Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification

Measurement of blood oxygen saturation (sO2) by optical imaging oximetry provides invaluable insight into local tissue functions and metabolism. Despite different embodiments and modalities, all label-free optical-imaging oximetry techniques utilize the same principle of sO2-dependent spectral contrast from haemoglobin. Traditional approaches for quantifying sO2 often rely on analytical models that are fitted by the spectral measurements. These approaches in practice suffer from uncertainties due to biological variability, tissue geometry, light scattering, systemic spectral bias, and variations in the experimental conditions. Here, we propose a new data-driven approach, termed deep spectral learning (DSL), to achieve oximetry that is highly robust to experimental variations and, more importantly, able to provide uncertainty quantification for each sO2 prediction. To demonstrate the robustness and generalizability of DSL, we analyse data from two visible light optical coherence tomography (vis-OCT) setups across two separate in vivo experiments on rat retinas. Predictions made by DSL are highly adaptive to experimental variabilities as well as the depth-dependent backscattering spectra. Two neural-network-based models are tested and compared with the traditional least-squares fitting (LSF) method. The DSL-predicted sO2 shows significantly lower mean-square errors than those of the LSF. For the first time, we have demonstrated en face maps of retinal oximetry along with a pixel-wise confidence assessment. Our DSL overcomes several limitations of traditional approaches and provides a more flexible, robust, and reliable deep learning approach for in vivo non-invasive label-free optical oximetry.R01 CA224911 - NCI NIH HHS; R01 CA232015 - NCI NIH HHS; R01 NS108464 - NINDS NIH HHS; R21 EY029412 - NEI NIH HHSAccepted manuscrip

Illumination coding meets uncertainty learning: toward reliable AI-augmented phase imaging

We propose a physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging. We design an asymmetric coded illumination scheme to encode high-resolution phase information across a wide field-of-view. We then develop a matching DL algorithm to provide large-SBP phase estimation. We show that this illumination coding scheme is highly scalable in achieving flexible resolution, and robust to experimental variations. We demonstrate this technique on both static and dynamic biological samples, and show that it can reliably achieve 5X resolution enhancement across 4X FOVs using only five multiplexed measurements -- more than 10X data reduction over the state-of-the-art. Typical DL algorithms tend to provide over-confident predictions, whose errors are only discovered in hindsight. We develop an uncertainty learning framework to overcome this limitation and provide predictive assessment to the reliability of the DL prediction. We show that the predicted uncertainty maps can be used as a surrogate to the true error. We validate the robustness of our technique by analyzing the model uncertainty. We quantify the effect of noise, model errors, incomplete training data, and "out-of-distribution" testing data by assessing the data uncertainty. We further demonstrate that the predicted credibility maps allow identifying spatially and temporally rare biological events. Our technique enables scalable AI-augmented large-SBP phase imaging with dependable predictions.Published versio

Scalable and reliable deep learning for computational microscopy in complex media

Emerging deep learning based computational microscopy techniques promise novel imaging capabilities beyond traditional techniques. In this talk, I will discuss two microscopy applications. First, high space-bandwidth product microscopy typically requires a large number of measurements. I will present a novel physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging [1], enabling significant reduction of the required measurements, opening up real-time applications. In this technique, we design asymmetric coded illumination patterns to encode high-resolution phase information across a wide field-of-view. We then develop a matching DL algorithm to provide large-SBP phase estimation. We demonstrate this technique on both static and dynamic biological samples, and show that it can reliably achieve 5× resolution enhancement across 4× FOVs using only five multiplexed measurements. In addition, we develop an uncertainty learning framework to provide predictive assessment to the reliability of the DL prediction. We show that the predicted uncertainty maps can be used as a surrogate to the true error. We validate the robustness of our technique by analyzing the model uncertainty. We quantify the effect of noise, model errors, incomplete training data, and “out-of-distribution” testing data by assessing the data uncertainty. We further demonstrate that the predicted credibility maps allow identifying spatially and temporally rare biological events. Our technique enables scalable DL-augmented large-SBP phase imaging with reliable predictions and uncertainty quantifications. Second, I will turn to the pervasive problem of imaging in scattering media. I will discuss a new deep learning- based technique that is highly generalizable and resilient to statistical variations of the scattering media [2]. We develop a statistical ‘one-to-all’ deep learning technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media. REFERENCES [1] Xue, Y., Cheng, S., Li, Y., and Tian, L., “Illumination coding meets uncertainty learning: toward reliable ai-augmented phase imaging,” arXiv:1901.02038 (2019). [2] Li, Y., Xue, Y., and Tian, L., “Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media,” Optica 5, 1181 (2018)

