ALBERT

Description: With the increasing sensitivity and accuracy of contemporary high-performance electronic information systems to electromagnetic energy, they are also very vulnerable to be damaged by high-energy electromagnetic fields. In this work, an all-dielectric electromagnetic field sensor is proposed based on a microring resonator structure. The sensor is designed to work at 35 GHz RF field using a lithium niobate-on-insulator (LNOI) material system. The 2.5-D variational finite difference time domain (varFDTD) and finite difference eigenmode (FDE) methods are utilized to analyze the single-mode condition, bending loss, as well as the transmission loss to achieve optimized waveguide dimensions. In order to obtain higher sensitivity, the quality factor (Q-factor) of the microring resonator is optimized to be 106 with the total ring circumference of 3766.59 μm. The lithium niobate layer is adopted in z-cut direction to utilize TM mode in the proposed all-dielectric electric field sensor, and with the help of the periodically poled lithium niobate (PPLN) technology, the electro-optic (EO) tunability of the device is enhanced to 48 pm·μm/V.

Description: Background Sepsis is a highly lethal and heterogeneous disease. Utilization of an unsupervised method may identify novel clinical phenotypes that lead to targeted therapies and improved care. Methods Our objective was to derive clinically relevant sepsis phenotypes from a multivariate panel of physiological data using subgraph-augmented nonnegative matrix factorization. We utilized data from the Medical Information Mart for Intensive Care III database of patients who were admitted to the intensive care unit with sepsis. The extracted data contained patient demographics, physiological records, sequential organ failure assessment scores, and comorbidities. We applied frequent subgraph mining to extract subgraphs from physiological time series and performed nonnegative matrix factorization over the subgraphs to derive patient clusters as phenotypes. Finally, we profiled these phenotypes based on demographics, physiological patterns, disease trajectories, comorbidities and outcomes, and performed functional validation of their clinical implications. Results We analyzed a cohort of 5782 patients, derived three novel phenotypes of distinct clinical characteristics and demonstrated their prognostic implications on patient outcome. Subgroup 1 included relatively less severe/deadly patients (30-day mortality, 17%) and was the smallest-in-size group (n = 1218, 21%). It was characterized by old age (mean age, 73 years), a male majority (male-to-female ratio, 59-to-41), and complex chronic conditions. Subgroup 2 included the most severe/deadliest patients (30-day mortality, 28%) and was the second-in-size group (n = 2036, 35%). It was characterized by a male majority (male-to-female ratio, 60-to-40), severe organ dysfunction or failure compounded by a wide range of comorbidities, and uniquely high incidences of coagulopathy and liver disease. Subgroup 3 included the least severe/deadly patients (30-day mortality, 10%) and was the largest group (n = 2528, 44%). It was characterized by low age (mean age, 60 years), a balanced gender ratio (male-to-female ratio, 50-to-50), the least complicated conditions, and a uniquely high incidence of neurologic disease. These phenotypes were validated to be prognostic factors of mortality for sepsis patients. Conclusions Our results suggest that these phenotypes can be used to develop targeted therapies based on phenotypic heterogeneity and algorithms designed for monitoring, validating and intervening clinical decisions for sepsis patients.