Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry

Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion

Service based virtual RAN architecture for next generation cellular systems

Service based architecture (SBA) is a paradigm shift from Service-Oriented Architecture (SOA) to microservices, combining their principles. Network virtualization enables the application of SBA in cellular systems. To better guide the software design of this virtualized cellular system with SBA, this paper presents a software perspective and a positional approach to using fundamental development principles for adapting SBA in virtualized Radio Access Networks (vRANs). First, we present the motivation for using an SBA in cellular radio systems. Then, we explore the critical requirements, key principles, and components for the software to provide radio services in SBA. We also explore the potential of applying SBA-based Radio Access Network (RAN) by comparing the functional split requirements of 5G RAN with existing open-source software and accelerated hardware implementations of service bus, and discuss the limitations of SBA. Finally, we present some discussions, future directions, and a roadmap of applying such a high-level design perspective of SBA to next-generation RAN infrastructure.This work was supported in part by the European Union (EU) H2020 5GROWTH Project under Grant 856709, in part by the Generalitat de Catalunya under Grant 2017 SGR 1195, and in part by the National Program on Equipment and Scientific and Technical Infrastructure under the European Regional Development Fund (FEDER) under Grant EQC2018-005257-P

Evolution and Impact of High Content Imaging

Abstract/outline: The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990′s. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging:• Evolution and impact of high content imaging: An academic perspective• Evolution and impact of high content imaging: An industry perspective• Evolution of high content image analysis• Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications• The role of data integration and multiomics• The role and evolution of image data repositories and sharing standards• Future perspective of high content imaging hardware and softwar