Publications by authors named "Sara Taylor"

56 Publications

It is time to become serious about closing the global resource gap for FCTC implementation.

Tob Induc Dis 2020 8;18:103. Epub 2020 Dec 8.

Framework Convention Alliance for Tobacco Control, Ottawa, Canada.

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http://dx.doi.org/10.18332/tid/130809DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7731297PMC
December 2020

Forecasting stress, mood, and health from daytime physiology in office workers and students.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:5953-5957

We examine the problem of forecasting tomorrow morning's three self-reported levels (on scales from 0 to 100) of stressed-calm, sad-happy, and sick-healthy based on physiological data (skin conductance, skin temperature, and acceleration) from a sensor worn on the wrist from 10am-5pm today. We train automated forecasting regression algorithms using Random Forests and compare their performance over two sets of data: "workers" consisting of 490 days of weekday data from 39 employees at a high-tech company in Japan and "students" consisting of 3,841 days of weekday data from 201 New England USA college students. Mean absolute errors on held-out test data achieved 10.8, 13.5, and 14.4 for the estimated levels of mood, stress, and health respectively of office workers, and 17.8, 20.3, and 20.4 for the mood, stress, and health respectively of students. Overall the two groups reported comparable stress and mood scores, while employees reported slightly poorer health, and engaged in significantly lower levels of physical activity as measured by accelerometers. We further examine differences in population features and how systems trained on each population performed when tested on the other.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176706DOI Listing
July 2020

High-quality nuclear genome for Sarcoptes scabiei-A critical resource for a neglected parasite.

PLoS Negl Trop Dis 2020 10 1;14(10):e0008720. Epub 2020 Oct 1.

Cell and Molecular Biology Department, Infectious Diseases Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.

The parasitic mite Sarcoptes scabiei is an economically highly significant parasite of the skin of humans and animals worldwide. In humans, this mite causes a neglected tropical disease (NTD), called scabies. This disease results in major morbidity, disability, stigma and poverty globally and is often associated with secondary bacterial infections. Currently, anti-scabies treatments are not sufficiently effective, resistance to them is emerging and no vaccine is available. Here, we report the first high-quality genome and transcriptomic data for S. scabiei. The genome is 56.6 Mb in size, has a a repeat content of 10.6% and codes for 9,174 proteins. We explored key molecules involved in development, reproduction, host-parasite interactions, immunity and disease. The enhanced 'omic data sets for S. scabiei represent comprehensive and critical resources for genetic, functional genomic, metabolomic, phylogenetic, ecological and/or epidemiological investigations, and will underpin the design and development of new treatments, vaccines and/or diagnostic tests.
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http://dx.doi.org/10.1371/journal.pntd.0008720DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591027PMC
October 2020

Cytotrophoblast extracellular vesicles enhance decidual cell secretion of immune modulators via TNFα.

Development 2020 Sep 8;147(17). Epub 2020 Sep 8.

Center for Reproductive Sciences, University of California, San Francisco, CA 94143, USA

The placenta releases large quantities of extracellular vesicles (EVs) that likely facilitate communication between the embryo/fetus and the mother. We isolated EVs from second trimester human cytotrophoblasts (CTBs) by differential ultracentrifugation and characterized them using transmission electron microscopy, immunoblotting and mass spectrometry. The 100,000  pellet was enriched for vesicles with a cup-like morphology typical of exosomes. They expressed markers specific to this vesicle type, CD9 and HRS, and the trophoblast proteins placental alkaline phosphatase and HLA-G. Global profiling by mass spectrometry showed that placental EVs were enriched for proteins that function in transport and viral processes. A cytokine array revealed that the CTB 100,000  pellet contained a significant amount of tumor necrosis factor α (TNFα). CTB EVs increased decidual stromal cell (dESF) transcription and secretion of NF-κB targets, including IL8, as measured by qRT-PCR and cytokine array. A soluble form of the TNFα receptor inhibited the ability of CTB 100,000  EVs to increase dESF secretion of IL8. Overall, the data suggest that CTB EVs enhance decidual cell release of inflammatory cytokines, which we theorize is an important component of successful pregnancy.
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http://dx.doi.org/10.1242/dev.187013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7502596PMC
September 2020

Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health.

IEEE Trans Affect Comput 2020 Apr-Jun;11(2):200-213. Epub 2017 Dec 19.

Program of Media Arts and Sciences and the MIT Media Lab.

While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Three formulations of MTL are compared: i) MTL deep neural networks, which share several hidden layers but have final layers unique to each task; ii) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types; and iii) a Hierarchical Bayesian model in which tasks share a common Dirichlet Process prior. We offer the code for this work in open source. These techniques are investigated in the context of predicting future mood, stress, and health using data collected from surveys, wearable sensors, smartphone logs, and the weather. Empirical results demonstrate that using MTL to account for individual differences provides large performance improvements over traditional machine learning methods and provides personalized, actionable insights.
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http://dx.doi.org/10.1109/TAFFC.2017.2784832DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266106PMC
December 2017

The Role of Synaptic Cell Adhesion Molecules and Associated Scaffolding Proteins in Social Affiliative Behaviors.

Biol Psychiatry 2020 09 22;88(6):442-451. Epub 2020 Feb 22.

Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address:

Social affiliative behaviors-engagement in positive (i.e., nonaggressive) social approach and reciprocal social interactions with a conspecific-comprise a construct within the National Institute of Mental Health Research Domain Criteria Social Processes Domain. These behaviors are disrupted in multiple human neurodevelopmental and neuropsychiatric disorders, such as autism, schizophrenia, social phobia, and others. Human genetic studies have strongly implicated synaptic cell adhesion molecules (sCAMs) in several such disorders that involve marked reductions, or other dysregulations, of social affiliative behaviors. Here, we review the literature on the role of sCAMs in social affiliative behaviors. We integrate findings pertaining to synapse structure and morphology, neurotransmission, postsynaptic signaling pathways, and neural circuitry to propose a multilevel model that addresses the impact of a diverse group of sCAMs, including neurexins, neuroligins, protocadherins, immunoglobulin superfamily proteins, and leucine-rich repeat proteins, as well as their associated scaffolding proteins, including SHANKs and others, on social affiliative behaviors. This review finds that the disruption of sCAMs often manifests in changes in social affiliative behaviors, likely through alterations in synaptic maturity, pruning, and specificity, leading to excitation/inhibition imbalance in several key regions, namely the medial prefrontal cortex, basolateral amygdala, hippocampus, anterior cingulate cortex, and ventral tegmental area. Unraveling the complex network of interacting sCAMs in glutamatergic synapses will be an important strategy for elucidating the mechanisms of social affiliative behaviors and the alteration of these behaviors in many neuropsychiatric and neurodevelopmental disorders.
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http://dx.doi.org/10.1016/j.biopsych.2020.02.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442706PMC
September 2020

Common humanity in the classroom: Increasing self-compassion and coping self-efficacy through a mindfulness-based intervention.

J Am Coll Health 2020 Mar 9:1-8. Epub 2020 Mar 9.

Psychology Department, Hendrix College, Conway, AR, USA.

To examine the effectiveness of a classroom-based mindfulness-based intervention (MBI) in improving stress, coping, and psychological well-being in college students. Sixty-one students at a small liberal arts college. As part of a college course, students in the MBI condition ( = 33) completed mindfulness meditations, reflective journaling, and participated in group discussions over the course of eight weeks. A control group of students ( 28) received traditional instruction about stress and coping as part of a concurrently taught college course. Perceived stress, mental health, mindfulness, self-compassion, and coping self-efficacy were measured before and after the intervention and instruction. Significant improvements in self-compassion and coping self-efficacy emerged, particularly in the domains of common humanity, isolation, and emotion-focused coping self-efficacy. These findings suggest that incorporation of MBIs into the classroom can be an effective strategy to enhance the well-being of college students.
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http://dx.doi.org/10.1080/07448481.2020.1728278DOI Listing
March 2020

How to eliminate scabies parasites from fomites: A high-throughput ex vivo experimental study.

J Am Acad Dermatol 2020 Jul 17;83(1):241-245. Epub 2019 Dec 17.

Cellular and Molecular Biology Department, Infectious Diseases Program, Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, Brisbane, Australia. Electronic address:

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http://dx.doi.org/10.1016/j.jaad.2019.11.069DOI Listing
July 2020

Expression-Based Cell Lineage Analysis in Through a Course-Based Research Experience for Early Undergraduates.

