G protein–coupled receptors (GPCRs) are important therapeutic targets that exhibit functional selectivity (biased signaling), in which different ligands or receptor variants elicit distinct downstream signaling. Understanding all the signaling events and biases that contribute to both the beneficial and adverse effects of GPCR stimulation by given ligands is important for drug discovery. Here, we report the design, validation, and use of pathway-selective bioluminescence resonance energy transfer (BRET) biosensors that monitor the engagement and activation of signaling effectors downstream of G proteins, including protein kinase C (PKC), phospholipase C (PLC), p63RhoGEF, and Rho. Combined with G protein and β-arrestin BRET biosensors, our sensors enabled real-time monitoring of GPCR signaling at different levels in downstream pathways in both native and engineered cells. Profiling of the responses to 14 angiotensin II (AngII) type 1 receptor (AT1R) ligands enabled the clustering of compounds into different subfamilies of biased ligands and showed that, in addition to the previously reported functional selectivity between Gαq and β-arrestin, there are also biases among G protein subtypes. We also demonstrated that biases observed at the receptor and G protein levels propagated to downstream signaling pathways and that these biases could occur through the engagement of different G proteins to activate a common effector. We also used these tools to determine how naturally occurring AT1R variants affected signaling bias. This suite of BRET biosensors provides a useful resource for fingerprinting biased ligands and mutant receptors and for dissecting functional selectivity at various levels of GPCR signaling.

Bioactive ceramics have received great attention in the past decades owing to their success in stimulating cell proliferation, differentiation and bone tissue regeneration. They can react and form chemical bonds with cells and tissues in human body. This paper provides a comprehensive review of the application of bioactive ceramics for bone repair and regeneration. The review systematically summarizes the types and characters of bioactive ceramics, the fabrication methods for nanostructure and hierarchically porous structure, typical toughness methods for ceramic scaffold and corresponding mechanisms such as fiber toughness, whisker toughness and particle toughness. Moreover, greater insights into the mechanisms of interaction between ceramics and cells are provided, as well as the development of ceramic-based composite materials. The development and challenges of bioactive ceramics are also discussed from the perspective of bone repair and regeneration.

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β‐arrestins (βarrs) are key regulators of G protein‐coupled receptor (GPCR) signaling and trafficking, and their knockdown typically leads to a decrease in agonist‐induced ERK1/2 MAP kinase activation. Interestingly, for some GPCRs, knockdown of βarr1 augments agonist‐induced ERK1/2 phosphorylation although a mechanistic basis for this intriguing phenomenon is unclear. Here, we use selected GPCRs to explore a possible correlation between the spatial positioning of receptor phosphorylation sites and the contribution of βarr1 in ERK1/2 activation. We discover that engineering a spatially positioned double‐phosphorylation‐site cluster in the bradykinin receptor (B2R), analogous to that present in the vasopressin receptor (V2R), reverses the contribution of βarr1 in ERK1/2 activation from inhibitory to promotive. An intrabody sensor suggests a conformational mechanism for this role reversal of βarr1, and molecular dynamics simulation reveals a bifurcated salt bridge between this double‐phosphorylation site cluster and Lys294 in the lariat loop of βarr1, which directs the orientation of the lariat loop. Our findings provide novel insights into the opposite roles of βarr1 in ERK1/2 activation for different GPCRs with a direct relevance to biased agonism and novel therapeutics.

We show that the image representations in a deep neural network (DNN) can be manipulated to mimic those of other natural images, with only minor, imperceptible perturbations to the original image. Previous methods for generating adversarial images focused on image perturbations designed to produce erroneous class labels. Here we instead concentrate on the internal layers of DNN representations, to produce a new class of adversarial images that differs qualitatively from others. While the adversary is perceptually similar to one image, its internal representation appears remarkably similar to a different image, from a different class and bearing little if any apparent similarity to the input. Further, they appear generic and consistent with the space of natural images. This phenomenon demonstrates the possibility to trick a DNN to confound almost any image with any other chosen image, and raises questions about DNN representations, as well as the properties of natural images themselves. INTRODUCTIONRecent papers have shown that deep neural networks (DNNs) for image classification can be fooled, often using relatively simple methods to generate so-called adversarial images (