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A collaborative force for precision medicine progress: the STRIPE pharmacogenomics conference series
The landscape of precision medicine The field of precision medicine offers a more personalized approach to healthcare, recognizing the unique combination of genetic, environmental, and…
Perspectives on transport pathways of microplastics across the Middle East and North Africa (MENA) region
This perspective will focus for the first time on the occurrence and potential transport pathways of MPs within the MENA region. The delivery mechanism of MPs and characteristics of ocean currents and air patterns are discussed in detail within the Arabian Gulf -Gulf of Oman complex, the Red Sea-Gulf of Aden complex, the southern Arabian margin, and non-MENA region to the south, as well as the Mediterranean Sea respectively. Significant variable dissemination and seasonal delivery across different locations in the MENA regions are revealed from this analysis. The review provides guidance for researchers and government authorities in conducting MPs research and proposing actionable measures to mitigate risks associated with chemical and biological contamination.
On opportunities and challenges of large multimodal foundation models in education
Recently, the option to use large language models as a middleware connecting various AI tools and other large language models led to the development of so-called large multimodal foundation models, which have the power to process spoken text, music, images and videos. In this overview, we explain a new set of opportunities and challenges that arise from the integration of large multimodal foundation models in education.
Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives
Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.
Clinical and economic outcomes of a pharmacogenomics-enriched comprehensive medication management program in a self-insured employee population
Clinical and economic outcomes from a pharmacogenomics-enriched comprehensive medication management program were evaluated over 26 months in a self-insured U.S. employee population (n = 452 participants; n = 1500 controls) using propensity matched pre-post design with adjusted negative binomial and linear regression models. After adjusting for baseline covariates, program participation was associated with 39% fewer inpatient (p = 0.05) and 39% fewer emergency department (p = 0.002) visits, and with 21% more outpatient visits (p < 0.001) in the follow-up period compared to the control group. Results show pharmacogenomics-enriched comprehensive medication management can favorably impact healthcare utilization in a self-insured employer population by reducing emergency department and inpatient visits and can offer the potential for cost savings. Self-insured employers may consider implementing pharmacogenomics-enriched comprehensive medication management to improve the healthcare of their employees.
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