A robust experimental and computational analysis framework at multiple resolutions, modalities and coverages.

The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is needed, especially for cancer immunotherapy development, which requires understanding of molecular mechanisms and discovery of more druggable targets. In this work, we assembled and evaluated an integrated experimental framework and analytical process to enable genome-wide scale discovery of ligand-receptors potentially used for cellular crosstalks, followed by targeted validation. We assessed the complementarity of four different technologies: single-cell RNA sequencing and Spatial transcriptomic (measuring over >20,000 genes), RNA In Situ Hybridization (RNAscope, measuring 4-12 genes) and Opal Polaris multiplex protein staining (4-9 proteins). To utilize the multimodal data, we implemented existing methods and also developed STRISH (Spatial TRanscriptomic In Situ Hybridization), a computational method that can automatically scan across the whole tissue section for local expression of gene (e.g. RNAscope data) and/or protein markers (e.g. Polaris data) to recapitulate an interaction landscape across the whole tissue. We evaluated the approach to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma, which account for over 70% of cancer cases. We showed that inference of cell-cell interactions using scRNA-seq data can misdetect or detect false positive interactions. Spatial transcriptomics still suffers from misdetecting lowly expressed ligand-receptor interactions, but reduces false discovery. RNAscope and Polaris are sensitive methods for defining the location of potential ligand receptor interactions, and the STRISH program can determine the probability that local gene co-expression reflects true cell-cell interaction. We expect that the approach described here will be widely applied to discover and validate ligand receptor interaction in different types of solid cancer tumors.

Six problems for causal inference from fMRI.

Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and physiological knowledge may somewhat constrain the possible hypotheses, but there often remains a vast space of possible causal structures. To find actual effective connectivity relations, search methods must accommodate indirect measurements of nonlinear time series dependencies, feedback, multiple subjects possibly varying in identified regions of interest, and unknown possible location-dependent variations in BOLD response delays. We describe combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects. The method is illustrated with an empirical data set and confirmed with simulations of time series of non-linear, randomly generated, effective connectivities, with feedback, subject to random differences of BOLD delays, with regions of interest missing at random for some subjects, measured with noise approximating the signal to noise ratio of the empirical data.

Solving the brain synchrony eigenvalue problem: conservation of temporal dynamics (fMRI) over subjects doing the same task.

Brain measures often show highly structured temporal dynamics that synchronize when observers are doing the same task. The standard method for analysis of brain imaging signals (e.g. fMRI) uses the GLM for each voxel indexed against a specified experimental design but does not explicitly involve temporal dynamics. Consequently, the design variables that determine the functional brain areas are those correlated with the design variation rather than the common or conserved brain areas across subjects with the same temporal dynamics given the same stimulus conditions. This raises an important theoretical question: Are temporal dynamics conserved across individuals experiencing the same stimulus task? This general question can be framed in a dynamical systems context and further be posed as an eigenvalue problem about the conservation of synchrony across all brains simultaneously. We show that solving the problem results in a non-arbitrary measure of temporal dynamics across brains that scales over any number of subjects, stabilizes with increasing sample size, and varies systematically across tasks and stimulus conditions.

Bottom-up and top-down brain functional connectivity underlying comprehension of everyday visual action.

How can the components of visual comprehension be characterized as brain activity? Making sense of a dynamic visual world involves perceiving streams of activity as discrete units such as eating breakfast or walking the dog. In order to parse activity into distinct events, the brain relies on both the perceptual (bottom-up) data available in the stimulus as well as on expectations about the course of the activity based on previous experience with, or knowledge about, similar types of activity (top-down data). Using fMRI, we examined the contribution of bottom-up and top-down processing to the comprehension of action streams by contrasting familiar action sequences with those having exactly the same perceptual detection and motor responses (yoked control), but no visual action familiarity. New methods incorporating structural equation modeling of the data yielded distinct patterns of interactivity among brain areas as a function of the degree to which bottom-up and top-down data were available.

The distribution of BOLD susceptibility effects in the brain is non-Gaussian.

A key assumption underlying fMRI analysis in the general linear model is that the underlying distribution of BOLD Susceptibility is gaussian. Analysis of several common data sets and experimental paradigms shows that the underlying distribution for the BOLD signal is non-Gaussian. Further identification shows that the distribution is probably Gamma and implications for hemodynamic modeling are discussed as well as recommendations concerning inferential testing in heavy-tailed environments.

