Central nervous system immune interactome is a function of cancer lineage, tumor microenvironment, and STAT3 expression.

BACKGROUNDImmune cell profiling of primary and metastatic CNS tumors has been focused on the tumor, not the tumor microenvironment (TME), or has been analyzed via biopsies.METHODSEn bloc resections of gliomas (n = 10) and lung metastases (n = 10) were analyzed via tissue segmentation and high-dimension Opal 7-color multiplex imaging. Single-cell RNA analyses were used to infer immune cell functionality.RESULTSWithin gliomas, T cells were localized in the infiltrating edge and perivascular space of tumors, while residing mostly in the stroma of metastatic tumors. CD163+ macrophages were evident throughout the TME of metastatic tumors, whereas in gliomas, CD68+, CD11c+CD68+, and CD11c+CD68+CD163+ cell subtypes were commonly observed. In lung metastases, T cells interacted with CD163+ macrophages as dyads and clusters at the brain-tumor interface and within the tumor itself and as clusters within the necrotic core. In contrast, gliomas typically lacked dyad and cluster interactions, except for T cell CD68+ cell dyads within the tumor. Analysis of transcriptomic data in glioblastomas revealed that innate immune cells expressed both proinflammatory and immunosuppressive gene signatures.CONCLUSIONOur results show that immunosuppressive macrophages are abundant within the TME and that the immune cell interactome between cancer lineages is distinct. Further, these data provide information for evaluating the role of different immune cell populations in brain tumor growth and therapeutic responses.FUNDINGThis study was supported by the NIH (NS120547), a Developmental research project award (P50CA221747), ReMission Alliance, institutional funding from Northwestern University and the Lurie Comprehensive Cancer Center, and gifts from the Mosky family and Perry McKay. Performed in the Flow Cytometry & Cellular Imaging Core Facility at MD Anderson Cancer Center, this study received support in part from the NIH (CA016672) and the National Cancer Institute (NCI) Research Specialist award 1 (R50 CA243707). Additional support was provided by CCSG Bioinformatics Shared Resource 5 (P30 CA046592), a gift from Agilent Technologies, a Research Scholar Grant from the American Cancer Society (RSG-16-005-01), a Precision Health Investigator Award from University of Michigan (U-M) Precision Health, the NCI (R37-CA214955), startup institutional research funds from U-M, and a Biomedical Informatics & Data Science Training Grant (T32GM141746)

Competing Sound Sources Reveal Spatial Effects in Cortical Processing

Neurons in the avian auditory forebrain show strong sensitivity to the spatial configuration of two competing sources, even though there is only weak spatial dependence for any single source

Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-Fire Neurons

The modulation of the sensitivity, or gain, of neural responses to input is an important component of neural computation. It has been shown that divisive gain modulation of neural responses can result from a stochastic shunting from balanced (mixed excitation and inhibition) background activity. This gain control scheme was developed and explored with static inputs, where the membrane and spike train statistics were stationary in time. However, input statistics, such as the firing rates of pre-synaptic neurons, are often dynamic, varying on timescales comparable to typical membrane time constants. Using a population density approach for integrate-and-fire neurons with dynamic and temporally rich inputs, we find that the same fluctuation-induced divisive gain modulation is operative for dynamic inputs driving nonequilibrium responses. Moreover, the degree of divisive scaling of the dynamic response is quantitatively the same as the steady-state responses—thus, gain modulation via balanced conductance fluctuations generalizes in a straight-forward way to a dynamic setting

Responses of Tectal Neurons to Contrasting Stimuli: An Electrophysiological Study in the Barn Owl

The saliency of visual objects is based on the center to background contrast. Particularly objects differing in one feature from the background may be perceived as more salient. It is not clear to what extent this so called “pop-out” effect observed in humans and primates governs saliency perception in non-primates as well. In this study we searched for neural-correlates of pop-out perception in neurons located in the optic tectum of the barn owl. We measured the responses of tectal neurons to stimuli appearing within the visual receptive field, embedded in a large array of additional stimuli (the background). Responses were compared between contrasting and uniform conditions. In a contrasting condition the center was different from the background while in the uniform condition it was identical to the background. Most tectal neurons responded better to stimuli in the contrsating condition compared to the uniform condition when the contrast between center and background was the direction of motion but not when it was the orientation of a bar. Tectal neurons also preferred contrasting over uniform stimuli when the center was looming and the background receding but not when the center was receding and the background looming. Therefore, our results do not support the hypothesis that tectal neurons are sensitive to pop-out per-se. The specific sensitivity to the motion contrasting stimulus is consistent with the idea that object motion and not large field motion (e.g., self-induced motion) is coded in the neural responses of tectal neurons

On the semantics of concurrency: Partial orders and transition systems

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Timed unfoldings for networks of timed automata

Abstract. Whereas partial order methods have proved their efficiency for the analysis of discrete-event systems, their application to timed systems remains a challenging research topic. Here, we design a verification algorithm for networks of timed automata with invariants. Based on the unfolding technique, our method produces a branching process as an acyclic Petri net extended with read arcs. These arcs verify conditions on tokens without consuming them, thus expressing concurrency between conditions checks. They are useful for avoiding the explosion of the size of the unfolding due to clocks which are compared with constants but not reset. Furthermore, we attach zones to events, in addition to markings. We then compute a complete finite prefix of the unfolding. The presence of invariants goes against the concurrency since it entails a global synchronization on time. The use of read arcs and the analysis of the clock constraints appearing in invariants helps increasing the concurrency relation between events. Finally, the finite prefix can be used to decide reachability properties, and transition enabling.

Partial orderings descriptions and observations of nondeterministic concurrent processes

A methodology is introduced for defining truly concurrent semantics of processes as equivalence classes of Labelled Event Structures (LES). The construction of a les providing the operational semantics of systems consists of three main steps. First, systems are decomposed into sets of sequential processes and a set of rewriting rules is introduced which describe both the actions sequential processes may perform and their causal relation. Then, the rewriting rules are used to build an occurrence net. Finally, the required event structure is easily derived from the occurrence net. As a test case, a partial ordering operational semantics is introduced first for a subset of Milner's CCS and then for the whole calculus. The proposed semantics are consistent with the original interleaving semantics of the calculus and are able to capture all and only the parallelism present in its multiset semantics. In order to obtain more abstract semantic definitions, new notions of observational equivalence on Labelled Event Structures are introduced that preserve both concurrency and nondeterminism