The Wave Structure Function And Temporal Frequency Spread In Weak To Strong Optical Turbulence

This paper presents analytic expressions for the wave structure function, frequency spread of the temporal frequency spectrum, and the temporal frequency spectrum of optical signals propagating through a random medium, specifically the Earth’s atmosphere. The results are believed to be valid for all optical turbulence conditions. These expressions are developed using the Rytov approximation method. Generally, the validity of statistical quantities obtained via this method is restricted to conditions of weak optical turbulence. However, in this work, by using a modification of the effective atmospheric spectral model presented by Andrews et al. for scintillation index, wave structure function expressions have been derived that are valid in all turbulence conditions as evidenced by comparison to experimental data. Analytic wave structure function results are developed for plane, spherical, and Gaussian-beam waves for one-way propagation. For the special case of a spherical wave, comparisons are made with experimental data. The double pass case is also considered. Analytic expressions for the wave structure function are given that incorporate reflection from a smooth target for an incident spherical wave. Additionally, analytic expressions for the frequency spread of the temporal frequency spectrum and the temporal frequency spectrum itself, after one-way propagation for horizontal and slant paths, are derived for plane and spherical waves. These results are also based on the Rytov perturbation method . Expressions that are believed to be valid in all turbulence conditions are also developed by use of the effective atmospheric spectral model used in the wave structure function development. Finally, double pass frequency spread expressions are also presented. As in the case of the wave structure function, reflection from a smooth target with an incident spherical wave is considered

Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond

This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and beyond. The first study of its kind, we found that Bayesian Neural Networks (BNNs) and intelligent training techniques allowed our models to maintain performance amidst significant data shifts. Our results emphasize the importance of developing robust AI models capable of matching or surpassing clinician predictions, even under challenging conditions. Our exploration of model explainability revealed that stochastic models generate more diverse and personalized explanations thereby highlighting the need for AI models that provide detailed and individualized insights in real-world clinical settings. Furthermore, we underscored the importance of quantifying uncertainty in AI models which enables clinicians to make better-informed decisions based on reliable predictions. Our study advocates for prioritizing implementation science in AI research for healthcare and ensuring that AI solutions are practical, beneficial, and sustainable in real-world clinical environments. By addressing unique challenges and complexities in healthcare settings, researchers can develop AI models that effectively improve clinical practice and patient outcomes

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

Occupancy by key transcription factors is a more accurate predictor of enhancer activity than histone modifications or chromatin accessibility

BACKGROUND: Regulated gene expression controls organismal development, and variation in regulatory patterns has been implicated in complex traits. Thus accurate prediction of enhancers is important for further understanding of these processes. Genome-wide measurement of epigenetic features, such as histone modifications and occupancy by transcription factors, is improving enhancer predictions, but the contribution of these features to prediction accuracy is not known. Given the importance of the hematopoietic transcription factor TAL1 for erythroid gene activation, we predicted candidate enhancers based on genomic occupancy by TAL1 and measured their activity. Contributions of multiple features to enhancer prediction were evaluated based on the results of these and other studies. RESULTS: TAL1-bound DNA segments were active enhancers at a high rate both in transient transfections of cultured cells (39 of 79, or 56%) and transgenic mice (43 of 66, or 65%). The level of binding signal for TAL1 or GATA1 did not help distinguish TAL1-bound DNA segments as active versus inactive enhancers, nor did the density of regulation-related histone modifications. A meta-analysis of results from this and other studies (273 tested predicted enhancers) showed that the presence of TAL1, GATA1, EP300, SMAD1, H3K4 methylation, H3K27ac, and CAGE tags at DNase hypersensitive sites gave the most accurate predictors of enhancer activity, with a success rate over 80% and a median threefold increase in activity. Chromatin accessibility assays and the histone modifications H3K4me1 and H3K27ac were sensitive for finding enhancers, but they have high false positive rates unless transcription factor occupancy is also included. CONCLUSIONS: Occupancy by key transcription factors such as TAL1, GATA1, SMAD1, and EP300, along with evidence of transcription, improves the accuracy of enhancer predictions based on epigenetic features. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13072-015-0009-5) contains supplementary material, which is available to authorized users

Latent Patient Cluster Discovery for Robust Future Forecasting and New-Patient Generalization.

Commonly referred to as predictive modeling, the use of machine learning and statistical methods to improve healthcare outcomes has recently gained traction in biomedical informatics research. Given the vast opportunities enabled by large Electronic Health Records (EHR) data and powerful resources for conducting predictive modeling, we argue that it is yet crucial to first carefully examine the prediction task and then choose predictive methods accordingly. Specifically, we argue that there are at least three distinct prediction tasks that are often conflated in biomedical research: 1) data imputation, where a model fills in the missing values in a dataset, 2) future forecasting, where a model projects the development of a medical condition for a known patient based on existing observations, and 3) new-patient generalization, where a model transfers the knowledge learned from previously observed patients to newly encountered ones. Importantly, the latter two tasks-future forecasting and new-patient generalizations-tend to be more difficult than data imputation as they require predictions to be made on potentially out-of-sample data (i.e., data following a different predictable pattern from what has been learned by the model). Using hearing loss progression as an example, we investigate three regression models and show that the modeling of latent clusters is a robust method for addressing the more challenging prediction scenarios. Overall, our findings suggest that there exist significant differences between various kinds of prediction tasks and that it is important to evaluate the merits of a predictive model relative to the specific purpose of a prediction task

Atmospheric-Induced Frequency Spread In Optical Waves

This paper introduces analytic expressions for the long time average atmospheric-induced frequency spread of optical waves propagating through clear air turbulence. Spherical wave results are given for the horizontal double-pass case with reflection from a smooth target for bistatic and monostatic channels. The models presented are expected to be valid for weak-to-moderate scintillation environments. The results are discussed in the context of \u27micro Doppler laser radar (LIDAR) target detection systems

<title>Atmospheric-induced frequency fluctuations in LIDAR</title>

It is well known that the transmission of an optical signal through the turbulent atmosphere results in random phase fluctuations. In turn, these random phase fluctuations impart a random frequency fluctuation onto the optical signal. As laser radar (lidar) systems rely on the evaluation of micro-Doppler frequency shifts of the reflected optical wave to determine certain target characteristics, it is critical to understand the impact of the atmospheric induced frequency fluctuations. Additionally, lidar systems used for defense applications would typically operate in moderate to strong atmospheric turbulence conditions. Hence, for such applications, it is necessary to develop models describing atmospheric induced frequency fluctuations of an optical wave that are valid in all regimes of optical turbulence. In this paper, we present preliminary results for a model of atmospheric induced frequency fluctuations for the double pass propagation problem in weak optical turbulence conditions and a possible method for extension of these results into moderate to strong turbulence conditions

Atmospheric Induced Frequency Fluctuations In Lidar

It is well known that the transmission of an optical signal through the turbulent atmosphere results in random phase fluctuations. In turn, these random phase fluctuations impart a random frequency fluctuation onto the optical signal. As laser radar (lidar) systems rely on the evaluation of micro-Doppler frequency shifts of the reflected optical wave to determine certain target characteristics, it is critical to understand the impact of the atmospheric induced frequency fluctuations. Additionally, lidar systems used for defense applications would typically operate in moderate to strong atmospheric turbulence conditions. Hence, for such applications, it is necessary to develop models describing atmospheric induced frequency fluctuations of an optical wave that are valid in all regimes of optical turbulence. In this paper, we present preliminary results for a model of atmospheric induced frequency fluctuations for the double pass propagation problem in weak optical turbulence conditions and a possible method for extension of these results into moderate to strong turbulence conditions