Authors:
John M Olson Cory J Evans Kathy T Ngo Hee Jong Kim Joseph Duy Nguyen Kayla G H Gurley Truc Ta Vijay Patel Lisa Han Khoa T Truong-N Letty Liang Maggie K Chu Hiu Lam Hannah G Ahn Abhik Kumar Banerjee In Young Choi Ross G Kelley Naseem Moridzadeh Awais M Khan Omair Khan Szuyao Lee Elizabeth B Johnson Annie Tigranyan Jay Wang Anand D Gandhi Manish M Padhiar Joseph Hargan Calvopina Kirandeep Sumra Kristy Ou Jessie C Wu Joseph N Dickan Sabrena M Ahmadi Donald N Allen Van Thanh Mai Saif Ansari George Yeh Earl Yoon Kimberly Gon John Y Yu Johnny He Jesse M Zaretsky Noemi E Lee Edward Kuoy Alexander N Patananan Daniel Sitz PhuongThao Tran Minh-Tu Do Samira J Akhave Silverio D Alvarez Bobby Asem Neda Asem Nicole A Azarian Arezou Babaesfahani Ahmad Bahrami Manjeet Bhamra Ragini Bhargava Rakesh Bhatia Subir Bhatia Nicholas Bumacod Jonathan J Caine Thomas A Caldwell Nicole A Calica Elise M Calonico Carman Chan Helen H-L Chan Albert Chang Chiaen Chang Daniel Chang Jennifer S Chang Nauman Charania Jasmine Y Chen Kevin Chen Lu Chen Yuyu Chen Derek J Cheung Jesse J Cheung Jessica J Chew Nicole B Chew Cheng-An Tony Chien Alana M Chin Chee Jia Chin Youngho Cho Man Ting Chou Ke-Huan K Chow Carolyn Chu Derrick M Chu Virginia Chu Katherine Chuang Arunit Singh Chugh Mark R Cubberly Michael Guillermo Daniel Sangita Datta Raj Dhaliwal Jenny Dinh Dhaval Dixit Emmylou Dowling Melinda Feng Christopher M From Daisuke Furukawa Himaja Gaddipati Lilit Gevorgyan Zunera Ghaznavi Tulika Ghosh Jaskaran Gill David J Groves Kalkidan K Gurara Ali R Haghighi Alexandra L Havard Nasser Heyrani Tanya Hioe Kirim Hong Justin J Houman Molly Howland Elaine L Hsia Justin Hsueh Stacy Hu Andrew J Huang Jasmine C Huynh Jenny Huynh Chris Iwuchukwu Michael J Jang An An Jiang Simran Kahlon Pei-Yun Kao Manpreet Kaur Matthew G Keehn Elizabeth J Kim Hannah Kim Michelle J Kim Shawn J Kim Aleksandar Kitich Ross A Kornberg Nicholas G Kouzelos Jane Kuon Bryan Lau Roger K Lau Rona Law Huy D Le Rachael Le Carrou Lee Christina Lee Grace E Lee Kenny Lee Michelle J Lee Regina V Lee Sean H K Lee Sung Kyu Lee Sung-Ling D Lee Yong Jun Lee Megan J Leong David M Li Hao Li Xingfu Liang Eric Lin Michelle M Lin Peter Lin Tiffany Lin Stacey Lu Serena S Luong Jessica S Ma Li Ma Justin N Maghen Sravya Mallam Shivtaj Mann Jason H Melehani Ryan C Miller Nitish Mittal Carmel M Moazez Susie Moon Rameen Moridzadeh Kaley Ngo Hanh H Nguyen Kambria Nguyen Thien H Nguyen Angela W Nieh Isabella Niu Seo-Kyung Oh Jessica R Ong Randi K Oyama Joseph Park Yaelim A Park Kimberly A Passmore Ami Patel Amy A Patel Dhruv Patel Tirth Patel Katherine E Peterson An Huynh Pham Steven V Pham Melissa E Phuphanich Neil D Poria Alexandra Pourzia Victoria Ragland Riki D Ranat Cameron M Rice David Roh Solomon Rojhani Lili Sadri Agafe Saguros Zainab Saifee Manjot Sandhu Brooke Scruggs Lisa M Scully Vanessa Shih Brian A Shin Tamir Sholklapper Harnek Singh Sumedha Singh Sondra L Snyder Katelyn F Sobotka Sae Ho Song Siddharth Sukumar Halley C Sullivan Mark Sy Hande Tan Sara K Taylor Shivani K Thaker Tulsi Thakore Gregory E Tong Jacinda N Tran Jonathan Tran Tuan D Tran Vivi Tran Cindy L Trang Hung G Trinh Peter Trinh Han-Ching H Tseng Ted T Uotani Akram V Uraizee Kent K T Vu Kevin K T Vu Komal Wadhwani Paluk K Walia Rebecca S Wang Shuo Wang Stephanie J Wang Danica D Wiredja Andrew L Wong Daniel Wu Xi Xue Griselda Yanez Yung-Hsuan Yang Zhong Ye Victor W Yee Cynthia Yeh Yue Zhao Xin Zheng Anke Ziegenbalg Jon Alkali Ida Azizkhanian Akash Bhakta Luke Berry Ryen Castillo Sonja Darwish Holly Dickinson Ritika Dutta Rahul Kumar Ghosh Riley Guerin Jonathan Hofman Garrick Iwamoto Sarah Kang Andrew Kim Brian Kim Hanwool Kim Kristine Kim Suji Kim Julie Ko Michael Koenig Alejandro LaRiviere Clifton Lee Jiwon Lee Brandon Lung Max Mittelman Mark Murata Yujin Park Daniel Rothberg Ben Sprung-Keyser Kunal Thaker Vivian Yip Paul Picard Francie Diep Nikki Villarasa Volker Hartenstein Casey Shapiro Marc Levis-Fitzgerald Leslie Jaworski David Loppato Ira E Clark Utpal Banerjee

G3 (Bethesda) 2019 11 5;9(11):3791-3800. Epub 2019 Nov 5.

Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA 90095

A variety of genetic techniques have been devised to determine cell lineage relationships during tissue development. Some of these systems monitor cell lineages spatially and/or temporally without regard to gene expression by the cells, whereas others correlate gene expression with the lineage under study. The AL4 echnique for eal-time nd lonal xpression (G-TRACE) system allows for rapid, fluorescent protein-based visualization of both current and past GAL4 expression patterns and is therefore amenable to genome-wide expression-based lineage screens. Here we describe the results from such a screen, performed by undergraduate students of the University of California, Los Angeles (UCLA) Undergraduate Research Consortium for Functional Genomics (URCFG) and high school summer scholars as part of a discovery-based education program. The results of the screen, which reveal novel expression-based lineage patterns within the brain, the imaginal disc epithelia, and the hematopoietic lymph gland, have been compiled into the G-TRACE Expression Database (GED), an online resource for use by the research community. The impact of this discovery-based research experience on student learning gains was assessed independently and shown to be greater than that of similar programs conducted elsewhere. Furthermore, students participating in the URCFG showed considerably higher STEM retention rates than UCLA STEM students that did not participate in the URCFG, as well as STEM students nationwide.
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http://dx.doi.org/10.1534/g3.119.400541DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6829132PMC
November 2019

Systematic Review of Father Involvement and Child Outcomes in Pediatric Chronic Illness Populations.

J Clin Psychol Med Settings 2020 03;27(1):89-106

Division of Pediatric Psychology, Department of Pediatrics, Michigan Medicine, C.S. Mott Children's Hospital, 1540 E. Hospital Dr., SPC 5718, Ann Arbor, MI, 48109, USA.

The overall objective of this paper was to systematically review and synthesize the emerging literature investigating the role of father involvement in pediatric outcomes among chronic illness populations. This review sought to answer the following questions: (1) what measures are used to assess father involvement in pediatric chronic illness populations, and who is the respondent, and (2) how is father involvement associated with child psychosocial and health related outcomes in pediatric chronic illness populations? Databases were searched using a key word search strategy. Articles were screened according to exclusion criteria, resulting in 15 identified articles that included a pediatric illness population, and assessed both father involvement and a child outcome variable. Qualitative analysis revealed that several measures have been used to assess father involvement in pediatric chronic illness populations. As a whole, the majority of findings indicate that better outcomes are associated with more father involvement in illness and non-illness related activities, and higher father-child relationship quality. Contradictory findings may be due to the quality of the involvement being assessed, or the possibility that father's become more involved with illness tasks in response to their child's poorer health outcomes. Future research should include the development and use of psychometrically sound measures of father involvement and employ more diverse samples with rigorous methodology.
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http://dx.doi.org/10.1007/s10880-019-09623-5DOI Listing
March 2020

Stress measurement using speech: Recent advancements, validation issues, and ethical and privacy considerations.