Detection of immunologically significant factors for chronic fatigue syndrome using neural-network classifiers.

Neural-network classifiers were used to detect immunological differences in groups of chronic fatigue syndrome (CFS) patients that heretofore had not shown significant differences from controls. In the past linear methods were unable to detect differences between CFS groups and non-CFS control groups in the nonveteran population. An examination of the cluster structure for 29 immunological factors revealed a complex, nonlinear decision surface. Multilayer neural networks showed an over 16% improvement in an n-fold resampling generalization test on unseen data. A sensitivity analysis of the network found differences between groups that are consistent with the hypothesis that CFS symptoms are a consequence of immune system dysregulation. Corresponding decreases in the CD19(+) B-cell compartment and the CD34(+) hematopoietic progenitor subpopulation were also detected by the neural network, consistent with the T-cell expansion. Of significant interest was the fact that, of all the cytokines evaluated, the only one to be in the final model was interleukin-4 (IL-4). Seeing an increase in IL-4 suggests a shift to a type 2 cytokine pattern. Such a shift has been hypothesized, but until now convincing evidence to support that hypothesis has been lacking.

Development of Schemata during Event Parsing: Neisser's Perceptual Cycle as a Recurrent Connectionist Network.

Neural net simulations of human event parsing are described. A recurrent net was used to simulate data collected from human subjects watching short videotaped event sequences. In one simulation, the net was trained on one-half of a taped sequence with the other half of the sequence being used to test transfer performance. In another simulation, the net was trained on one complete event sequence and transfer to a different event sequence was tested. Neural net simulations provide a unique means of observing the interrelation of top-down and bottom-up processing in a basic cognitive task. Examination of computational patterns of the net and cluster analysis of the hidden units revealed two factors that may be central to event perception: (1) similarity between a current input and an activated schema and (2) expected duration of a given event. Although the importance of similarity between input and activated schemata during event perception has been acknowledged previously (e.g., Neisser, 1976; Schank, 1982), the present study provides specific instantiation of how similarity judgments can be made using both top-down and bottom-up processing. Moreover, unlike other work on event perception, this approach provides a potential mechanism for how schemata develop.

Mechanical evaluation of resorbable copolymers for end use as vascular grafts.

Nonwoven, nonporous, completely resorbable vascular grafts of selected polymeric composition (3 mm ID) were prepared and evaluated for similarity of mechanical properties to arterial blood vessels. Copolymers of L-lactide, D,L-lactide, and epsilon-caprolactone were selected for diversity of mechanical properties and degradation rates. Two homogeneous grafts were tested: a 50% L-lactide and 50% epsilon-caprolactone copolymer (L-epsilon), and a 70/30 solution blend of L-epsilon copolymer and its corresponding D,L-lactide copolymer (D,L-epsilon). Composite grafts also were tested: 1) a two-layer graft, 2) an alternating layers graft, and 3) a D,L-epsilon graft reinforced with circumferentially wound poly-L-lactide fibers. The resorbable grafts windowed the physiologic range for circumferential Young's modulus and tensile strength, and were kink resistant. The arterial compliance was greater than that of all solid wall resorbable grafts. Incorporation of porosity into the grafts, which is necessary for tissue ingrowth, is expected to lessen this difference.

Multivariate analysis of Drosophila courtship.

Courtship records of 15 pairs of Drosophila melanogaster were analyzed for temporal stationarity of courtship behaviors, behavioral diversity, behavioral intercorrelations, sequential properties, and information transmission for both sexes. Durations of one male behavior, "orient-back," and two female behaviors, "preen" and "stand still," were found to change from the first to the second half of courtship. Male diversity was greater than female diversity, and both were stationary over time. Correlation analyses failed to single out any particular male or female behaviors as being influential in controlling courtship duration. Male behavior sequences formed several multibehavior loops; female behavior consisted of only a few terminal two-tuple transitions. Transmission analysis carried out on the joint male/female transition matrix showed a higher transmission rate from males to females (12%) than from females to males (7%). Potential applications of this multivariate analysis to investigations of neurobiological and evolutionary aspects of Drosophila courtship behavior are proposed.