RIP1-dependent linear and nonlinear recruitments of caspase-8 and RIP3 respectively to necrosome specify distinct cell death outcomes

There remains a significant gap in our quantitative understanding of crosstalk between apoptosis and necroptosis pathways. By employing the SWATH-MS technique, we quantified absolute amounts of up to thousands of proteins in dynamic assembling/de-assembling of TNF signaling complexes. Combining SWATH-MS-based network modeling and experimental validation, we found that when RIP1 level is below ~1000 molecules/cell (mpc), the cell solely undergoes TRADD-dependent apoptosis. When RIP1 is above ~1000 mpc, pro-caspase-8 and RIP3 are recruited to necrosome respectively with linear and nonlinear dependence on RIP1 amount, which well explains the co-occurrence of apoptosis and necroptosis and the paradoxical observations that RIP1 is required for necroptosis but its increase down-regulates necroptosis. Higher amount of RIP1 (>~46,000 mpc) suppresses apoptosis, leading to necroptosis alone. The relation between RIP1 level and occurrence of necroptosis or total cell death is biphasic. Our study provides a resource for encoding the complexity of TNF signaling and a quantitative picture how distinct dynamic interplay among proteins function as basis sets in signaling complexes, enabling RIP1 to play diverse roles in governing cell fate decisions.

Application of facial expression recognition technology in diagnosis and treatment of psychiatry / 中华行为医学与脑科学杂志

In psychiatry, observation of the patients is often an important basis for making a diagnosis during clinical practice. However, changes in emotional facial expressions are often subtle and difficult to detect. For this reason, automated facial expression recognition can be used to assist in identifying mental disorders. Facial expression is one of the important ways of emotional expression, and strong similarities of basic human facial expression are not affected by cultural background or congenital blindness. With the development of computer science, facial expression recognition methods are also constantly improving. Among them, deep-learning-based facial expression recognition approaches, with their powerful information processing capabilities, highly reduce the dependence on face-physics-based models and other pre-processing techniques by using trainable feature extraction models to automatically learn representations from images and videos. This article focuses on the progress of facial expression recognition system in the diagnosis and treatment of schizophrenia, depression, borderline personality disorder, autism spectrum disorder and other diseases. This article also explores the application of facial expression recognition technology in the field of psychiatry and remote psychology intervention.