Stress 2019 07 4;22(4):408-413. Epub 2019 Apr 4.

b Affective Computing Group, MIT Media Lab, Massachusetts Institute of Technology , Cambridge , MA , USA.

Life stress is a well-established risk factor for a variety of mental and physical health problems, including anxiety disorders, depression, chronic pain, heart disease, asthma, autoimmune diseases, and neurodegenerative disorders. The purpose of this article is to describe emerging approaches for assessing stress using speech, which we do by reviewing the methodological advantages of these digital health tools, and the validation, ethical, and privacy issues raised by these technologies. As we describe, it is now possible to assess stress via the speech signal using smartphones and smart speakers that employ software programs and artificial intelligence to analyze several features of speech and speech acoustics, including pitch, jitter, energy, rate, and length and number of pauses. Because these digital devices are ubiquitous, we can now assess individuals' stress levels in real time in almost any natural environment in which people speak. These technologies thus have great potential for advancing digital health initiatives that involve continuously monitoring changes in psychosocial functioning and disease risk over time. However, speech-based indices of stress have yet to be well-validated against stress biomarkers (e.g., cortisol, cytokines) that predict disease risk. In addition, acquiring speech samples raises the possibility that conversations intended to be private could one day be made public; moreover, obtaining real-time psychosocial risk information prompts ethical questions regarding how these data should be used for medical, commercial, and personal purposes. Although assessing stress using speech thus has enormous potential, there are critical validation, privacy, and ethical issues that must be addressed.
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http://dx.doi.org/10.1080/10253890.2019.1584180DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081839PMC
July 2019

Use of In-Game Rewards to Motivate Daily Self-Report Compliance: Randomized Controlled Trial.

J Med Internet Res 2019 01 3;21(1):e11683. Epub 2019 Jan 3.

Affective Computing Group, Department of Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States.

Background: Encouraging individuals to report daily information such as unpleasant disease symptoms, daily activities and behaviors, or aspects of their physical and emotional state is difficult but necessary for many studies and clinical trials that rely on patient-reported data as primary outcomes. Use of paper diaries is the traditional method of completing daily diaries, but digital surveys are becoming the new standard because of their increased compliance; however, they still fall short of desired compliance levels.

Objective: Mobile games using in-game rewards offer the opportunity to increase compliance above the rates of digital diaries and paper diaries. We conducted a 5-week randomized control trial to compare the completion rates of a daily diary across 3 conditions: a paper-based participant-reported outcome diary (Paper PRO), an electronic-based participant-reported outcome diary (ePRO), and a novel ePRO diary with in-game rewards (Game-Motivated ePRO).

Methods: We developed a novel mobile game that is a combination of the idle and pet collection genres to reward individuals who complete a daily diary with an in-game reward. Overall, 197 individuals aged 6 to 24 years (male: 100 and female: 97) were enrolled in a 5-week study after being randomized into 1 of the 3 methods of daily diary completion. Moreover, 157 participants (male: 84 and female: 69) completed at least one diary and were subsequently included in analysis of compliance rates.

Results: We observed a significant difference (F=6.341; P=.002) in compliance to filling out daily diaries, with the Game-Motivated ePRO group having the highest compliance (mean completion 86.4%, SD 19.6%), followed by the ePRO group (mean completion 77.7%, SD 24.1%), and finally, the Paper PRO group (mean completion 70.6%, SD 23.4%). The Game-Motivated ePRO (P=.002) significantly improved compliance rates above the Paper PRO. In addition, the Game-Motivated ePRO resulted in higher compliance rates than the rates of ePRO alone (P=.09). Equally important, even though we observed significant differences in completion of daily diaries between groups, we did not observe any statistically significant differences in association between the responses to a daily mood question and study group, the average diary completion time (P=.52), or the System Usability Scale score (P=.88).

Conclusions: The Game-Motivated ePRO system encouraged individuals to complete the daily diaries above the compliance rates of the Paper PRO and ePRO without altering the participants' responses.

Trial Registration: ClinicalTrials.gov NCT03738254; http://clinicaltrials.gov/ct2/show/NCT03738254 (Archived by WebCite at http://www.webcitation.org/74T1p8u52).
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http://dx.doi.org/10.2196/11683DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6682282PMC
January 2019

Sex differences in GBM revealed by analysis of patient imaging, transcriptome, and survival data.

Sci Transl Med 2019 01;11(473)

Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA.

Sex differences in the incidence and outcome of human disease are broadly recognized but, in most cases, not sufficiently understood to enable sex-specific approaches to treatment. Glioblastoma (GBM), the most common malignant brain tumor, provides a case in point. Despite well-established differences in incidence and emerging indications of differences in outcome, there are few insights that distinguish male and female GBM at the molecular level or allow specific targeting of these biological differences. Here, using a quantitative imaging-based measure of response, we found that standard therapy is more effective in female compared with male patients with GBM. We then applied a computational algorithm to linked GBM transcriptome and outcome data and identified sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling are the critical determinants of survival for male and female patients, respectively. The clinical relevance of cell cycle and integrin signaling pathway signatures was further established through correlations between gene expression and in vitro chemotherapy sensitivity in a panel of male and female patient-derived GBM cell lines. Together, these results suggest that greater precision in GBM molecular subtyping can be achieved through sex-specific analyses and that improved outcomes for all patients might be accomplished by tailoring treatment to sex differences in molecular mechanisms.
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http://dx.doi.org/10.1126/scitranslmed.aao5253DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6502224PMC
January 2019

