Impact of nonlinear effects in Si towards integrated microwave-photonic applications.

As one of major integrated microwave photonics (IMWP) platforms, Si photonics exhibits the intensity-dependent Kerr effect and two-photon absorption (TPA) with associated free carrier effects (FCE). At the commonly used 1.55 µm, TPA losses and the associated FCE would eventually limit the dynamic range of Si photonic links. Resonating structures such as ring resonators (RRs) experience enhanced nonlinear effects due to significant intensity buildup. According to the bandgap characteristics of Si, TPA can be eliminated at and beyond 2.2 µm. In this work, a systemic simulation of straight waveguides and RRs is performed at wavelengths from 1.55 to 2.2 µm where the wavelength-dependent TPA loss is investigated. Moreover, the Kerr effect leads to unwanted change of refractive index, which shifts the RR resonant wavelength at both 1.55 and 2.2 µm, thus needing shift compensation. Compensated RRs operating at 2.2 µm could open a new venue for Si photonics towards IMWP applications.

Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition.