353 research outputs found
Empirical study of parallel LRU simulation algorithms
This paper reports on the performance of five parallel algorithms for simulating a fully associative cache operating under the LRU (Least-Recently-Used) replacement policy. Three of the algorithms are SIMD, and are implemented on the MasPar MP-2 architecture. Two other algorithms are parallelizations of an efficient serial algorithm on the Intel Paragon. One SIMD algorithm is quite simple, but its cost is linear in the cache size. The two other SIMD algorithm are more complex, but have costs that are independent on the cache size. Both the second and third SIMD algorithms compute all stack distances; the second SIMD algorithm is completely general, whereas the third SIMD algorithm presumes and takes advantage of bounds on the range of reference tags. Both MIMD algorithm implemented on the Paragon are general and compute all stack distances; they differ in one step that may affect their respective scalability. We assess the strengths and weaknesses of these algorithms as a function of problem size and characteristics, and compare their performance on traces derived from execution of three SPEC benchmark programs
When all else fails, listen to the patient: A viewpoint on the use of ecological momentary assessment in clinical trials
Going remote: Implementing digital research methods at an academic medical center during COVID-19
COVID-19 has forced medical research institutions to conduct clinical research remotely. Here, we describe how a university\u27s mHealth Research Core helped facilitate the shift to remote research during the COVID-19 pandemic. In 2019 (pre-pandemic), we conducted stakeholder interviews and leadership group sessions to identify, create, and implement resources and core functions to support investigator-initiated mHealth research. Between April 2019 and February 2020, we identified four investigator needs: 1)
Toward Realistic Dosimetry In Vitro: Determining Effective Concentrations of Test Substances in Cell Culture and Their Prediction by an In Silico Mass Balance Model
Nominal concentrations (CNom) in cell culture media are routinely used to define concentration–effect relationships in the in vitro toxicology. The actual concentration in the medium (CMedium) can be affected by adsorption processes, evaporation, or degradation of chemicals. Therefore, we measured the total and free concentration of 12 chemicals, covering a wide range of lipophilicity (log KOW −0.07–6.84), in the culture medium (CMedium) and cells (CCell) after incubation with Balb/c 3T3 cells for up to 48 h. Measured values were compared to predictions using an as yet unpublished in silico mass balance model that combined relevant equations from similar models published by others. The total CMedium for all chemicals except tamoxifen (TAM) were similar to the CNom. This was attributed to the cellular uptake of TAM and accumulation into lysosomes. The free (i.e., unbound) CMedium for the low/no protein binding chemicals were similar to the CNom, whereas values of all moderately to highly protein-bound chemicals were less than 30% of the CNom. Of the 12 chemicals, the two most hydrophilic chemicals, acetaminophen (APAP) and caffeine (CAF), were the only ones for which the CCell was the same as the CNom. The CCell for all other chemicals tended to increase over time and were all 2- to 274-fold higher than CNom. Measurements of CCytosol, using a digitonin method to release cytosol, compared well with CCell (using a freeze–thaw method) for four chemicals (CAF, APAP, FLU, and KET), indicating that both methods could be used. The mass balance model predicted the total CMedium within 30% of the measured values for 11 chemicals. The free CMedium of all 12 chemicals were predicted within 3-fold of the measured values. There was a poorer prediction of CCell values, with a median overprediction of 3- to 4-fold. In conclusion, while the number of chemicals in the study is limited, it demonstrates the large differences between CNom and total and free CMedium and CCell, which were also relatively well predicted by the mass balance model
Getting to more effective weight management in antipsychotic-treated youth: a survey of barriers and preferences
Background: Mentally ill youth are at risk for developing obesity, especially when they require antipsychotic treatment; moreover, they may face unique challenges in adhering to behavioral weight loss interventions. The aims of this project were to characterize the challenges families of youth with psychiatric disorders face when engaging in weight loss treatment and to gather information on attitudes and preferences for weight management interventions in this population. Methods: We devised a telephone survey to evaluate caregiver-perceived barriers/challenges to and preferences for behavioral weight loss treatment in overweight or obese mentally ill youth ages 6–18 treated with an antipsychotic agent in an outpatient setting. Results: A total of 26 parents or primary caregivers completed the survey. The most commonly cited barriers to participation in physical activity (PA) and maintaining a healthy diet were child's dislike of PA and child's preference for energy-dense foods, respectively, which were impacted by psychiatric symptoms. Preferences for weight loss treatment included individualized, prescribed meal plans and shopping lists, and exercise support/demonstration, with a preference for Internet or cell phone applications to help with monitoring food intake and exercise. Conclusions: These results suggest that targets for obesity treatment in this population include individualized, specific support that takes into account the child's motivation, which is effected by psychiatric symptoms. Tools for providing support may include the use of telehealth visits and mobile device applications for self-monitoring
Mindfulness training for depressed older adults using smartphone technology: Protocol for a fully remote precision clinical trial
BACKGROUND: Precision medicine, optimized interventions, and access to care are catchphrases for the future of behavioral treatments. Progress has been slow due to the dearth of clinical trials that optimize interventions\u27 benefits, individually tailor interventions to meet individual needs and preferences, and lead to rapid implementation after effectiveness is demonstrated. Two innovations have emerged to meet these challenges: fully remote trials and precision clinical trials.
OBJECTIVE: This paper provides a detailed description of Mindful MyWay, a study designed to test online mindfulness training in older adults with depression. Consistent with the concept of fully remote trials using a smartphone app, the study requires no in-person contact and can be conducted with participants anywhere in the United States. Based upon the precision medicine framework, the study assesses participants using high-frequency assessments of symptoms, cognitive performance, and patient preferences to both understand the individualized nature of treatment response and help individually tailor the intervention.