Vomit Comet Physiology: Autonomic Changes in Novice Flyers.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:1172-1176

This exploratory study examined the effects of varying g-forces, including feelings of weightlessness, on an individual's physiology during parabolic flight. Specifically, we collected heart rate, accelerometer, and skin conductance measurements from 16 flyers aboard a parabolic flight using wearable, wireless sensors. The biosignals were then correlated to participant reports of nausea, anxiety, and excitement during periods of altered g-forces. Using linear mixed-effects models, we found that (1) heart rate was positively correlated to individuals' self-reported highest/lowest periods of both anxiety and excitement, and (2) bilateral skin conductance asymmetry was positively correlated to individuals' self-reported highest/lowest periods of nausea.
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http://dx.doi.org/10.1109/EMBC.2018.8512414DOI Listing
July 2018

Prognosis in patients diagnosed with loco-regional failure of breast cancer: 34 years longitudinal data from the Stockholm-Gotland cancer registry.

Breast Cancer Res Treat 2018 Dec 17;172(3):703-712. Epub 2018 Sep 17.

Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.

Rationale: Survival after loco-regional failure (LRF) of breast cancer was investigated at the population level.

Methods: Using the Stockholm cancer registry, 2698 patients diagnosed with LRF between 1980 and 2014 were identified and divided into three cohorts by year of LRF diagnosis. Post-relapse event-free survival (EFS) and overall survival (OS) were analyzed separately in local and loco-regional relapses and compared across the cohorts by Kaplan-Meier method. Relative survival was estimated and Poisson regression models, adjusted for clinically relevant prognostic factors, were fitted for excess mortality ratio calculation. Age-related survival trends were also explored.

Results: Among 1922 patients diagnosed with local relapse, 1032 (54%) EFS events and 931 (48%) deaths were registered. A significant improvement in EFS (p < 0.001) and OS (p < 0.001) was demonstrated in tumors that recurred locally in the years 1990-1999 and 2000-2014 compared with 1980-1989, regardless of age at relapse (≤ 60 years; > 60 years). In women with loco-regional relapse, 557 out of 776 (72%) experienced a post-relapse event and 522 (67%) died. Significantly longer EFS and OS were seen over time in the whole group (p < 0.001 and p = 0.003, respectively) and in younger (p < 0.001; p < 0.001) but not in older women (p = 0.55; p = 0.80). Relative survival was consistent with OS and a statistically significant decrease in mortality after loco-regional recurrence over time was seen only in women aged ≤ 60 years.

Conclusions: Survival after loco-regional failure of breast cancer has improved over time, especially in younger women.
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http://dx.doi.org/10.1007/s10549-018-4936-2DOI Listing
December 2018

Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks.

IEEE J Biomed Health Inform 2019 07 29;23(4):1607-1617. Epub 2018 Aug 29.

Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, which is a problem, since people often do not fill them out accurately. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep onset/offset using a type of recurrent neural network with long-short-term memory (LSTM) cells for synthesizing temporal information. We collected 5580 days of multimodal data from 186 participants and compared the new method for sleep/wake classification and sleep onset/offset detection to, first, nontemporal machine learning methods and, second, a state-of-the-art actigraphy software. The new LSTM method achieved a sleep/wake classification accuracy of 96.5%, and sleep onset/offset detection F scores of 0.86 and 0.84, respectively, with mean absolute errors of 5.0 and 5.5 min, res-pectively, when compared with sleep/wake state and sleep onset/offset assessed using actigraphy and sleep diaries. The LSTM results were statistically superior to those from nontemporal machine learning algorithms and the actigraphy software. We show good generalization of the new algorithm by comparing participant-dependent and participant-independent models, and we show how to make the model nearly realtime with slightly reduced performance.
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http://dx.doi.org/10.1109/JBHI.2018.2867619DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6837840PMC
July 2019

Focused ultrasound combined with microbubble-mediated intranasal delivery of gold nanoclusters to the brain.

J Control Release 2018 09 26;286:145-153. Epub 2018 Jul 26.

Department of Biomedical Engineering, Washington University in St. Louis, Saint Louis, MO 63130, USA; Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO 63108, USA. Electronic address:

Focused ultrasound combined with microbubble-mediated intranasal delivery (FUSIN) is a new brain drug delivery technique. FUSIN utilizes the nasal route for direct nose-to-brain drug administration, thereby bypassing the blood-brain barrier (BBB) and minimizing systemic exposure. It also uses FUS-induced microbubble cavitation to enhance transport of intranasally (IN) administered agents to the FUS-targeted brain location. Previous studies have provided proof-of-concept data showing the feasibility of FUSIN to deliver dextran and the brain-derived neurotrophic factor to the caudate putamen of mouse brains. The objective of this study was to evaluate the biodistribution of IN administered gold nanoclusters (AuNCs) and assess the feasibility and short-term safety of FUSIN for the delivery of AuNCs to the brainstem. Three experiments were performed. First, the whole-body biodistribution of IN administered Cu-alloyed AuNCs (Cu-AuNCs) was assessed using in vivo positron emission tomography/computed tomography (PET/CT) and verified with ex vivo gamma counting. Control mice were intravenously (IV) injected with the Cu-AuNCs. Second, Cu-AuNCs and Texas red-labeled AuNCs (TR-AuNCs) were used separately to evaluate FUSIN delivery outcome in the brain. Cu-AuNCs or TR-AuNCs were administered to mice through the nasal route, followed by FUS sonication at the brainstem in the presence of systemically injected microbubbles. The spatial distribution of Cu-AuNCs and TR-AuNCs were examined by autoradiography and fluorescence microscopy of ex vivo brain slices, respectively. Third, histological analysis was performed to evaluate any potential histological damage to the nose and brain after FUSIN treatment. The experimental results revealed that IN administration induced significantly lower Cu-AuNCs accumulation in the blood, lungs, liver, spleen, kidney, and heart compared with IV injection. FUSIN enhanced the delivery of Cu-AuNCs and TR-AuNCs at the FUS-targeted brain region compared with IN delivery alone. No histological-level tissue damage was detected in the nose, trigeminal nerve, and brain. These results suggest that FUSIN is a promising technique for noninvasive, spatially targeted, and safe delivery of nanoparticles to the brain with minimal systemic exposure.
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http://dx.doi.org/10.1016/j.jconrel.2018.07.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138562PMC
September 2018

Focused Ultrasound Enabled Trans-Blood Brain Barrier Delivery of Gold Nanoclusters: Effect of Surface Charges and Quantification Using Positron Emission Tomography.

