The role of TREM proteins in lung homeostasis and inflammation

The family of Triggering Receptors Expressed on Myeloid Cells (TREM) contain novel activating receptors of the Ig super-family that are expressed on myeloid cells. TREM-1 is a transmembrane glycoprotein expressed on blood neutrophils and a subset of monocytes, but not on lymphocytes or other cell types and is upregulated by bacterial and fungal products. TREM-1 signaling potentiates the outcome of Toll-like Receptor (TLR) signaling. Blockade of TREM-1 prevents experimentally induced septic shock. In addition to the membrane-bound form, a soluble TREM-1 molecule (sTREM-1) exists that regulates membrane bound TREM-1 by competing against the, as yet, unknown natural TREM-1 ligand. sTREM-1 is detected at high levels during bacterial infection and asthma and is used as a predictive biomarker for severe inflammation. TREM-2 on, the other hand is not known to be secreted and the membrane bound form is reported to prevent TLR signaling and promote osteoclastogenesis. The expression and function of TREM proteins during respiratory viral infection is not currently known. In this thesis we investigate the hypothesis that TREM-1 signaling contributes to excessive cytokine production during pulmonary viral infection in a murine model and that soluble TREM-1 and membrane bound TREM-2 proteins are anti-inflammatory. We show, for the first time, the following novel mechanisms that significantly increase our understanding of this important receptor family: 1) TREM-1 is highly up-regulated during influenza and the subsequent release of sTREM-1 likely contributes to secondary bacterial super-infection, 2) Blockade of TREM-1 at the onset of viral infection significantly reduces the risk of secondary bacterial pneumonia and 3) TREM-2 expressing macrophages that appear during the resolution of influenza-induced inflammation display a regulatory phenotype that can reduce TLR responsiveness of inflammatory macrophages. Taken together, our data suggests that TREM-1 proteins represent a novel therapeutic target for the alleviation of influenza-induced pathology and that TREM-2 expression identifies a novel population of regulatory macrophages

Collaborative Deep Reinforcement Learning for Joint Object Search

We examine the problem of joint top-down active search of multiple objects under interaction, e.g., person riding a bicycle, cups held by the table, etc.. Such objects under interaction often can provide contextual cues to each other to facilitate more efficient search. By treating each detector as an agent, we present the first collaborative multi-agent deep reinforcement learning algorithm to learn the optimal policy for joint active object localization, which effectively exploits such beneficial contextual information. We learn inter-agent communication through cross connections with gates between the Q-networks, which is facilitated by a novel multi-agent deep Q-learning algorithm with joint exploitation sampling. We verify our proposed method on multiple object detection benchmarks. Not only does our model help to improve the performance of state-of-the-art active localization models, it also reveals interesting co-detection patterns that are intuitively interpretable