METHODS: Mindful MyWay is an open-label early-phase clinical trial for individuals 65 years and older with current depression. A smartphone app was developed to help coordinate the study, deliver the intervention, and evaluate the acceptability of the intervention, as well as predictors and outcomes of it. The curriculum for the fully remote intervention parallels the mindfulness-based stress reduction curriculum, a protocolized group-based mindfulness training that is typically provided in person. After consent and screening, participants download The Healthy Mind Lab mobile health smartphone app from the Apple App Store, allowing them to complete brief smartphone-based assessments of depressive symptoms and cognitive performance 4 times each day for 4 weeks prior to and after completing the intervention. The intervention consists of an introduction video and 10 weekly mindfulness training sessions, with the expectation to practice mindfulness at home daily. The app collects participant preference data throughout the 10-week intervention period; these high-frequency assessments identify participants\u27 individually dynamic preferences toward the goal of optimizing the intervention in future iterations.
RESULTS: Participant recruitment and data collection began in March 2019. Final end point assessments will be collected in May 2022. The paper describes lessons learned regarding the critical role of early-phase testing prior to moving to a randomized trial.
CONCLUSIONS: The Mindful MyWay study is an exemplar of innovative clinical trial designs that use smartphone technology in behavioral and neuropsychiatric conditions. These include fully remote studies that can recruit throughout the United States, including hard-to-access areas, and collect high-frequency data, which is ideal for idiographic assessment and individualized intervention optimization. Our findings will be used to modify our methods and inform future randomized controlled trials within a precision medicine framework.
TRIAL REGISTRATION: ClinicalTrials.gov NCT03922217; https://clinicaltrials.gov/ct2/show/NCT03922217.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39233
Antidepressant augmentation versus switch in treatment-resistant geriatric depression
BACKGROUND: The benefits and risks of augmenting or switching antidepressants in older adults with treatment-resistant depression have not been extensively studied.
METHODS: We conducted a two-step, open-label trial involving adults 60 years of age or older with treatment-resistant depression. In step 1, patients were randomly assigned in a 1:1:1 ratio to augmentation of existing antidepressant medication with aripiprazole, augmentation with bupropion, or a switch from existing antidepressant medication to bupropion. Patients who did not benefit from or were ineligible for step 1 were randomly assigned in step 2 in a 1:1 ratio to augmentation with lithium or a switch to nortriptyline. Each step lasted approximately 10 weeks. The primary outcome was the change from baseline in psychological well-being, assessed with the National Institutes of Health Toolbox Positive Affect and General Life Satisfaction subscales (population mean, 50; higher scores indicate greater well-being). A secondary outcome was remission of depression.
RESULTS: In step 1, a total of 619 patients were enrolled; 211 were assigned to aripiprazole augmentation, 206 to bupropion augmentation, and 202 to a switch to bupropion. Well-being scores improved by 4.83 points, 4.33 points, and 2.04 points, respectively. The difference between the aripiprazole-augmentation group and the switch-to-bupropion group was 2.79 points (95% CI, 0.56 to 5.02; P = 0.014, with a prespecified threshold P value of 0.017); the between-group differences were not significant for aripiprazole augmentation versus bupropion augmentation or for bupropion augmentation versus a switch to bupropion. Remission occurred in 28.9% of patients in the aripiprazole-augmentation group, 28.2% in the bupropion-augmentation group, and 19.3% in the switch-to-bupropion group. The rate of falls was highest with bupropion augmentation. In step 2, a total of 248 patients were enrolled; 127 were assigned to lithium augmentation and 121 to a switch to nortriptyline. Well-being scores improved by 3.17 points and 2.18 points, respectively (difference, 0.99; 95% CI, -1.92 to 3.91). Remission occurred in 18.9% of patients in the lithium-augmentation group and 21.5% in the switch-to-nortriptyline group; rates of falling were similar in the two groups.
CONCLUSIONS: In older adults with treatment-resistant depression, augmentation of existing antidepressants with aripiprazole improved well-being significantly more over 10 weeks than a switch to bupropion and was associated with a numerically higher incidence of remission. Among patients in whom augmentation or a switch to bupropion failed, changes in well-being and the occurrence of remission with lithium augmentation or a switch to nortriptyline were similar. (Funded by the Patient-Centered Outcomes Research Institute; OPTIMUM ClinicalTrials.gov number, NCT02960763.)
Genoviz Software Development Kit: Java tool kit for building genomics visualization applications
<p>Abstract</p> <p>Background</p> <p>Visualization software can expose previously undiscovered patterns in genomic data and advance biological science.</p> <p>Results</p> <p>The Genoviz Software Development Kit (SDK) is an open source, Java-based framework designed for rapid assembly of visualization software applications for genomics. The Genoviz SDK framework provides a mechanism for incorporating adaptive, dynamic zooming into applications, a desirable feature of genome viewers. Visualization capabilities of the Genoviz SDK include automated layout of features along genetic or genomic axes; support for user interactions with graphical elements (Glyphs) in a map; a variety of Glyph sub-classes that promote experimentation with new ways of representing data in graphical formats; and support for adaptive, semantic zooming, whereby objects change their appearance depending on zoom level and zooming rate adapts to the current scale. Freely available demonstration and production quality applications, including the Integrated Genome Browser, illustrate Genoviz SDK capabilities.</p> <p>Conclusion</p> <p>Separation between graphics components and genomic data models makes it easy for developers to add visualization capability to pre-existing applications or build new applications using third-party data models. Source code, documentation, sample applications, and tutorials are available at <url>http://genoviz.sourceforge.net/</url>.</p
Adiposity, hepatic triglyceride, and carotid intima media thickness during behavioral weight loss treatment in antipsychotic-treated youth: A randomized pilot study
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