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Application and performance evaluation of autonomous adaptive LIV data acquisition in the living nematode model system Caenorhabditis elegans. a Adaptive LIV data acquisition of an early-stage L2 C. elegans over a spatial map defined with domain knowledge to include the pharyngeal, nerve ring, and intestinal regions of the animal. b Density plot of sampled areas from adaptive LIV sampling for ease of more frequently sampled regions (red circle). c Coefficient heat maps of MCR component 1 (intensity range: 0.00-5.08), MCR component 4 (intensity range: 0.00-1.13), and the overlaid RG coefficient maps of both MCR components 1 and 4. Red cursor indicates same pixel; red circle indicates same region of dense sampling (scale bar, 10 μm). d Loading vectors for MCR components 1 (blue) and 4 (red) over evaluated frequency domain of 3500 to 2800 cm −1 . Vibrational stretching assignments are labeled as discussed in the main text and Supplementary Fig. 7. e Standard UG sampling compared with LIV sampling over a tightly bound, pharyngeal mapping region of late-stage L1 C. elegans (scale bar, 10 μm) per given time interval as defined by ordered, sample point domain. The first three principal components are displayed as RGB false color composites.
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Non-invasive and label-free spectral microscopy (spectromicroscopy) techniques can provide quantitative biochemical information complementary to genomic sequencing, transcriptomic profiling, and proteomic analyses. However, spectromicroscopy techniques generate high-dimensional data; acquisition of a single spectral image can range from tens of min...
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... using mean Voronoi-weighted LOO hϵ LOO i V to quantify modeling accuracy, we found that adaptive LIV data acquisition outperformed the non-adaptive data acquisition methods ( Supplementary Fig. 1) in this experimental system. To verify this conclusion, we tuned the spectral, on-target ratio (OTR) assessment by selecting the major contributing peak per component using our normalized mean standard spectra ( Supplementary Fig. 2) and variance spectra ( Supplementary Fig. 3); peak selection guided by normalized spectra emphasize chemical identification 17 over concentration in spectral interpretation. For high vacuum grease, we referenced the symmetric stretching mode of ν(Si-O-Si) at 798 cm −1 emerging from its fumed amorphous silica 18 composition. ...
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... when coupled with current mapping region software restrictions often lead to temporally inefficient spatiochemical mapping of unfixed samples. With our implemented user interface ( Supplementary Fig.e 6), we were able to apply domain knowledge in spatial and spectral restrictions to better optimize our adaptive data acquisition of C. elegans (Fig. 2a) for comparison against the high-spatial (step-size 1.5 μm) resolution map of the same sample. We found that increased adaptive LIV sampling in the spatial domain (Fig. 2b) identified regions characterized by chemistries consistent with those of known anatomical features. Sampling increased in either transitional or overlapping ...
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... interface ( Supplementary Fig.e 6), we were able to apply domain knowledge in spatial and spectral restrictions to better optimize our adaptive data acquisition of C. elegans (Fig. 2a) for comparison against the high-spatial (step-size 1.5 μm) resolution map of the same sample. We found that increased adaptive LIV sampling in the spatial domain (Fig. 2b) identified regions characterized by chemistries consistent with those of known anatomical features. Sampling increased in either transitional or overlapping anatomical regions between pharyngeal, head, neck, and body wall muscle 23 , regions of the nerve ring 24 , and the lipid-rich intestine 25 . Our qualitative post validation of ...
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... wall muscle 23 , regions of the nerve ring 24 , and the lipid-rich intestine 25 . Our qualitative post validation of adaptive data acquisition using multivariate curve resolution 26 (MCR) and Fourier self-deconvolution 27 (FSD) SR-FTIR analyses further confirmed these anatomical colocalization results with reliable MCR components 28 1 and 4 ( Fig. 2c) corresponding to hydrated proteins (amino acid ν(N-H) stretching modes) 19 and hydrated lipid assemblies (N-H, O-H, methyl, and ν(-(CH 2 ) n -) stretching modes) 19,29 , respectively ( Fig. 2d and Supplementary Fig. 7). With these two components overlapping in the more frequently sampled region, we verify that adaptive LIV data ...
