Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Learning classifier systems (LCSs) belong to a class of algorithms based on the principle of self-organization and have frequently been applied to the task of solving mazes, an important type of reinforcement learning (RL) problem. Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world applications related to the problem of navigation. However, the best achievements of LCSs in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. We construct a new LCS agent that has a simpler and more transparent performance mechanism, but that can still solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. To improve our understanding of the nature and structure of maze environments, we analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics, and develop a set of new maze environments. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well as, and in some cases better than, other published systems

Caspase-8 and c-FLIPL associate in lipid rafts with NF-kappaB adaptors during T cell activation.

Humans and mice lacking functional caspase-8 in T cells manifest a profound immunodeficiency syndrome due to defective T cell antigen receptor (TCR)-induced NF-kappaB signaling and proliferation. It is unknown how caspase-8 is activated following T cell stimulation, and what is the caspase-8 substrate(s) that is necessary to initiate T cell cycling. We observe that following TCR ligation, a small portion of total cellular caspase-8 and c-FLIP(L) rapidly migrate to lipid rafts where they associate in an active caspase complex. Activation of caspase-8 in lipid rafts is followed by rapid cleavage of c-FLIP(L) at a known caspase-8 cleavage site. The active caspase.c-FLIP complex forms in the absence of Fas (CD95/APO1) and associates with the NF-kappaB signaling molecules RIP1, TRAF2, and TRAF6, as well as upstream NF-kappaB regulators PKC theta, CARMA1, Bcl-10, and MALT1, which connect to the TCR. The lack of caspase-8 results in the absence of MALT1 and Bcl-10 in the active caspase complex. Consistent with this observation, inhibition of caspase activity attenuates NF-kappaB activation. The current findings define a link among TCR, caspases, and the NF-kappaB pathway that occurs in a sequestered lipid raft environment in T cells

PKCθ Signals Activation versus Tolerance In Vivo

Understanding the pathways that signal T cell tolerance versus activation is key to regulating immunity. Previous studies have linked CD28 and protein kinase C-θ (PKCθ) as a potential signaling pathway that influences T cell activation. Therefore, we have compared the responses of T cells deficient for CD28 and PKCθ in vivo and in vitro. Here, we demonstrate that the absence of PKCθ leads to the induction of T cell anergy, with a phenotype that is comparable to the absence of CD28. Further experiments examined whether PKCθ triggered other CD28-dependent responses. Our data show that CD4 T cell–B cell cooperation is dependent on CD28 but not PKCθ, whereas CD28 costimulatory signals that augment proliferation can be uncoupled from signals that regulate anergy. Therefore, PKCθ relays a defined subset of CD28 signals during T cell activation and is critical for the induction of activation versus tolerance in vivo

The mixed problem for the Laplacian in Lipschitz domains

We consider the mixed boundary value problem or Zaremba's problem for the Laplacian in a bounded Lipschitz domain in R^n. We specify Dirichlet data on part of the boundary and Neumann data on the remainder of the boundary. We assume that the boundary between the sets where we specify Dirichlet and Neumann data is a Lipschitz surface. We require that the Neumann data is in L^p and the Dirichlet data is in the Sobolev space of functions having one derivative in L^p for some p near 1. Under these conditions, there is a unique solution to the mixed problem with the non-tangential maximal function of the gradient of the solution in L^p of the boundary. We also obtain results with data from Hardy spaces when p=1.Comment: Version 5 includes a correction to one step of the main proof. Since the paper appeared long ago, this submission includes the complete paper, followed by a short section that gives the correction to one step in the proo

CXCR7 influences leukocyte entry into the CNS parenchyma by controlling abluminal CXCL12 abundance during autoimmunity

During CNS autoimmunity, brain endothelial cell CXCR7 internalizes CXCL12 from the perivascular space, thereby permitting leukocyte migration into the CNS parenchyma

Development and Application of Single Cell Multi-omics Methods for Complex Disease

Complex diseases such as Alzheimer’s disease are driven by molecular changes in many cell types in different tissues. Recent advances in scRNAseq and spatial transcriptomics provide tools to determine cell type specific effects in individual tissues resulting from genetic and environmental perturbations. Properly interpreting these data require computational tools and biologically rooted analyses to identify key mechanisms underlying complex diseases. Here we design, develop, and apply computational methods for integrating scRNAseq and spatial transcriptomics data to identify mechanisms underlying pathogenesis of disease and potential therapeutics. First, we designed a deep learning approach, JSTA, for integrating scRNAseq and spatial transcriptome data from multiplexed FISH for cell segmentation and cell type annotation, revealing spatially distributed cell subtypes and spatially differentially expressed genes in the mouse hippocampus. Next, we developed a gradient-boosting machine based approach, SCING, for identifying cell type specific gene regulatory networks (GRN) using scRNAseq and spatial transcriptomics data. This tool provides GRN subnetworks annotated with biological pathways for associating subnetwork expression with disease phenotypes and spatial domains. We applied these and other existing tools to scRNAseq and spatial transcriptomics datasets to understand the mechanism underlying diverse types of diseases or physiological traits, including Alzheimer’s disease and heart innervating neurons and satellite glial cells in the stellate ganglion in the context of dilated cardiomyopathy. Our studies established new computational tools applicable to diverse types of single cell omics data and revealed biological insights to complex diseases

SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics

Summary: Gene regulatory network (GRN) inference is an integral part of understanding physiology and disease. Single cell/nuclei RNA-seq (scRNA-seq/snRNA-seq) data has been used to elucidate cell-type GRNs; however, the accuracy and speed of current scRNAseq-based GRN approaches are suboptimal. Here, we present Single Cell INtegrative Gene regulatory network inference (SCING), a gradient boosting and mutual information-based approach for identifying robust GRNs from scRNA-seq, snRNA-seq, and spatial transcriptomics data. Performance evaluation using Perturb-seq datasets, held-out data, and the mouse cell atlas combined with the DisGeNET database demonstrates the improved accuracy and biological interpretability of SCING compared to existing methods. We applied SCING to the entire mouse single cell atlas, human Alzheimer’s disease (AD), and mouse AD spatial transcriptomics. SCING GRNs reveal unique disease subnetwork modeling capabilities, have intrinsic capacity to correct for batch effects, retrieve disease relevant genes and pathways, and are informative on spatial specificity of disease pathogenesis