Chip-scale high-precision measurements of physical quantities such as temperature, pressure, refractive index, and analytes have become common with nanophotonics and nanoplasmonics resonance cavities. Despite several important accomplishments, such optical sensors are still limited in their performances in the short and, in particular, long time regimes. Two major limitations are environmental fluctuations, which are imprinted on the measured signal, and the lack of miniaturized, scalable robust and precise methods of measuring optical frequencies directly. Here, by utilizing a frequency-locked loop combined with a reference resonator, we overcome these limitations and convert the measured signal from the optical domain to the radio-frequency domain. By doing so, we realize a highly precise on-chip sensing device with sensing precision approaching 10 −8 in effective refractive index units, and 90 μK in temperature. Such an approach paves the way for single particle detection and high-precision chip-scale thermometry.
Pre-clinical and clinical scientific endeavours provide complementary perspectives on fundamental biological processes of translational value. Harnessing the information value on the totality of such knowledge requires novel approaches to integrate across the breadth of experimental space. High-throughput screens are often the first step on this bridge to the patient. However, their representative capacity to encompass all the cellular contexts encountered in a patient are often limited due to experimental constraints. Thus, we present PerturbX, a new deep learning model to predict transcriptional responses to chemical or genetic perturbations in unobserved cellular contexts, and to uncover interpretable factors of variation associated with the predicted response. PerturbX can be applied in both an unimodal or multimodal setting. We believe that our proposed approach has the ability to inform novel biomarker discovery and contribute to a redefinition of the drug development pipeline.
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