To increase the temporal resolution and maximal imaging time of super-resolution (SR) microscopy, we have developed a deconvolution algorithm for structured illumination microscopy based on Hessian matrixes (Hessian-SIM). It uses the continuity of biological structures in multiple dimensions as a priori knowledge to guide image reconstruction and attains artifact-minimized SR images with less than 10% of the photon dose used by conventional SIM while substantially outperforming current algorithms at low signal intensities. Hessian-SIM enables rapid imaging of moving vesicles or loops in the endoplasmic reticulum without motion artifacts and with a spatiotemporal resolution of 88 nm and 188 Hz. Its high sensitivity allows the use of sub-millisecond excitation pulses followed by dark recovery times to reduce photobleaching of fluorescent proteins, enabling hour-long time-lapse SR imaging of actin filaments in live cells. Finally, we observed the structural dynamics of mitochondrial cristae and structures that, to our knowledge, have not been observed previously, such as enlarged fusion pores during vesicle exocytosis.

Today, AI is being increasingly used to help human experts make decisions in high-stakes scenarios. In these scenarios, full automation is often undesirable, not only due to the significance of the outcome, but also because human experts can draw on their domain knowledge complementary to the model's to ensure task success. We refer to these scenarios as AI-assisted decision making, where the individual strengths of the human and the AI come together to optimize the joint decision outcome. A key to their success is to appropriately calibrate human trust in the AI on a case-by-case basis; knowing when to trust or distrust the AI allows the human expert to appropriately apply their knowledge, improving decision outcomes in cases where the model is likely to perform poorly. This research conducts a case study of AI-assisted decision making in which humans and AI have comparable performance alone, and explores whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. Specifically, we study the effect of showing confidence score and local explanation for a particular prediction. Through two human experiments, we show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making, which may also depend on whether the human can bring in enough unique knowledge to complement the AI's errors. We also highlight the problems in using local explanation for AI-assisted decision making scenarios and invite the research community to explore new approaches to explainability for calibrating human trust in AI.

Farnesoid X receptor (FXR) (nuclear receptor subfamily 1, group H, member 4) is a member of nuclear hormone receptor superfamily, which plays essential roles in metabolism of bile acids, lipid, and glucose. We previously showed spontaneously hepatocarcinogenesis in aged FXR(-/-) mice, but its relevance to human hepatocellular carcinoma (HCC) is unclear. Here, we report a systematical analysis of hepatocarcinogenesis in FXR(-/-) mice and FXR expression in human liver cancer. In this study, liver tissues obtained from FXR(-/-) and wild-type mice at different ages were compared by microarray gene profiling, histological staining, chemical analysis, and quantitative real-time PCR. Primary hepatic stellate cells and primary hepatocytes isolated from FXR(-/-) and wild-type mice were also analyzed and compared. The results showed that the altered genes in FXR(-/-) livers were mainly related to metabolism, inflammation, and fibrosis, which suggest that hepatocarcinogenesis in FXR(-/-) mice recapitulated the progression of human liver cancer. Indeed, FXR expression in human HCC was down-regulated compared with normal liver tissues. Furthermore, the proinflammatory cytokines, which were up-regulated in human HCC microenvironment, decreased FXR expression by inhibiting the transactivity of hepatic nuclear factor 1α on FXR gene promoter. Our study thereby demonstrates that the down-regulation of FXR has an important role in human hepatocarcinogenesis and FXR(-/-) mice provide a unique animal model for HCC study.

We report a method to significantly enhance the conductivity of lithium ions in a polymeric lithium salt membrane by introducing functionalized meso/macro-pores to accommodate a mixture of organic solvents in the polymer matrix.

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