Macrophages are important mediators of inflammation and tissue remodeling. Recent insights into the heterogeneity of macrophage subpopulations have renewed interest in their functional diversity in homeostasis and disease. In addition, their plasticity enables them to perform a variety of functions in response to changing tissue contexts, such as those imposed by aging. These qualities make macrophages particularly intriguing cells given their dichotomous role in protecting against, or accelerating, diseases of the cardiovascular system and the eye, two tissues that are particularly susceptible to the effects of aging. We review novel perspectives on macrophage biology, as informed by recent studies detailing the diversity of macrophage identity and function, as well as mechanisms influencing macrophage behavior that might offer opportunities for new therapeutic strategies. New Insights into Macrophage Lineage, Function, and Plasticity in Health and Disease Highlights Recent studies combining single-cell transcriptomics, imaging, and functional assays provide mechanistic validation of prior observations indicating that murine macrophage heterogeneity contributes to diverse roles in healthy tissue and during disease response.

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In this paper we propose a deep neural network model with an encoder-decoder architecture that translates images of math formulas into their LaTeX markup sequences. The encoder is a convolutional neural network (CNN) that transforms images into a group of feature maps. To better capture the spatial relationships of math symbols, the feature maps are augmented with 2D positional encoding before being unfolded into a vector. The decoder is a stacked bidirectional long short-term memory (LSTM) model integrated with the soft attention mechanism, which works as a language model to translate the encoder output into a sequence of LaTeX tokens. The neural network is trained in two steps. The first step is token-level training using the Maximum-Likelihood Estimation (MLE) as the objective function. At completion of the token-level training, the sequence-level training objective function is employed to optimize the overall model based on the policy gradient algorithm from reinforcement learning. Our design also overcomes the exposure bias problem by closing the feedback loop in the decoder during sequence-level training, i.e., feeding in the predicted token instead of the ground truth token at every time step. The model is trained and evaluated on the IM2LATEX-100K dataset and shows state-of-the-art performance on both sequence-based and imagebased evaluation metrics.

This paper presents an approach to use commercial videogames for biofeedback training. It consists of intercepting signals from the game controller and adapting them in real-time based on physiological measurements from the player. We present three sample implementations and a case study for teaching stress self-regulation via an immersive car racing game. We use a crossover gaming device to manipulate controller signals, and a respiratory sensor to monitor the players' breathing rate. We then alter the speed of the car to encourage slow deep breathing, in this way, allowing players to reduce their arousal while playing the game. We evaluate the approach against an alternative form of biofeedback that uses a graphic overlay to convey physiological information, and a control condition (playing the game without biofeedback). Experimental results show that our approach can promote deep breathing during gameplay, and also during a subsequent task, once biofeedback is removed. Our results also indicate that delivering biofeedback through subtle changes in gameplay can be as effective as delivering them directly through a visual display. These results open the possibility to develop low-cost and engaging biofeedback interventions using a variety of commercial videogames to promote adherence.