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We present reciprocal polarization imaging for the optical activity of chiral media in reflection geometry. The method is based on the reciprocal polar decomposition of backscattering Mueller matrices accounting for the reciprocity of light waves in forward and backward scattering paths. Anisotropic depolarization is introduced to gain sensitivity to optical activity in backscattering. Experiments with glucose solutions show that while the Lu–Chipman decomposition of the backscattering Mueller matrices produces erroneous results, reciprocal polarization imaging correctly retrieves the optical activity of chiral media. The recovered optical rotation agrees with that obtained in the forward geometry and increases linearly with the concentration and thickness of the chiral media. The potential forin vivoglucose monitoring based on optical activity sensing using reciprocal polarization imaging is then discussed.

 

In many oceanic regions, anthropogenic warming will coincide with iron (Fe) limitation. Interactive effects between warming and Fe limitation on phytoplankton physiology and biochemical function are likely, as temperature and Fe availability affect many of the same essential cellular pathways. However, we lack a clear understanding of how globally significant phytoplankton such as the picocyanobacteriaSynechococcuswill respond to these co-occurring stressors, and what underlying molecular mechanisms will drive this response. Moreover, ecotype-specific adaptations can lead to nuanced differences in responses between strains. In this study,Synechococcusisolates YX04-1 (oceanic) and XM-24 (coastal) from the South China Sea were acclimated to Fe limitation at two temperatures, and their physiological and proteomic responses were compared. Both strains exhibited reduced growth due to warming and Fe limitation. However, coastal XM-24 maintained relatively higher growth rates in response to warming under replete Fe, while its growth was notably more compromised under Fe limitation at both temperatures compared with YX04-1. In response to concurrent heat and Fe stress, oceanic YX04-1 was better able to adjust its photosynthetic proteins and minimize the generation of reactive oxygen species while reducing proteome Fe demand. Its intricate proteomic response likely enabled oceanic YX04-1 to mitigate some of the negative impact of warming on its growth during Fe limitation. Our study highlights how ecologically-shaped adaptations inSynechococcusstrains even from proximate oceanic regions can lead to differing physiological and proteomic responses to these climate stressors.

 

We present single-shot high-performance quantitative phase imaging with a physics-inspired plug-and-play denoiser for polarization differential interference contrast (PDIC) microscopy. The quantitative phase is recovered by the alternating direction method of multipliers (ADMM), balancing total variance regularization and a pre-trained dense residual U-net (DRUNet) denoiser. The custom DRUNet uses the Tanh activation function to guarantee the symmetry requirement for phase retrieval. In addition, we introduce an adaptive strategy accelerating convergence and explicitly incorporating measurement noise. After validating this deep denoiser-enhanced PDIC microscopy on simulated data and phantom experiments, we demonstrated high-performance phase imaging of histological tissue sections. The phase retrieval by the denoiser-enhanced PDIC microscopy achieves significantly higher quality and accuracy than the solution based on Fourier transforms or the iterative solution with total variance regularization alone.

 

Cryoelectron tomography directly visualizes heterogeneous macromolecular structures in their native and complex cellular environments. However, existing computer-assisted structure sorting approaches are low throughput or inherently limited due to their dependency on available templates and manual labels. Here, we introduce a high-throughput template-and-label-free deep learning approach, Deep Iterative Subtomogram Clustering Approach (DISCA), that automatically detects subsets of homogeneous structures by learning and modeling 3D structural features and their distributions. Evaluation on five experimental cryo-ET datasets shows that an unsupervised deep learning based method can detect diverse structures with a wide range of molecular sizes. This unsupervised detection paves the way for systematic unbiased recognition of macromolecular complexes in situ. 

In this work, we address the problem of detecting anomalies in a certain laboratory automation setting. At first, we collect video images of liquid transfer in automated laboratory experiments. We mimic the real-world challenges of developing an anomaly detection model by considering two points. First, the size of the collected dataset is set to be relatively small compared to large-scale video datasets. Second, the dataset has a class imbalance problem where the majority of the collected videos are from abnormal events. Consequently, the existing learning-based video anomaly detection methods do not perform well. To this end, we develop a practical human-engineered feature extraction method to detect anomalies from the liquid transfer video images. Our simple yet effective method outperforms state-of-the-art anomaly detection methods with a notable margin. In particular, the proposed method provides 19% and 76% average improvement in AUC and Equal Error Rate, respectively. Our method also quantifies the anomalies and provides significant benefits for deployment in the real-world experimental setting. 

In many real-life image analysis applications, particularly in biomedical research domains, the objects of interest undergo multiple transformations that alter their visual properties while keeping the semantic content unchanged. Disentangling images into semantic content factors and transformations can provide significant benefits into many domain-specific image analysis tasks. To this end, we propose a generic unsupervised framework, Harmony, that simultaneously and explicitly disentangles semantic content from multiple parameterized transformations. Harmony leverages a simple cross-contrastive learning framework with multiple explicitly parameterized latent representations to disentangle content from transformations. To demonstrate the efficacy of Harmony, we apply it to disentangle image semantic content from several parameterized transformations (rotation, translation, scaling, and contrast). Harmony achieves significantly improved disentanglement over the baseline models on several image datasets of diverse domains. With such disentanglement, Harmony is demonstrated to incentivize bioimage analysis research by modeling structural heterogeneity of macromolecules from cryo-ET images and learning transformation-invariant representations of protein particles from single-particle cryo-EM images. Harmony also performs very well in disentangling content from 3D transformations and can perform coarse and fast alignment of 3D cryo-ET subtomograms. Therefore, Harmony is generalizable to many other imaging domains and can potentially be extended to domains beyond imaging as well.