Flux-Limited Diffusion for Multiple Scattering in Participating Media

For the rendering of multiple scattering effects in participating media, methods based on the diffusion approximation are an extremely efficient alternative to Monte Carlo path tracing. However, in sufficiently transparent regions, classical diffusion approximation suffers from non-physical radiative fluxes which leads to a poor match to correct light transport. In particular, this prevents the application of classical diffusion approximation to heterogeneous media, where opaque material is embedded within transparent regions. To address this limitation, we introduce flux-limited diffusion, a technique from the astrophysics domain. This method provides a better approximation to light transport than classical diffusion approximation, particularly when applied to heterogeneous media, and hence broadens the applicability of diffusion-based techniques. We provide an algorithm for flux-limited diffusion, which is validated using the transport theory for a point light source in an infinite homogeneous medium. We further demonstrate that our implementation of flux-limited diffusion produces more accurate renderings of multiple scattering in various heterogeneous datasets than classical diffusion approximation, by comparing both methods to ground truth renderings obtained via volumetric path tracing.Comment: Accepted in Computer Graphics Foru

Developing a Series of AI Challenges for the United States Department of the Air Force

Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative between the DAF and MIT to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-MIT AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances

Machine Learning-driven Patient-specific Early Seizure Detection for Neuromodulation Devices

Epilepsy is a chronic disorder of the brain that predisposes individuals to experiencing recurrent and unprovoked seizures affecting 50 million people worldwide. Recent advances in fundamental neuroscience and implantable electronics have enabled the development of neuromodulation devices for the treatment of epilepsy. Modern neuromodulation devices detect abnormal electrical activity in the brain associated with seizures and activate electrical stimulation to prevent seizures from occurring. Today, there is a growing trend towards integrating machine learning for seizure detection on such devices to improve their efficacy. This thesis assesses the suitability of current machine learning models for neuromodulation devices by evaluating their seizure detection performance and efficiency in resource-constrained environments. Particular emphasis is placed on comparing traditional machine learning to modern deep learning models. This thesis will show that, in the seizure detection context, deep learning models can be implemented in a compact and resource-efficient way despite their computational complexity.M.A.S

Administration Rates of the Tdap Vaccine in Obstetric Patients

BACKGROUND: Infants younger than 6 months of age are at high risk for contracting pertussis because of not being fully vaccinated. The Advisory Committee on Immunization Practices (ACIP) recommends vaccinating all pregnant women with tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis vaccine (Tdap) between 27 and 36 weeks to offer passive immunity to the infant to help protect them until they are able to receive the full pertussis series. OBJECTIVE: To assess and compare compliance with the 2013 ACIP recommendation of vaccinating pregnant women with Tdap at 27 to 36 weeks\u27 gestation in 2 obstetric clinics. METHODS: This cross-sectional, retrospective chart review evaluated Tdap vaccine compliance in a random sample of obstetric patients from October 2013 to September 2014. The primary outcome evaluated the proportion of patients who received Tdap between 27 and 36 weeks\u27 gestation. Secondary outcomes included the proportion of patients who received Tdap at any point in pregnancy and within 30 days postpartum. RESULTS: The charts of 573 patients were reviewed, and 237 met inclusion criteria. For the primary outcome, 142 patients (59.9%) received the Tdap vaccine. Overall, 156 patients (65.8%) received Tdap at some point during the pregnancy. Factors associated with receiving the Tdap vaccination were insurance status, prenatal care risk level and site of prenatal care, receipt of the influenza vaccine, and preterm labor in the current pregnancy. CONCLUSION: The Tdap vaccine rate was 65.8%, with 59.9% of patients receiving the vaccine within the recommended ACIP timeframe. Further education, improvements in documentation, and chart reminders are needed to enhance administration

Next generation cardiac safety testing through the Comprehensive in vitro Proarrhythmia Assay (CiPA) paradigm

For the last decade, cardiac safety screening to evaluate the propensity of drugs to produce QT interval prolongation and Torsades de Pointes (TdP) arrhythmia has been conducted according to the ICH-S7B and ICH-E14 guidelines. hERG channel assays and in vivo QT measurements have been central to the existing approach. While effective, the present paradigm carries a risk of unnecessary compound attrition and high cost, especially when “thorough QT” (TQT) studies are initiated. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative is a public-private collaboration with the aim of updating the existing cardiac safety testing paradigm in order to better evaluate arrhythmia risk and remove the need for TQT testing. It is hoped that CiPA will produce a standardized ion channel assay approach, incorporating defined tests against major cardiac ion channels, the results of which then inform evaluation of proarrhythmic actions in silico, using human ventricular action potential models. These results are then to be validated using human (stem-cell derived) cardiomyocytes. This article reviews the rationale, progress of and challenges for the CiPA initiative, if this new paradigm to replace existing practice and, in time, lead to updated and widely accepted cardiac safety testing guidelines

An international validation of the AO spine subaxial injury classification system

Purpose To validate the AO Spine Subaxial Injury Classification System with participants of various experience levels, subspecialties, and geographic regions. Methods A live webinar was organized in 2020 for validation of the AO Spine Subaxial Injury Classification System. The validation consisted of 41 unique subaxial cervical spine injuries with associated computed tomography scans and key images. Intraobserver reproducibility and interobserver reliability of the AO Spine Subaxial Injury Classification System were calculated for injury morphology, injury subtype, and facet injury. The reliability and reproducibility of the classification system were categorized as slight (? = 0-0.20), fair (? = 0.21-0.40), moderate (? = 0.41-0.60), substantial (? = 0.61-0.80), or excellent (? = > 0.80) as determined by the Landis and Koch classification. Results A total of 203 AO Spine members participated in the AO Spine Subaxial Injury Classification System validation. The percent of participants accurately classifying each injury was over 90% for fracture morphology and fracture subtype on both assessments. The interobserver reliability for fracture morphology was excellent (? = 0.87), while fracture subtype (? = 0.80) and facet injury were substantial (? = 0.74). The intraobserver reproducibility for fracture morphology and subtype were excellent (? = 0.85, 0.88, respectively), while reproducibility for facet injuries was substantial (? = 0.76). Conclusion The AO Spine Subaxial Injury Classification System demonstrated excellent interobserver reliability and intraobserver reproducibility for fracture morphology, substantial reliability and reproducibility for facet injuries, and excellent reproducibility with substantial reliability for injury subtype