Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system

High-throughput cell sorting with flow cytometers is an important tool in modern clinical cell studies. Most cytometers use biomarkers that selectively bind to the cell, but induce significant changes in morphology and inner cell processes leading sometimes to its death. This makes label-based cell sorting schemes unsuitable for further investigation. We propose a label-free technique that uses a digital inline holographic microscopy for cell imaging and an integrated, optical neural network for high-speed classification. The perspective of dense integration makes it attractive to ultrafast, large-scale cell sorting. Network simulations for a ternary classification task (monocytes/granulocytes/lymphocytes) resulted in 89% accuracy

The Design of the n2EDM Experiment

We present the design of a next-generation experiment, n2EDM, currently under construction at the ultracold neutron source at the Paul Scherrer Institute (PSI) with the aim of carrying out a high-precision search for an electric dipole moment of the neutron. The project builds on experience gained with the previous apparatus operated at PSI until 2017, and is expected to deliver an order of magnitude better sensitivity with provision for further substantial improvements. An overview is of the experimental method and setup is given, the sensitivity requirements for the apparatus are derived, and its technical design is described

A functional variant in the Stearoyl-CoA desaturase gene promoter enhances fatty acid desaturation in pork

There is growing public concern about reducing saturated fat intake. Stearoyl-CoA desaturase (SCD) is the lipogenic enzyme responsible for the biosynthesis of oleic acid (18:1) by desaturating stearic acid (18:0). Here we describe a total of 18 mutations in the promoter and 3′ non-coding region of the pig SCD gene and provide evidence that allele T at AY487830:g.2228T>C in the promoter region enhances fat desaturation (the ratio 18:1/18:0 in muscle increases from 3.78 to 4.43 in opposite homozygotes) without affecting fat content (18:0+18:1, intramuscular fat content, and backfat thickness). No mutations that could affect the functionality of the protein were found in the coding region. First, we proved in a purebred Duroc line that the C-T-A haplotype of the 3 single nucleotide polymorphisms (SNPs) (g.2108C>T; g.2228T>C; g.2281A>G) of the promoter region was additively associated to enhanced 18:1/18:0 both in muscle and subcutaneous fat, but not in liver. We show that this association was consistent over a 10-year period of overlapping generations and, in line with these results, that the C-T-A haplotype displayed greater SCD mRNA expression in muscle. The effect of this haplotype was validated both internally, by comparing opposite homozygote siblings, and externally, by using experimental Duroc-based crossbreds. Second, the g.2281A>G and the g.2108C>T SNPs were excluded as causative mutations using new and previously published data, restricting the causality to g.2228T>C SNP, the last source of genetic variation within the haplotype. This mutation is positioned in the core sequence of several putative transcription factor binding sites, so that there are several plausible mechanisms by which allele T enhances 18:1/18:0 and, consequently, the proportion of monounsaturated to saturated fat.This research was supported by grants from the Spanish Ministry of Science and Innovation (AGL2009-09779 and AGL2012-33529). RRF is recipient of a PhD scholarship from the Spanish Ministry of Science and Innovation (BES-2010-034607). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of manuscript

The design of the n2EDM experiment: nEDM Collaboration

We present the design of a next-generation experiment, n2EDM, currently under construction at the ultracold neutron source at the Paul Scherrer Institute (PSI) with the aim of carrying out a high-precision search for an electric dipole moment of the neutron. The project builds on experience gained with the previous apparatus operated at PSI until 2017, and is expected to deliver an order of magnitude better sensitivity with provision for further substantial improvements. An overview is of the experimental method and setup is given, the sensitivity requirements for the apparatus are derived, and its technical design is described

Neural network for blood cell classification in a holographic microscopy system

Modern clinical laboratories are equipped with high-throughput flow cytometers for fast and accurate cell sorting. Most cytometers use selective biomarkers which often induce significant changes in the cell morphology, sometimes leading to cell death. However, for purposes like cell imaging there exist label-free techniques, for example digital inline holographic microscopy. Yet the image reconstruction algorithms needed to analyze the images do not scale up easily to large numbers of cells. We suggest an integrated, optical neural network to deal with the high-speed image classification with the promise of dense integration for ultrafast, cell sorting. A ternary classification task, distinguishing between monocytes, granulocytes, and lymphocytes resulted in 89% accuracy