Using machine learning for chemical-free histological tissue staining.

Hematoxylin and eosin staining can be hazardous, expensive, and prone to error and variability. To circumvent these issues, artificial intelligence/machine learning models such as generative adversarial networks (GANs), are being used to 'virtually' stain unstained tissue images indistinguishable from chemically stained tissue. Frameworks such as deep convolutional GANs (DCGAN) and conditional GANs (CGANs) have successfully generated highly reproducible 'stained' images. However, their utility may be limited by requiring registered, paired images which can be difficult to obtain. To avoid these dataset requirements, we attempted to use an unsupervised CycleGAN pix2pix model(5,6) to turn unpaired, unstained bright-field images into pathologist-approved digitally 'stained' images. Using formalin-fixed-paraffin-embedded liver samples, 5µm section images (20x) were obtained before and after staining to create "stained" an "unstained" datasets. Model implementation was conducted using Ubuntu 20.04.4 LTS, 32 GB RAM, Intel Core i7-9750 CPU @2.6 GHz, Nvidia GeForce RTX 2070 Mobile, Python 3.7.11 and Tensorflow 2.9.1. The CycleGAN framework utilized a u-net-based generator and discriminator from pix2pix, a CGAN. The CycleGAN used a modified loss function, cycle consistent loss that assumed unpaired images, so loss was measured twice. To our knowledge, this is the first documented application of this architecture using unpaired bright-field images. Results and suggested improvements are discussed.

Colorimetric Gas Detection Tubes: Limits of Detection and Evaluation Using Active Chemical Warfare Agents.

Chemical weapons continue to be an ongoing threat that necessitates the improvement of existing detection technologies where new technologies are absent. Lower limits of detection will facilitate early warning of exposure to chemical weapons and enable more rapid deployment of countermeasures. Here, we evaluate two colorimetric gas detection tubes, developed by Draeger Inc., for sarin and sulfur mustard chemical warfare agents and determine their limits of detection using active chemical agent. Being that commercial companies are only able to use chemical agent simulants during sensor development, it is imperative to determine limits of detection using active agent. The limit of detection was determined based on the absence of a reasonably perceptible color response at incrementally lower concentrations. A chemical vapor generator was constructed to produce stable and quantifiable concentrations of chemical agent vapor, with the presence of chemical agent verified and monitored by a secondary detector. The limits of detection of the colorimetric gas detection tubes were determined to be 0.0046 ± 0.0002 and 2.1 ± 0.3 mg/m3 for sarin and sulfur mustard, respectively. The response of the sarin detection tube was readily observable with little issue. The sulfur mustard detection tube exhibited a weaker response to active agent compared to the simulant that was used during development, which will affect their concept of operations in real-world detection scenarios.

Physics-based agent to simulant correlations for vapor phase mass transport.

Chemical warfare agent simulants are often used as an agent surrogate to perform environmental testing, mitigating exposure hazards. This work specifically addresses the assessment of downwind agent vapor concentration resulting from an evaporating simulant droplet. A previously developed methodology was used to estimate the mass diffusivities of the chemical warfare agent simulants methyl salicylate, 2-chloroethyl ethyl sulfide, di-ethyl malonate, and chloroethyl phenyl sulfide. Along with the diffusivity of the chemical warfare agent bis(2-chloroethyl) sulfide, the simulant diffusivities were used in an advection-diffusion model to predict the vapor concentrations downwind from an evaporating droplet of each chemical at various wind velocities and temperatures. The results demonstrate that the simulant-to-agent concentration ratio and the corresponding vapor pressure ratio are equivalent under certain conditions. Specifically, the relationship is valid within ranges of measurement locations relative to the evaporating droplet and observation times. The valid ranges depend on the relative transport properties of the agent and simulant, and whether vapor transport is diffusion or advection dominant.