Absolute optical chiral analysis using cavity-enhanced polarimetry

Chiral analysis is central for scientific advancement in the fields of chemistry, biology, and medicine. It is also indispensable in the development and quality control of chiral compounds in the chemical and pharmaceutical industries. Current methods for chiral analysis, namely optical polarimetry, mass spectrometry and nuclear magnetic resonance, are either insensitive, have low time resolution, or require preparation steps, and so are unsuited for monitoring chiral dynamics within complex environments: the current need of both research and industry. Here we present the concept of absolute optical chiral analysis, as enabled by cavity-enhanced polarimetry, which allows for accurate unambiguous enantiomeric characterization and enantiomeric-excess determination of chiral compounds within complex mixtures at trace levels, without the need for calibration, even in the gas phase. The utility of this approach is demonstrated by post chromatographic analysis of complex gaseous mixtures, the rapid quality control of perfume mixtures containing chiral volatile compounds, and the online in-situ observation of chiral volatile emissions from a plant under stress. Our approach and technology offer a step change in chiral compound determination, enabling online quality control of complex chemical mixtures, identification of counterfeit goods, detection of pests on plants, and assessment of chiral emission processes from climate relevant ecosystems.

Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization

The Patch-seq approach is a powerful variation of the patch-clamp technique that allows for the combined electrophysiological, morphological, and transcriptomic characterization of individual neurons. To generate Patch-seq datasets at scale, we identified and refined key factors that contribute to the efficient collection of high-quality data. We developed patch-clamp electrophysiology software with analysis functions specifically designed to automate acquisition with online quality control. We recognized the importance of extracting the nucleus for transcriptomic success and maximizing membrane integrity during nucleus extraction for morphology success. The protocol is generalizable to different species and brain regions, as demonstrated by capturing multimodal data from human and macaque brain slices. The protocol, analysis and acquisition software are compiled at https://github.com/AllenInstitute/patchseqtools. This resource can be used by individual labs to generate data across diverse mammalian species and that is compatible with large publicly available Patch-seq datasets.

A Digital Twin-Driven Method for Online Quality Control in Process Industry

To ensure the stability of product quality and production continuity, quality control is drawing increasing attention from the process industry. However, current methods cannot meet requirements with regard to time series data, high coupling parameters, delayed data acquisition and ambiguous operation control. A digital twin-driven (DTD) method for real-time monitoring, evaluation and optimization of process parameters that are strongly related to product quality is proposed. Based on a process simulation model, production status information and quality related data are obtained. Combined with an improved genetic algorithm (GA), a time sequential prediction model of bidirectional gated recurrent unit (bi-GRU) with attention mechanism (AM) is built to flexibly allocate parameter weights, accurately predict product quality, timely evaluate technical process and rapidly generate optimized control plans. A typical case study and relevant filed tests from the process industry are presented to prove the effectiveness of the method. Results indicate that the proposed method clearly outperforms its competitors.

Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization

The Patch-seq approach is a powerful variation of the standard patch clamp technique that allows for the combined electrophysiological, morphological, and transcriptomic characterization of individual neurons. To generate Patch-seq datasets at a scale and quality that can be integrated with high-throughput dissociated cell transcriptomic data, we have optimized the technique by identifying and refining key factors that contribute to the efficient collection of high-quality data. To rapidly generate high-quality electrophysiology data, we developed patch clamp electrophysiology software with analysis functions specifically designed to automate acquisition with online quality control. We recognized a substantial improvement in transcriptomic data quality when the nucleus was extracted following the recording. For morphology success, the importance of maximizing the neuron’s membrane integrity during the extraction of the nucleus was much more critical to success than varying the duration of the electrophysiology recording. We compiled the lab protocol with the analysis and acquisition software at https://github.com/AllenInstitute/patchseqtools. This resource can be used by individual labs to generate Patch-seq data across diverse mammalian species and that is compatible with recent large-scale publicly available Allen Institute Patch-seq datasets.