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Circadian rhythms play a vital role in maintaining a person’s well-being but remain difficult to quantify accurately. Numerous approaches exist to measure these rhythms, but they often suffer from performance issues on the individual level. This work implements a Steady-State Kalman Filter as a method for estimating the circadian phase shifts from biometric signals. Our framework can automatically fit the filter’s parameters to biometric data obtained for each individual, and we were able to consistently estimate the phase shift within 1 hour of melatonin estimates on 100% of all subjects in this study. The estimation method opens up the possibility of real-time control and assessment of the circadian system, as well as chronotherapeutic intervention. 

Solar flares not only pose risks to outer space technologies and astronauts’ well being, but also cause disruptions on earth to our high-tech, interconnected infrastructure our lives highly depend on. While a number of machine-learning methods have been proposed to improve flare prediction, none of them, to the best of our knowledge, have investigated the impact of outliers on the reliability and robustness of those models’ performance. In this study, we investigate the impact of outliers in a multivariate time series benchmark dataset, namely SWAN-SF, on flare prediction models, and test our hypothesis. That is, there exist outliers in SWAN-SF, removal of which enhances the performance of the prediction models on unseen datasets. We employ Isolation Forest to detect the outliers among the weaker flare instances. Several experiments are carried out using a large range of contamination rates which determine the percentage of present outliers. We assess the quality of each dataset in terms of its actual contamination using TimeSeriesSVC. In our best findings, we achieve a 279% increase in True Skill Statistic and 68% increase in Heidke Skill Score. The results show that overall a significant improvement can be achieved for flare prediction if outliers are detected and removed properly. 

Abstract There is a need for new in vitro systems that enable pharmaceutical companies to collect more physiologically-relevant information on drug response in a low-cost and high-throughput manner. For this purpose, three-dimensional (3D) spheroidal models have been established as more effective than two-dimensional models. Current commercial techniques, however, rely heavily on self-aggregation of dissociated cells and are unable to replicate key features of the native tumor microenvironment, particularly due to a lack of control over extracellular matrix components and heterogeneity in shape, size, and aggregate forming tendencies. In this study, we overcome these challenges by coupling tissue engineering toolsets with microfluidics technologies to create engineered cancer microspheres. Specifically, we employ biosynthetic hydrogels composed of conjugated poly(ethylene glycol) (PEG) and fibrinogen protein (PEG-Fb) to create engineered breast and colorectal cancer tissue microspheres for 3D culture, tumorigenic characterization, and examination of potential for high-throughput screening (HTS). MCF7 and MDA-MB-231 cell lines were used to create breast cancer microspheres and the HT29 cell line and cells from a stage II patient-derived xenograft (PDX) were encapsulated to produce colorectal cancer (CRC) microspheres. Using our previously developed microfluidic system, highly uniform cancer microspheres (intra-batch coefficient of variation (CV) ≤ 5%, inter-batch CV < 2%) with high cell densities (>20×106 cells/ml) were produced rapidly, which is critical for use in drug testing. Encapsulated cells maintained high viability and displayed cell type-specific differences in morphology, proliferation, metabolic activity, ultrastructure, and overall microsphere size distribution and bulk stiffness. For PDX CRC microspheres, the percentage of human (70%) and CRC (30%) cells was maintained over time and similar to the original PDX tumor, and the mechanical stiffness also exhibited a similar order of magnitude (103 Pa) to the original tumor. The cancer microsphere system was shown to be compatible with an automated liquid handling system for administration of drug compounds; MDA-MB-231 microspheres were distributed in 384 well plates and treated with staurosporine (1 μM) and doxorubicin (10 μM). Expected responses were quantified using CellTiter-Glo® 3D, demonstrating initial applicability to HTS drug discovery. PDX CRC microspheres were treated with Fluorouracil (5FU) (10 to 500 μM) and displayed a decreasing trend in metabolic activity with increasing drug concentration. Providing a more physiologically relevant tumor microenvironment in a high-throughput and low-cost manner, the PF hydrogel-based cancer microspheres could potentially improve the translational success of drug candidates by providing more accurate in vitro prediction of in vivo drug efficacy. Citation Format: Elizabeth A. Lipke, Wen J. Seeto, Yuan Tian, Mohammadjafar Hashemi, Iman Hassani, Benjamin Anbiah, Nicole L. Habbit, Michael W. Greene, Dmitriy Minond, Shantanu Pradhan. Production of cancer tissue-engineered microspheres for high-throughput screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 175. 

Weak measurement (WM) with state pre- and post-selection can amplify otherwise undetectable small signals and thus has potential in precision measurement applications. Although frequency measurements offer the hitherto highest precision due to the stable narrow atomic transitions, it remains a long-standing interest to develop new schemes to further escalate their performance. Here, we demonstrate a WM-enhanced correlation spectroscopy technique capable of narrowing the resonance linewidth down to 0.1 Hz in a room-temperature atomic vapour cell. The potential of this technique for precision measurement is demonstrated through weak magnetic-field sensing. By judiciously pre- and post-selecting frequency-modulated input and output optical states in a nearly orthogonal manner, a sensitivity of 7 fT Hz^(−1/2) at a low frequency near DC is achieved using only one laser beam with 15 µW of power. Additionally, our results extend the WM framework to a non-Hermitian Hamiltonian and shed new light on metrology and bio-magnetic field sensing. 

Non-Hermitian optical systems with parity-time (PT) symmetry have recently revealed many intriguing prospects that outperform conservative structures. The previous works are mostly rooted in complex arrangements with controlled gain-loss interplay. Here, we demonstrate anti-PT symmetry inherent in the nonlinear optical interaction based upon forward optical four-wave mixing in a laser-cooled atomic ensemble with negligible linear gain and loss. We observe that the pair of frequency modes undergo a nontrivial anti-PT phase transition between coherent power oscillation and optical parametric amplification in presence of a large phase mismatch.