Interaction of cellulase with three phenolic acids

The activity of cellulase against filter paper was enhanced by 28.32% and 15.17% after the addition of 0.83 mg/ml of ferulic acid and p-coumaric acid, respectively, and by 10.15% after the addition of salicylic acid at 0.67 mg/ml. The effects of three phenolic acids on the structure of cellulase were investigated via ultraviolet spectrophotometry, fluorescence spectroscopy, and circular dichroism (CD) spectroscopy. Ultraviolet spectroscopic results indicated that the peak absorbance of cellulase significantly increased and exhibited a 4–5 nm redshift after the addition of the three phenolic acids, suggesting that the phenolic acids strongly interacted with the enzyme. Fluorescence investigation of the interaction between the enzyme and the phenolic acids showed that ferulic acid and p-coumaric acid covalently reacted with the aromatic amino acid residues in cellulase, whereas salicylic acid interacted non-covalently with cellulase. CD analysis revealed that the addition of the phenolic acids significantly decreased α-helix content but increased β-sheet and random coil contents. The possible mechanism underlying the effects of these phenolic acids on cellulase activity was also discussed.</p

Reliable deep-learning-based phase imaging with uncertainty quantification: supplementary material

Emerging deep-learning (DL)-based techniques have significant potential to revolutionize biomedical imaging. However, one outstanding challenge is the lack of reliability assessment in the DL predictions, whose errors are commonly revealed only in hindsight. Here, we propose a new Bayesian convolutional neural network (BNN)-based framework that overcomes this issue by quantifying the uncertainty of DL predictions. Foremost, we show that BNN-predicted uncertainty maps provide surrogate estimates of the true error from the network model and measurement itself. The uncertainty maps characterize imperfections often unknown in real-world applications, such as noise, model error, incomplete training data, and out-of-distribution testing data. Quantifying this uncertainty provides a per-pixel estimate of the confidence level of the DL prediction as well as the quality of the model and data set. We demonstrate this framework in the application of large space–bandwidth product phase imaging using a physics-guided coded illumination scheme. From only five multiplexed illumination measurements, our BNN predicts gigapixel phase images in both static and dynamic biological samples with quantitative credibility assessment. Furthermore, we show that low-certainty regions can identify spatially and temporally rare biological phenomena. We believe our uncertainty learning framework is widely applicable to many DL-based biomedical imaging techniques for assessing the reliability of DL predictions.https://www.osapublishing.org/optica/fulltext.cfm?uri=optica-7-4-332&id=429860Published versio

Type II taste cells participate in mucosal immune surveillance

The oral microbiome is second only to its intestinal counterpart in diversity and abundance but its effects on taste cells remains largely unexplored. Using single-cell RNASeq, we found that mouse taste cells, in particular, sweet and umami receptor cells that express taste 1 receptor member 3 (Tas1r3), have a gene expression signature reminiscent of Microfold (M) cells, a central player in immune surveillance in the mucosa-associated lymphoid tissue (MALT) such as those in the Peyer’s patch and tonsils. Administration of tumor necrosis factor ligand superfamily member 11 (TNFSF11; also known as RANKL), a growth factor required for differentiation of M cells, dramatically increased M cell proliferation and marker gene expression in the taste papillae and in cultured taste organoids from wild-type (WT) mice. Taste papillae and organoids from knockout mice lacking Spib (SpibKO), a RANKL-regulated transcription factor required for M cell development and regeneration on the other hand, failed to respond to RANKL. Taste papillae from SpibKO mice also showed reduced expression of NF-κB signaling pathway components and proinflammatory cytokines and attracted fewer immune cells. However, lipopolysaccharide-induced expression of cytokines was strongly up-regulated in SpibKO mice compared to their WT counterparts. Like M cells, taste cells from WT but not SpibKO mice readily took up fluorescently labeled microbeads, a proxy for microbial transcytosis. The proportion of taste cell subtypes are unaltered in SpibKO mice; however, they displayed increased attraction to sweet and umami taste stimuli. We propose that taste cells are involved in immune surveillance and may tune their taste responses to microbial signaling and infection