Small 2018 07 2;14(30):e1703115. Epub 2018 Jul 2.

Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA.

Focused ultrasound (FUS) technology is reported to enhance the delivery of Cu-integrated ultrasmall gold nanoclusters ( Cu-AuNCs) across the blood-brain barrier (BBB) as measured by positron emission tomography (PET). To better define the optimal physical properties for brain delivery, Cu-AuNCs with different surface charges are synthesized and characterized. In vivo biodistribution studies are performed to compare the individual organ uptake of each type of Cu-AuNCs. Quantitative PET imaging post-FUS treatment shows site-targeted brain penetration, retention, and diffusion of the negative, neutral, and positive Cu-AuNCs. Autoradiography is performed to compare the intrabrain distribution of these nanoclusters. PET Imaging demonstrates the effective BBB opening and successful delivery of Cu-AuNCs into the brain. Of the three Cu-AuNCs investigated, the neutrally charged nanostructure performs the best and is the candidate platform for future theranostic applications in neuro-oncology.
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http://dx.doi.org/10.1002/smll.201703115DOI Listing
July 2018

Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study.

J Med Internet Res 2018 06 8;20(6):e210. Epub 2018 Jun 8.

Affective Computing Group, Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States.

Background: Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being.

Objective: We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions.

Methods: We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures.

Results: We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification.

Conclusions: New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.
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http://dx.doi.org/10.2196/jmir.9410DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6015266PMC
June 2018

QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform.

Sensors (Basel) 2018 Apr 5;18(4). Epub 2018 Apr 5.

Affective Computing Group, MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Smartphones and wearable sensors have enabled unprecedented data collection, with many products now providing feedback to users about recommended step counts or sleep durations. However, these recommendations do not provide personalized insights that have been shown to be best suited for a specific individual. A scientific way to find individualized recommendations and causal links is to conduct experiments using single-case experimental design; however, properly designed single-case experiments are not easy to conduct on oneself. We designed, developed, and evaluated a novel platform, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. We provide software for the platform that we used (available for free on GitHub), which provides the methodological elements to run many kinds of customized studies. In this work, we evaluate its use with four different kinds of personalized investigations, examining how variables such as sleep duration and regularity, activity, and leisure time affect personal happiness, stress, productivity, and sleep efficiency. We conducted a six-week pilot study ( = 13) to evaluate QuantifyMe. We describe the lessons learned developing the platform and recommendations for its improvement, as well as its potential for enabling personalized insights to be scientifically evaluated in many individuals, reducing the high administrative cost for advancing human health and wellbeing.
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http://dx.doi.org/10.3390/s18041097DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948910PMC
April 2018

A phase-I study of lapatinib in combination with foretinib, a c-MET, AXL and vascular endothelial growth factor receptor inhibitor, in human epidermal growth factor receptor 2 (HER-2)-positive metastatic breast cancer.

Breast Cancer Res 2017 05 2;19(1):54. Epub 2017 May 2.

Princess Margaret Cancer Centre and University Health Network, University of Toronto, Toronto, ON, Canada.

Background: The mechanisms of resistance to anti-human epidermal growth factor receptor 2 (HER 2) therapies are unclear but may include the tyrosine-protein kinase Met (c-Met), vascular endothelial growth factor (VEGF) and AXL pathways. Foretinib is an inhibitor of c-Met, VEGF receptor 2 (VEGFR-2), platelet-derived growth factor receptor beta (PDGFRB), AXL, Fms-like tyrosine kinase 3 (FLT3), angiopoiten receptor (TIE-2), RET and RON kinases. This phase Ib study sought to establish the associated toxicities, pharmacokinetics (PK) and recommended phase II doses (RP2D) of foretinib and lapatinib in a cohort of HER-2-positive patients with metastatic breast cancer (MBC).

Methods: Women with HER-2 positive MBC, Performance status (PS 0-2), and no limit on number of prior chemotherapies or lines of anti-HER-2 therapies were enrolled. A 3 + 3 dose escalation design was utilized. Four dose levels were intended with starting doses of foretinib 30 mg and lapatinib 750 mg orally once a day (OD) on a 4-weekly cycle. Assessment of c-MET status from the primary archival tissue was performed.

Results: We enrolled 19 patients, all evaluable for toxicity assessment and for response evaluation. Median age was 60 years (34-86 years), 95% were PS 0-1, 53% were estrogen receptor-positive and 95% had at least one prior anti-HER-2-based regimen. The fourth dose level was reached (foretinib 45 mg/lapatinib 1250 mg) with dose-limiting toxicities of grade-3 diarrhea and fatigue. There was only one grade-4 non-hematological toxicity across all dose levels. There were no PK interactions between the agents. A median of two cycles was delivered across the dose levels (range 1-20) with associated progression-free survival of 3.2 months (95% CI 1.61-4.34 months). By immunohistochemical assessment with a specified cutoff, none of the 17 samples tested were classified as positive for c-Met.

