On-farm NIR sensor for milk analysis: Exploitation of bias monitoring and bias correction.

Long-term studies have shown a bias drift over time in the prediction performance of near-infrared spectroscopy measurement systems. This bias drift generally requires extra laboratory reference measurements to detect and correct for this bias. Since these reference measurements are expensive and time consuming, there is a need for advanced methodologies for bias drift monitoring and correction without the need for taking extra samples.

Landscape of Bone Marrow Metastasis in Human Neuroblastoma Unraveled by Transcriptomics and Deep Multiplex Imaging.

While the bone marrow attracts tumor cells in many solid cancers leading to poor outcome in affected patients, comprehensive analyses of bone marrow metastases have not been performed on a single-cell level. We here set out to capture tumor heterogeneity and unravel microenvironmental changes in neuroblastoma, a solid cancer with bone marrow involvement. To this end, we employed a multi-omics data mining approach to define a multiplex imaging panel and developed DeepFLEX, a pipeline for subsequent multiplex image analysis, whereby we constructed a single-cell atlas of over 35,000 disseminated tumor cells (DTCs) and cells of their microenvironment in the metastatic bone marrow niche.

A brief note on application of domain-invariant PLS for adapting near-infrared spectroscopy calibrations between different physical forms of samples.

Near-infrared (NIR) calibration models are widely developed and routinely used for the prediction of physicochemical properties of samples. However, the main challenge with NIR models is that they are highly specific to the physical form of the samples. For example, a NIR calibration established for solid samples can usually not be used for the same samples in powdered form.