Weak coupling interactions of colloidal lead sulphide nanocrystals with silicon photonic crystal nanocavities near 1.55 microns at room temperature

We observe the weak coupling of lead sulphide nanocrystals to localized defect modes of 2-dimensional silicon nanocavities. Cavity resonances characterized with ensemble nanocrystals are verified with cold-cavity measurements using integrated waveguides. Polarization dependence of the cavity field modes is observed. The linewidths measured in coupling experiments are broadened in comparison to the cold-cavity characterization, partly due to large homogeneous linewidths of the nanocrystals. The calculated Purcell factor for a single exciton is 75, showing promise toward applications in single photon systems. These novel light sources operate near 1.55 micron wavelengths at room temperature, permitting integration with current fiber communications networks.Comment: 11 pages, 4 figures, Content Modified from original manuscript with additional measurements and simulation

Evaluation of performance: multi-armed bandit vs. contextual bandit

Master of ScienceDepartment of Computer ScienceWilliam H. HsuThis work compares two methods, the multi-armed bandit (MAB) and contextual multi-armed bandit (CMAB), for action recommendation in a sequential decision making domain. It empirically evaluates their effectiveness on a customer relationship management task. The goal of this project is to experiment using [epsilon]-greedy and random selection strategies to characterize the exploration vs. exploitation tradeoff , which manifests when trying to increase or maximize profit while gaining new information regarding the process. The first method under observation, the multi-armed bandit (MAB), is simpler to compute and scales better to larger amounts of data; it has a wide range of applicability, including website optimization, clinical trials, adaptive routing, and stock trading. The contextual multi-armed bandit (CMAB) is an advanced version of the multi-armed bandit which takes into consideration the user’s past usage patterns, especially historical features of the user’s search history; its training data incorporates this context, resulting in a model that is more accurate but also requires a lot of user data which incurs privacy liabilities, an adverse property. This study measures the difference in outcome if the MAB or CMAB have access to user data and assesses, for a real-world application domain, whether this trade-off is significant and worthwhile in the bigger prospective

Metabolite, protein, and tissue dysfunction associated with COVID-19 disease severity

Proteins are direct products of the genome and metabolites are functional products of interactions between the host and other factors such as environment, disease state, clinical information, etc. Omics data, including proteins and metabolites, are useful in characterizing biological processes underlying COVID-19 along with patient data and clinical information, yet few methods are available to effectively analyze such diverse and unstructured data. Using an integrated approach that combines proteomics and metabolomics data, we investigated the changes in metabolites and proteins in relation to patient characteristics (e.g., age, gender, and health outcome) and clinical information (e.g., metabolic panel and complete blood count test results). We found significant enrichment of biological indicators of lung, liver, and gastrointestinal dysfunction associated with disease severity using publicly available metabolite and protein profiles. Our analyses specifically identified enriched proteins that play a critical role in responses to injury or infection within these anatomical sites, but may contribute to excessive systemic inflammation within the context of COVID-19. Furthermore, we have used this information in conjunction with machine learning algorithms to predict the health status of patients presenting symptoms of COVID-19. This work provides a roadmap for understanding the biochemical pathways and molecular mechanisms that drive disease severity, progression, and treatment of COVID-19

Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.

IntroductionThis study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-term readmissions. The prediction of unplanned readmissions after index CAS remains challenging. There is a need to leverage deep machine learning algorithms in order to develop robust prediction tools for early readmissions.MethodsPatients undergoing inpatient CAS during the year 2017 in the US Nationwide Readmission Database (NRD) were evaluated for the rates, predictors, and costs of unplanned 30-day readmission. Logistic regression, support vector machine (SVM), deep neural network (DNN), random forest, and decision tree models were evaluated to generate a robust prediction model.ResultsWe identified 16,745 patients who underwent CAS, of whom 7.4% were readmitted within 30 days. Depression [p < 0.001, OR 1.461 (95% CI 1.231-1.735)], heart failure [p < 0.001, OR 1.619 (95% CI 1.363-1.922)], cancer [p < 0.001, OR 1.631 (95% CI 1.286-2.068)], in-hospital bleeding [p = 0.039, OR 1.641 (95% CI 1.026-2.626)], and coagulation disorders [p = 0.007, OR 1.412 (95% CI 1.100-1.813)] were the strongest predictors of readmission. The artificial intelligence machine learning DNN prediction model has a C-statistic value of 0.79 (validation 0.73) in predicting the patients who might have all-cause unplanned readmission within 30 days of the index CAS discharge.ConclusionsMachine learning derived models may effectively identify high-risk patients for intervention strategies that may reduce unplanned readmissions post carotid artery stenting.Central illustrationFigure 2: ROC and AUPRC analysis of DNN prediction model with other classification models on 30-day readmission data for CAS subjects

Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.

INTRODUCTION: This study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-term readmissions. The prediction of unplanned readmissions after index CAS remains challenging. There is a need to leverage deep machine learning algorithms in order to develop robust prediction tools for early readmissions. METHODS: Patients undergoing inpatient CAS during the year 2017 in the US Nationwide Readmission Database (NRD) were evaluated for the rates, predictors, and costs of unplanned 30-day readmission. Logistic regression, support vector machine (SVM), deep neural network (DNN), random forest, and decision tree models were evaluated to generate a robust prediction model. RESULTS: We identified 16,745 patients who underwent CAS, of whom 7.4% were readmitted within 30 days. Depression [p < 0.001, OR 1.461 (95% CI 1.231–1.735)], heart failure [p < 0.001, OR 1.619 (95% CI 1.363–1.922)], cancer [p < 0.001, OR 1.631 (95% CI 1.286–2.068)], in-hospital bleeding [p = 0.039, OR 1.641 (95% CI 1.026–2.626)], and coagulation disorders [p = 0.007, OR 1.412 (95% CI 1.100–1.813)] were the strongest predictors of readmission. The artificial intelligence machine learning DNN prediction model has a C-statistic value of 0.79 (validation 0.73) in predicting the patients who might have all-cause unplanned readmission within 30 days of the index CAS discharge. CONCLUSIONS: Machine learning derived models may effectively identify high-risk patients for intervention strategies that may reduce unplanned readmissions post carotid artery stenting. CENTRAL ILLUSTRATION: Figure 2: ROC and AUPRC analysis of DNN prediction model with other classification models on 30-day readmission data for CAS subjects SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12325-021-01709-7

Molecular epidemiology of pregnancy using omics data: advances, success stories, and challenges.

Multi-omics approaches have been successfully applied to investigate pregnancy and health outcomes at a molecular and genetic level in several studies. As omics technologies advance, research areas are open to study further. Here we discuss overall trends and examples of successfully using omics technologies and techniques (e.g., genomics, proteomics, metabolomics, and metagenomics) to investigate the molecular epidemiology of pregnancy. In addition, we outline omics applications and study characteristics of pregnancy for understanding fundamental biology, causal health, and physiological relationships, risk and prediction modeling, diagnostics, and correlations

Molecular epidemiology of pregnancy using omics data: advances, success stories, and challenges

Abstract Multi-omics approaches have been successfully applied to investigate pregnancy and health outcomes at a molecular and genetic level in several studies. As omics technologies advance, research areas are open to study further. Here we discuss overall trends and examples of successfully using omics technologies and techniques (e.g., genomics, proteomics, metabolomics, and metagenomics) to investigate the molecular epidemiology of pregnancy. In addition, we outline omics applications and study characteristics of pregnancy for understanding fundamental biology, causal health, and physiological relationships, risk and prediction modeling, diagnostics, and correlations