long-standing interest in developing agents that target the dopaminergic system to treat substance use disorders. Dopamine has 5 receptor subtypes (DRD1 to DRD5). Given the localization and safety profile of the dopamine receptor D3 (DRD3), it is of therapeutic potential for TUD. In this chapter, the preclinical and clinical literature investigating the role of DRD3 in processes relevant to TUD will be reviewed, including in nicotine reinforcement, drug reinstatement, conditioned stimuli and cue-reactivity, executive function, and withdrawal. Similarities and differences in findings from the animal and human work will be synthesized and findings will be discussed in relation to the therapeutic potential of targeting DRD3 in TUD.
Language	English
Publishing date	2022-09-30
Publishing country	Germany
Document type	Journal Article
ISSN	1866-3370
ISSN	1866-3370
DOI	10.1007/7854_2022_392
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Novel therapeutic and drug development strategies for tobacco use disorder: endocannabinoid modulation.

Butler, Kevin / Le Foll, Bernard

Expert opinion on drug discovery

2020 Volume 15, Issue 9, Page(s) 1065–1080

Abstract: Introduction: Tobacco use disorder (TUD) is a chronic relapsing condition. Existing pharmacotherapy can assist smokers to initiate smoking cessation, but relapse rates remain high. Novel therapeutics are required to help people quit and also to prevent ... ...

Abstract	Introduction: Tobacco use disorder (TUD) is a chronic relapsing condition. Existing pharmacotherapy can assist smokers to initiate smoking cessation, but relapse rates remain high. Novel therapeutics are required to help people quit and also to prevent relapse. The endocannabinoid system has been increasingly implicated in reward and addiction processes and the cannabinoid CB1 receptor inverse agonist rimonabant has been shown to be effective at promoting smoking cessation but has been associated with adverse psychiatric side effects. Areas covered: Multiple converging factors likely contribute to the maintenance of smoking and cause relapse including nicotine reinforcement, propensity to reinstate drug seeking (induced by nicotine priming, nicotine-associated cues, and stress), the severity of withdrawal signs and executive function status. Studies assessing the impact of endocannabinoid (CB1 receptor, CB2 receptor, anandamide, and 2-arachidonoylglycerol) modulation on these addiction-related factors are reviewed. Future research avenues are also discussed. Expert opinion: Endocannabinoid research in TUD is at a relatively early stage. Based on current evidence, CB1 receptor neutral antagonists and fatty acid amide hydrolase inhibitors demonstrate positive effects in studies assessing several addiction-related factors. This suggests they offer the greatest promise as novel cessation and anti-relapse agents.
MeSH term(s)	Animals ; Cannabinoid Receptor Agonists/administration & dosage ; Cannabinoid Receptor Agonists/pharmacology ; Cannabinoid Receptor Antagonists/administration & dosage ; Cannabinoid Receptor Antagonists/pharmacology ; Drug Development ; Drug-Seeking Behavior/drug effects ; Endocannabinoids/metabolism ; Humans ; Smoking Cessation/methods ; Tobacco Use Disorder/drug therapy ; Tobacco Use Disorder/physiopathology
Chemical Substances	Cannabinoid Receptor Agonists ; Cannabinoid Receptor Antagonists ; Endocannabinoids
Language	English
Publishing date	2020-05-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2259618-5
ISSN	1746-045X ; 1746-0441
ISSN (online)	1746-045X
ISSN	1746-0441
DOI	10.1080/17460441.2020.1767581
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Article: Impact of Substance Use Disorder Pharmacotherapy on Executive Function: A Narrative Review.

Butler, Kevin / Le Foll, Bernard

Frontiers in psychiatry

2019 Volume 10, Page(s) 98

Abstract: Substance use disorders are chronic, relapsing, and harmful conditions characterized by executive dysfunction. While there are currently no approved pharmacotherapy options for stimulant and cannabis use disorders, there are several evidence-based ... ...