Conclusions: The RP2D of the combined foretinib and lapatinib is 45 mg and 1000 mg PO OD, respectively. Limited activity was seen with this combination in a predominantly unselected cohort of HER-2-positive patients with MBC.
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http://dx.doi.org/10.1186/s13058-017-0836-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414192PMC
May 2017

Metabolic factors, anthropometric measures, diet, and physical activity in long-term breast cancer survivors: change from diagnosis and comparison to non-breast cancer controls.

Breast Cancer Res Treat 2017 Jul 25;164(2):451-460. Epub 2017 Apr 25.

Division of Medical Oncology and Hematology; Division of Clinical Epidemiology, Department of Medicine, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, 1284-600 University Ave, Toronto, ON, M5G 1X5, Canada.

Purpose: We studied metabolic factors, diabetes, and anthropometric measurements at diagnosis and long-term follow-up (LTFU), mean 12.5 years post-diagnosis, in breast cancer (BC) survivors, and compared their status at LTFU to that of age-matched women without BC. Diet and physical activity were also assessed.

Method: 535 non-diabetic BC patients treated at three University of Toronto hospitals were followed prospectively; 285 surviving patients, without distant recurrence, participated in a LTFU study. A control group of 167 age-matched women without BC was recruited from a mammogram screening program at one of the hospitals. Change over time was analyzed using paired t tests, and comparisons between BC survivors and controls used age and education (AE)-adjusted regression models.

Results: Median weight gain in BC survivors was 2.00 kg (p < 0.0001); BMI, glucose, insulin, homeostasis model assessment (HOMA), and total cholesterol increased modestly but significantly. Waist circumference, glucose, and triglycerides were higher in LTFU BC survivors versus controls. BC survivors had significantly greater prevalence of diabetes/pre-diabetes versus controls (33 vs. 20.4%, AE-adjusted odds ratio (OR) 1.59, p = 0.050). This effect was restricted to those with lower levels of physical activity (<56 metabolic equivalent (MET)-hours/week: OR 2.70 versus 0.94 for those with higher physical activity, interaction p = 0.034). At LTFU, BC survivors were more physically active than at diagnosis (median increase 28 MET-hours/week interquartile range -14.8 to 82), and compared to controls (median 68.2 vs. 44 MET-hours/week, p < 0.0001).

Conclusion: The prevalence of the metabolic syndrome and diabetes/pre-diabetes was significantly higher in BC survivors than in controls group, notably in those with lower levels of physical activity. Enhanced diabetes/metabolic syndrome screening and promotion of physical activity may be warranted in BC survivors.
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http://dx.doi.org/10.1007/s10549-017-4263-zDOI Listing
July 2017

Multimodal Ambulatory Sleep Detection.

IEEE EMBS Int Conf Biomed Health Inform 2017 Feb 13;2017:465-468. Epub 2017 Apr 13.

Affective Computing Group, Media Lab, Massachusetts Institute of Technology.

Inadequate sleep affects health in multiple ways. Unobtrusive ambulatory methods to monitor long-term sleep patterns in large populations could be useful for health and policy decisions. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep episode on/offset. We collected 5580 days of multimodal data and applied recurrent neural networks for sleep/wake classification, followed by cross-correlation-based template matching for sleep episode on/offset detection. The method achieved a sleep/wake classification accuracy of 96.5%, and sleep episode on/offset detection F1 scores of 0.85 and 0.82, respectively, with mean errors of 5.3 and 5.5 min, respectively, when compared with sleep/wake state and sleep episode on/offset assessed using actigraphy and sleep diaries.
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http://dx.doi.org/10.1109/BHI.2017.7897306DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6010306PMC
February 2017

Reprogramming Medulloblastoma-Propagating Cells by a Combined Antagonism of Sonic Hedgehog and CXCR4.

Cancer Res 2017 03 28;77(6):1416-1426. Epub 2016 Dec 28.

Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri.

The CXCR4 chemokine and Sonic Hedgehog (SHH) morphogen pathways are well-validated therapeutic targets in cancer, including medulloblastoma. However, single-agent treatments with SHH or CXCR4 antagonists have not proven efficacious in clinical trials to date. Here, we discovered that dual inhibition of the SHH and CXCR4 pathways in a murine model of SHH-subtype medulloblastoma exerts potent antitumor effects. This therapeutic synergy resulted in the suppression of tumor-propagating cell function and correlated with increased histone H3 lysine 27 trimethylation within the promoters of stem cell genes, resulting in their decreased expression. These results demonstrate that CXCR4 contributes to the epigenetic regulation of a tumor-propagating cell phenotype. Moreover, they provide a mechanistic rationale to evaluate the combination of SHH and CXCR4 inhibitors in clinical trials for the treatment of medulloblastoma, as well as other cancers driven by SHH that coexpress high levels of CXCR4. .
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http://dx.doi.org/10.1158/0008-5472.CAN-16-0847DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5505622PMC
March 2017

Stage-Specific Demethylation in Primordial Germ Cells Safeguards against Precocious Differentiation.

Dev Cell 2016 10 9;39(1):75-86. Epub 2016 Sep 9.

Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address:

Remodeling DNA methylation in mammalian genomes can be global, as seen in preimplantation embryos and primordial germ cells (PGCs), or locus specific, which can regulate neighboring gene expression. In PGCs, global and locus-specific DNA demethylation occur in sequential stages, with an initial global decrease in methylated cytosines (stage I) followed by a Tet methylcytosine dioxygenase (Tet)-dependent decrease in methylated cytosines that act at imprinting control regions (ICRs) and meiotic genes (stage II). The purpose of the two-stage mechanism is unclear. Here we show that Dnmt1 preserves DNA methylation through stage I at ICRs and meiotic gene promoters and is required for the pericentromeric enrichment of 5hmC. We discovered that the functional consequence of abrogating two-stage DNA demethylation in PGCs was precocious germline differentiation leading to hypogonadism and infertility. Therefore, bypassing stage-specific DNA demethylation has significant consequences for progenitor germ cell differentiation and the ability to transmit DNA from parent to offspring.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5064860PMC
http://dx.doi.org/10.1016/j.devcel.2016.07.019DOI Listing
October 2016

"Kind and Grateful": A Context-Sensitive Smartphone App Utilizing Inspirational Content to Promote Gratitude.

Psychol Well Being 2016;6. Epub 2016 Jul 4.

MIT Media Lab, 75 Amherst St., 02139 Cambridge, MA USA.

Background: Previous research has shown that gratitude positively influences psychological wellbeing and physical health. Grateful people are reported to feel more optimistic and happy, to better mitigate aversive experiences, and to have stronger interpersonal bonds. Gratitude interventions have been shown to result in improved sleep, more frequent exercise and stronger cardiovascular and immune systems. These findings call for the development of technologies that would inspire gratitude. This paper presents a novel system designed toward this end.

Methods: We leverage pervasive technologies to naturally embed inspiration to express gratitude in everyday life. Novel to this work, mobile sensor data is utilized to infer optimal moments for stimulating contextually relevant thankfulness and appreciation. Sporadic mood measurements are inventively obtained through the smartphone lock screen, investigating their interplay with grateful expressions. Both momentary thankful emotion and dispositional gratitude are measured. To evaluate our system, we ran two rounds of randomized control trials (RCT), including a pilot study (N = 15, 2 weeks) and a main study (N = 27, 5 weeks). Studies' participants were provided with a newly developed smartphone app through which they were asked to express gratitude; the app displayed inspirational content to only the intervention group, while measuring contextual cues for all users.

Results: In both rounds of the RCT, the intervention was associated with improved thankful behavior. Significant increase was observed in multiple facets of practicing gratitude in the intervention groups. The average frequency of practicing thankfulness increased by more than 120 %, comparing the baseline weeks with the intervention weeks of the main study. In contrast, the control group of the same study exhibited a decrease of 90 % in the frequency of thankful expressions. In the course of the study's 5 weeks, increases in dispositional gratitude and in psychological wellbeing were also apparent. Analyzing the relation between mood and gratitude expressions, our data suggest that practicing gratitude increases the probability of going up in terms of emotional valence and down in terms of emotional arousal. The influences of inspirational content and contextual cues on promoting thankful behavior were also analyzed: We present data suggesting that the more successful times for eliciting expressions of gratitude tend to be shortly after a social experience, shortly after location change, and shortly after physical activity.

Conclusions: The results support our intervention as an impactful method to promote grateful affect and behavior. Moreover, they provide insights into design and evaluation of general behavioral intervention technologies.
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http://dx.doi.org/10.1186/s13612-016-0046-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4932143PMC
July 2016

Sensitization to the motor stimulant effects of 3,4-methylenedioxypyrovalerone (MDPV) and cross-sensitization to methamphetamine in rats.

J Drug Alcohol Res 2016 May;5

Arizona State University, Department of Psychology, Tempe, AZ, USA.

Background: In recent years, there has been a dramatic increase in abuse of the synthetic cathinone 3,4-methylenedioxypyrovalerone (MDPV), often in combination with other illicit stimulants.

Purpose: We sought to determine if repeated exposure to MDPV would produce sensitization to the motor stimulant effects of the drug, and whether cross-sensitization would develop with the stimulant effects of methamphetamine (METH).

Study Design: Male Sprague-Dawley rats were administered MDPV (1 or 5 mg/kg) or saline once daily for 5 days at 24 hour intervals, or were administered MDPV (1 mg/kg) or saline once daily for 5 days at 48 hour intervals. For cross-sensitization experiments, rats were administered METH (1 mg/kg) or MDPV (1 or 5 mg/kg) once daily for 5 days at 48 hour intervals, and following a 5 day incubation period, were given an acute challenge injection of either MDPV (0.5 mg/kg) or METH (0.5 mg/kg), respectively.

Results: Rats repeatedly administered MDPV (1 mg/kg) every 48 hours, but not every 24 hours, demonstrated increased motor activity when given either a subsequent challenge of MDPV (0.5 mg/kg i.p.) or METH (0.5 mg/kg), indicating the development of behavioral sensitization and cross-sensitization, respectively. Moreover, rats repeatedly administered METH (1 mg/kg) every 48 hours did not exhibit cross-sensitization to the motor stimulating effects of a subsequent challenge with MDPV (0.5 mg/kg).

Conclusion: These results suggest that specific patterns of MDPV administration may lead to lasting changes in behavioral responses to subsequent METH exposure.
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http://dx.doi.org/10.4303/jdar/235967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896315PMC
May 2016

Prediction of Happy-Sad mood from daily behaviors and previous sleep history.

Annu Int Conf IEEE Eng Med Biol Soc 2015 ;2015:6796-9

We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants for ~30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.
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http://dx.doi.org/10.1109/EMBC.2015.7319954DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4768795PMC
August 2016

Wavelet-based motion artifact removal for electrodermal activity.

Annu Int Conf IEEE Eng Med Biol Soc 2015 ;2015:6223-6

Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data.
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http://dx.doi.org/10.1109/EMBC.2015.7319814DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413204PMC
August 2016

Automatic identification of artifacts in electrodermal activity data.

Annu Int Conf IEEE Eng Med Biol Soc 2015 ;2015:1934-7

Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.
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http://dx.doi.org/10.1109/EMBC.2015.7318762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413200PMC
August 2016