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... Fourier self-deconvolution 27 (FSD) SR-FTIR analyses further confirmed these anatomical colocalization results with reliable MCR components 28 1 and 4 ( Fig. 2c) corresponding to hydrated proteins (amino acid ν(N-H) stretching modes) 19 and hydrated lipid assemblies (N-H, O-H, methyl, and ν(-(CH 2 ) n -) stretching modes) 19,29 , respectively ( Fig. 2d and Supplementary Fig. 7). With these two components overlapping in the more frequently sampled region, we verify that adaptive LIV data acquisition helps resolve spatiochemical gradients in a complex whole-organism model. ...
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... we were able to map the head region in 45 min with the LIV-based AADA software in comparison to ~4.9 h with the commercially available software. Lastly, we find that LIV-based AADA provides more comprehensive spatiochemical understanding of the total map domain at any given time interval in comparison to the established and standard UG sampling (Fig. 2e), suggesting that this aspect can be harnessed for further development of AADA to achieve adaptive highdimensional real-time, non-invasive, label-free imaging through modular additions to the sampling ...
Citations
The feasibility of Raman hperspectral imaging technique was explored to detect maize kernels aging during storage to eliminate its negative effects on maize sowing and storage. Both TTC test and germination test were employed to evaluate the viability of maize kernels and the anlysis based on pixel-level and object-level were conducted. Different variable selection algorithms were used for screening of key features related with viability and three modeling methods were performed to classify maize kernels viability. In addition, Whale Optimization Algorithm(WOA) optimization algorithm was brought in to improve the accuracy of viability classification. The results showed that object-level method was more suitable for the classification of maize kernels viability. The fusion SVM model optimized by WOA coupled with CARS and VCPA-IRIV algorithm achieved the satifactory performance. In general, Raman hperspectral imaging techinique could be used as a poweful alternative for the nondestructive detection of maize kernels aging.
Recent emergence of FTIR spectromicroscopy (micro-FTIR) as a dynamic spectroscopy for imaging to study biological chemistry has opened new possibilities for investigating in situ drug release, redox chemistry effects on biological molecules, DNA and drug interactions, membrane dynamics, and redox reactions with proteins at the single cell level. Micro-FTIR applied to metallodrugs has been playing an important role since the last decade because of its great potential to achieve more robust and controlled pharmacological effects against several diseases, including cancer. An important aspect in the development of these drugs is to understand their cellular properties, such as uptake, accumulation, activity, and toxicity. In this review, we present the potential application of micro-FTIR and its importance for studying metal-based drugs, highlighting the perspectives of chemistry of living cells. We also emphasise bioimaging, which is of high importance to localize the cellular processes, for a proper understanding of the mechanism of action.
Contributions from a workshop organized by The Center for Advanced
Mathematics For Energy Research Applications
The execution and analysis of complex experiments are challenged by the vast dimensionality of the underlying parameter spaces. Although an increase in data-acquisition rates should allow broader querying of the parameter space, the complexity of experiments and the subtle dependence of the model function on input parameters remains daunting owing to the sheer number of variables. New strategies for autonomous data acquisition are being developed, with one promising direction being the use of Gaussian process regression (GPR). GPR is a quick, non-parametric and robust approximation and uncertainty quantification method that can be applied directly to autonomous data acquisition. We review GPR-driven autonomous experimentation and illustrate its functionality using real-world examples from large experimental facilities in the USA and France. We introduce the basics of a GPR-driven autonomous loop with a focus on Gaussian processes, and then shift the focus to the infrastructure that needs to be built around GPR to create a closed loop. Finally, the case studies we discuss show that Gaussian-process-based autonomous data acquisition is a widely applicable method that can facilitate the optimal use of instruments and facilities by enabling the efficient acquisition of high-value datasets.