Abstract	Substance use disorders are chronic, relapsing, and harmful conditions characterized by executive dysfunction. While there are currently no approved pharmacotherapy options for stimulant and cannabis use disorders, there are several evidence-based options available to help reduce symptoms during detoxification and aid long-term cessation for those with tobacco, alcohol and opioid use disorders. While these medication options have shown clinical efficacy, less is known regarding their potential to enhance executive function. This narrative review aims to provide a brief overview of research that has investigated whether commonly used pharmacotherapies for these substance use disorders (nicotine, bupropion, varenicline, disulfiram, acamprosate, nalmefene, naltrexone, methadone, buprenorphine, and lofexidine) effect three core executive function components (working memory, inhibitory control and cognitive flexibility). While pharmacotherapy-induced enhancement of executive function may improve cessation outcomes in dependent populations, there are limited and inconsistent findings regarding the effects of these medications on executive function. We discuss possible reasons for the mixed findings and suggest some future avenues of work that may enhance the understanding of addiction pharmacotherapy and cognitive training interventions and lead to improved patient outcomes.
Language	English
Publishing date	2019-03-01
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2564218-2
ISSN	1664-0640
ISSN	1664-0640
DOI	10.3389/fpsyt.2019.00098
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Article: Implementation and Preliminary Evaluation of a 12-Week Cognitive Behavioural and Motivational Enhancement Group Therapy for Cannabis Use Disorder.

Trick, Leanne / Butler, Kevin / Bourgault, Zoe / Vandervoort, Julianne / Le Foll, Bernard

Substance abuse : research and treatment

2023 Volume 17, Page(s) 11782218231205840

Abstract: Background: The purpose of this paper is to provide a preliminary evaluation of treatment outcomes, retention and client satisfaction following a 12-week combined cognitive behavioural therapy (CBT) and motivational enhancement therapy (MET) group ... ...

Abstract	Background: The purpose of this paper is to provide a preliminary evaluation of treatment outcomes, retention and client satisfaction following a 12-week combined cognitive behavioural therapy (CBT) and motivational enhancement therapy (MET) group treatment for cannabis use disorder (CUD) delivered in an outpatient setting. Implementation of the program is also described. Methods: A retrospective observational cohort study was conducted using data collected from medical records and self-report assessments. Participants were treatment-seeking cannabis users at the Centre for Addiction and Mental Health, Toronto. Cannabis use, cannabis-related problems, craving, withdrawal symptoms, self-efficacy for remaining abstinent, depression and anxiety were assessed pre- and post-treatment. Treatment retention was calculated by inspecting clinic attendance records, and client satisfaction was evaluated using an anonymous feedback survey. Potential predictors of treatment outcomes and retention were investigated in exploratory analyses. Results: Cannabis use was lower and days of abstinence higher post-treatment (vs pre-treatment). Post-treatment improvements in cannabis-related problems, craving, withdrawal symptoms, self-efficacy and mood were also observed. Completion of group treatment (⩾75% of sessions attended) was 57% and moderate levels of treatment satisfaction were reported. Conclusions: This study provides preliminary evidence that a 12-week combined CBT and MET treatment for cannabis use disorder delivered in a novel group setting improves cannabis use outcomes. Potential predictors of reduced cannabis use and retention were identified. Future controlled studies are warranted, and strategies for increasing retention should be explored.
Language	English
Publishing date	2023-10-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	1458030-5
ISSN	1547-0164 ; 1178-2218 ; 0889-7077
ISSN (online)	1547-0164
ISSN	1178-2218 ; 0889-7077
DOI	10.1177/11782218231205840
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Book ; Online: DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data

Ghosh, Shantanu / Feng, Zheng / Bian, Jiang / Butler, Kevin / Prosperi, Mattia

2023

Abstract	Determining causal effects of interventions onto outcomes from real-world, observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is challenging due to underlying bias. Causal deep learning has improved over traditional techniques for estimating individualized treatment effects (ITE). We present the Doubly Robust Variational Information-theoretic Deep Adversarial Learning (DR-VIDAL), a novel generative framework that combines two joint models of treatment and outcome, ensuring an unbiased ITE estimation even when one of the two is misspecified. DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii) an information-theoretic generative adversarial network (Info-GAN) to generate counterfactuals; (iii) a doubly robust block incorporating treatment propensities for outcome predictions. On synthetic and real-world datasets (Infant Health and Development Program, Twin Birth Registry, and National Supported Work Program), DR-VIDAL achieves better performance than other non-generative and generative methods. In conclusion, DR-VIDAL uniquely fuses causal assumptions, VAE, Info-GAN, and doubly robustness into a comprehensive, performant framework. Code is available at: https://github.com/Shantanu48114860/DR-VIDAL-AMIA-22 under MIT license. Comment: AMIA Annual Symposium, 2022 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10148269/)
Keywords	Computer Science - Machine Learning ; Statistics - Methodology
Subject code	006
Publishing date	2023-03-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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