Optomechanically Induced Transparency/Absorption in a 3D Microwave Cavity Architecture at Ambient Temperature

Leveraging advancements in cavity optomechanics, we explore Optomechanically Induced Transparency/Absorption (OMIT/OMIA) in the microwave domain at ambient temperature. Contrary to previous studies employing cryogenic temperatures, this work exploits a 3D microwave cavity architecture to observe these effects at ambient temperature, broadening the scope of possible applications. The work successfully enhances the optomechanical coupling strength, enabling observable and robust OMIT/OMIA effects, and demonstrating up to 25 dB in signal amplification and 20 dB in attenuation. Operating in the unresolved sideband regime enables tunability across a wider frequency range, enhancing the system’s applicability in signal processing and sensing. The findings herein highlight the potential of optomechanical systems, presenting a simplified, cost-effective, and more feasible approach for applications at ambient temperature

Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter

Introduction Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications. Objectives Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts. Methods We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall®, oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time. Results Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall®: 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time. Conclusion Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks

Detectors for the James Webb Space Telescope Near-Infrared Spectrograph I: Readout Mode, Noise Model, and Calibration Considerations

We describe how the James Webb Space Telescope (JWST) Near-Infrared Spectrograph's (NIRSpec's) detectors will be read out, and present a model of how noise scales with the number of multiple non-destructive reads sampling-up-the-ramp. We believe that this noise model, which is validated using real and simulated test data, is applicable to most astronomical near-infrared instruments. We describe some non-ideal behaviors that have been observed in engineering grade NIRSpec detectors, and demonstrate that they are unlikely to affect NIRSpec sensitivity, operations, or calibration. These include a HAWAII-2RG reset anomaly and random telegraph noise (RTN). Using real test data, we show that the reset anomaly is: (1) very nearly noiseless and (2) can be easily calibrated out. Likewise, we show that large-amplitude RTN affects only a small and fixed population of pixels. It can therefore be tracked using standard pixel operability maps.Comment: 55 pages, 10 figure

Transduction of fetal mice with a feline lentiviral vector induces liver tumors which exhibit an E2F activation signature

This article is available open access through the publisher’s website at the link below. Copyright @ 2014 The American Society of Gene & Cell Therapy.Lentiviral vectors are widely used in basic research and clinical applications for gene transfer and long-term expression; however, safety issues have not yet been completely resolved. In this study, we characterized hepatocarcinomas that developed in mice 1 year after in utero administration of a feline-derived lentiviral vector. Mapped viral integration sites differed among tumors and did not coincide with the regions of chromosomal aberrations. Furthermore, gene expression profiling revealed that no known cancer-associated genes were deregulated in the vicinity of viral integrations. Nevertheless, five of the six tumors exhibited highly significant upregulation of E2F target genes, of which a majority are associated with oncogenesis, DNA damage response, and chromosomal instability. We further show in vivo and in vitro that E2F activation occurs early on following transduction of both fetal mice and cultured human hepatocytes. On the basis of the similarities in E2F target gene expression patterns among tumors and the lack of evidence implicating insertional mutagenesis, we propose that transduction of fetal mice with a feline lentiviral vector induces E2F-mediated major cellular processes that drive hepatocytes toward uncontrolled proliferation culminating in tumorigenesis.ISF, DFG, the Kamea Scientific Foundation, the European Research Council, the Lillyan & Alfy Nathan, Barbara Fox Miller, and Wolfson Foundations

Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts

Objective: The abundance of text available in social media and health related forums along with the rich expression of public opinion have recently attracted the interest of the public health community to use these sources for pharmacovigilance. Based on the intuition that patients post about Adverse Drug Reactions (ADRs) expressing negative sentiments, we investigate the effect of sentiment analysis features in locating ADR mentions. Methods: We enrich the feature space of a state-of-the-art ADR identification method with sentiment analysis features. Using a corpus of posts from the DailyStrength forum and tweets annotated for ADR and indication mentions, we evaluate the extent to which sentiment analysis features help in locating ADR mentions and distinguishing them from indication mentions. Results: Evaluation results show that sentiment analysis features marginally improve ADR identification in tweets and health related forum posts. Adding sentiment analysis features achieved a statistically significant F-measure increase from 72.14% to 73.22% in the Twitter part of an existing corpus using its original train/test split. Using stratified 10 10-fold cross-validation, statistically significant F-measure increases were shown in the DailyStrength part of the corpus, from 79.57% to 80.14%, and in the Twitter part of the corpus, from 66.91% to 69.16%. Moreover, sentiment analysis features are shown to reduce the number of ADRs being recognized as indications. Conclusion: This study shows that adding sentiment analysis features can marginally improve the performance of even a state-of-the-art ADR identification method. This improvement can be of use to pharmacovigilance practice, due to the rapidly increasing popularity of social media and health forums

Ndfip1 regulates nuclear Pten import in vivo to promote neuronal survival following cerebral ischemia

PTEN (phosphatase and tensin homologue deleted on chromosome TEN) is the major negative regulator of phosphatidylinositol 3-kinase signaling and has cell-specific functions including tumor suppression. Nuclear localization of PTEN is vital for tumor suppression; however, outside of cancer, the molecular and physiological events driving PTEN nuclear entry are unknown. In this paper, we demonstrate that cytoplasmic Pten was translocated into the nuclei of neurons after cerebral ischemia in mice. Critically, this transport event was dependent on a surge in the Nedd4 family–interacting protein 1 (Ndfip1), as neurons in Ndfip1-deficient mice failed to import Pten. Ndfip1 binds to Pten, resulting in enhanced ubiquitination by Nedd4 E3 ubiquitin ligases. In vitro, Ndfip1 overexpression increased the rate of Pten nuclear import detected by photobleaching experiments, whereas Ndfip1⁻/⁻ fibroblasts showed negligible transport rates. In vivo, Ndfip1 mutant mice suffered larger infarct sizes associated with suppressed phosphorylated Akt activation. Our findings provide the first physiological example of when and why transient shuttling of nuclear Pten occurs and how this process is critical for neuron survival.Jason Howitt, Jenny Lackovic, Ley-Hian Low, Adam Naguib, Alison Macintyre, Choo-Peng Goh, Jennifer K. Callaway, Vicki Hammond, Tim Thomas, Matthew Dixon, Ulrich Putz, John Silke, Perry Bartlett, Baoli Yang, Sharad Kumar, Lloyd C. Trotman, and Seong-Seng Ta