Related papers: Single-cell identification with quantum-enhanced n…
This paper addresses the problem of quantifying biomarkers in multi-stained tissues, based on color and spatial information. A deep learning based method that can automatically localize and quantify the cells expressing biomarker(s) in a…
Ensembles of nitrogen-vacancy (NV) centers are used as sensors to detect NMR signals from micron-sized samples at room temperature. In this scenario, the regime of large magnetic fields is especially interesting as it leads to a large…
In recent years, several unsupervised cell segmentation methods have been presented, trying to omit the requirement of laborious pixel-level annotations for the training of a cell segmentation model. Most if not all of these methods handle…
Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning paradigm.…
We present a protocol that detects molecular conformational changes with two nitroxide electron-spin labels and a nitrogen-vacancy (NV) center in diamond. More specifically, we demonstrate that the NV can detect energy shifts induced by the…
Computational pathology relies on effective representation learning to support cancer research and precision medicine. Although self-supervised learning has driven major progress at the patch and whole-slide image levels, representation…
Quantum sensing is a key component of quantum technology, enabling highly sensitive magnetometry. We combined a nickelocene molecule with scanning tunneling microscopy to perform versatile spin sensing of magnetic surfaces, namely of model…
Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity, enabling detailed analysis of complex biological systems at single-cell resolution. However, the high dimensionality and technical noise…
Calcium imaging has emerged as a workhorse method in neuroscience to investigate patterns of neuronal activity. Instrumentation to acquire calcium imaging movies has rapidly progressed and has become standard across labs. Still, algorithms…
Despite their superior performance, deep-learning methods often suffer from the disadvantage of needing large-scale well-annotated training data. In response, recent literature has seen a proliferation of efforts aimed at reducing the…
Detection and differentiation of circulating tumor cells (CTCs) and non-CTCs in blood draws of cancer patients pose multiple challenges. While the gold standard relies on tedious manual evaluation of an automatically generated selection of…
We propose a method to accurately obtain the ratio of tumor cells over an entire histological slide. We use deep fully convolutional neural network models trained to detect and classify cells on images of H&E-stained tissue sections.…
Sensing of signals from biological processes, such as action potential propagation in nerves, are essential for clinical diagnosis and basic understanding of physiology. Sensing can be performed electrically by placing sensor probes near or…
Solid-state quantum emitters have emerged as robust single-photon sources and addressable spins: key components in rapidly developing quantum technologies for broadband magnetometry, biological sensing, and quantum information science.…
Early detection and prognosis of breast cancer are feasible by utilizing histopathological grading of biopsy specimens. This research is focused on detection and grading of nuclear pleomorphism in histopathological images of breast cancer.…
Nuclear Magnetic Resonance (NMR) spectroscopy leverages nuclear magnetization to probe molecules' chemical environment, structure, and dynamics, with applications spanning from pharmaceuticals to the petroleum industry. Despite its utility,…
Single-cell RNA sequencing (scRNA-seq) data simulation is limited by classical methods that rely on linear correlations, failing to capture the intrinsic, nonlinear dependencies. No existing simulator jointly models gene-gene and cell-cell…
Proton nuclear magnetic resonance (1H-NMR) is a widely used tool for chemical structural analysis. However, 1H-NMR spectra suffer from natural aberrations that render computer-assisted automated identification of these spectra difficult,…
Our growing ability to tailor healthcare to the needs of individuals has the potential to transform clinical treatment. However, the measurement of multiple biomarkers to inform clinical decisions requires rapid, effective, and affordable…
Surface-enhanced Raman spectroscopy (SERS) sensing of DNA sequences by plasmonic nanopores could pave a way to new generation single-molecule sequencing platforms. The SERS discrimination of single DNA bases depends critically on the time…