Displacement-agnostic coherent imaging through scatter with an interpretable deep neural network

Coherent imaging through scatter is a challenging task in computational imaging. Both model-based and data-driven approaches have been explored to solve the inverse scattering problem. In our previous work, we have shown that a deep learning approach can make high-quality and highly generalizable predictions through unseen diffusers. Here, we propose a new deep neural network (DNN) model that is agnostic to a broader class of perturbations including scatterer change, displacements, and system defocus up to 10X depth of field. In addition, we develop a new analysis framework for interpreting the mechanism of our DNN model and visualizing its generalizability based on an unsupervised dimension reduction technique. We show that our DNN can unmix the scattering-specific information and extract the object-specific information so as to achieve generalization under different scattering conditions. Our work paves the way to a highly robust and interpretable deep learning approach to imaging through scattering media.Accepted manuscrip

Preparation of octacosanol from filter mud produced after sugarcane juice clarification

Filter mud from sugarcane juice clarification containing 6.85 g/100 g waxes was used for octacosanol extraction by supercritical CO2 and hot ethanol reflux method, respectively. Comparing with hot ethanol reflux extraction, supercritical CO2 extraction provided a similar yield of waxes but a higher content of octacosanol in the waxes (29.65 g/100 g vs. 22.52 g/100 g). However, saponification of the waxes extracted by hot ethanol reflux extraction has significantly increased octacosanol content to 47.8 g/100 g. For high efficient preparation of octacosanol from filter mud, hot ethanol reflux extraction of waxes followed by saponification was the method of choice.</p

Ice-nucleating particles from multiple aerosol sources in the urban environment of Beijing under mixed-phase cloud conditions

Ice crystals occurring in mixed-phase clouds play a vital role in global precipitation and energy balance because of the unstable equilibrium between coexistent liquid droplets and ice crystals, which affects cloud lifetime and radiative properties, as well as precipitation formation. Satellite observations proved that immersion freezing, i.e., ice formation on particles immersed within aqueous droplets, is the dominant ice nucleation (IN) pathway in mixed-phase clouds. However, the impact of anthropogenic emissions on atmospheric IN in the urban environment remains ambiguous. In this study, we present in situ observations of ambient ice-nucleating particle number concentration (NINP) measured at mixed-phase cloud conditions (−30 ∘C, relative humidity with respect to liquid water RHw= 104 %) and the physicochemical properties of ambient aerosol, including chemical composition and size distribution, at an urban site in Beijing during the traditional Chinese Spring Festival. The impact of multiple aerosol sources such as firework emissions, local traffic emissions, mineral dust, and urban secondary aerosols on NINP is investigated. The results show that NINP during the dust event reaches up to 160 # L−1 (where “#” represents number of particles), with an activation fraction (AF) of 0.0036 % ± 0.0011 %. During the rest of the observation, NINP is on the order of 10−1 to 10 # L−1, with an average AF between 0.0001 % and 0.0002 %. No obvious dependence of NINP on the number concentration of particles larger than 500 nm (N500) or black carbon (BC) mass concentration (mBC) is found throughout the field observation. The results indicate a substantial NINP increase during the dust event, although the observation took place at an urban site with high background aerosol concentration. Meanwhile, the presence of atmospheric BC from firework and traffic emissions, along with urban aerosols formed via secondary transformation during heavily polluted periods, does not influence the observed INP concentration. Our study corroborates previous laboratory and field findings that anthropogenic BC emission has a negligible effect on NINP and that NINP is unaffected by heavy pollution in the urban environment under mixed-phase cloud conditions.</p