Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment.

Cognitive processing is highly dependent on the functional integrity of gamma-amino-butyric acid (GABA) interneurons in the brain. These cells regulate excitability and synaptic plasticity of principal neurons balancing the excitatory/inhibitory tone of cortical networks. Reduced function of parvalbumin (PV) interneurons and disruption of GABAergic synapses in the cortical circuitry result in desynchronized network activity associated with cognitive impairment across many psychiatric disorders, including schizophrenia. However, the mechanisms underlying these complex phenotypes are still poorly understood. Here we show that in animal models, genetic deletion of fibroblast growth factor 14 (Fgf14), a regulator of neuronal excitability and synaptic transmission, leads to loss of PV interneurons in the CA1 hippocampal region, a critical area for cognitive function. Strikingly, this cellular phenotype associates with decreased expression of glutamic acid decarboxylase 67 (GAD67) and vesicular GABA transporter (VGAT) and also coincides with disrupted CA1 inhibitory circuitry, reduced in vivo gamma frequency oscillations and impaired working memory. Bioinformatics analysis of schizophrenia transcriptomics revealed functional co-clustering of FGF14 and genes enriched within the GABAergic pathway along with correlatively decreased expression of FGF14, PVALB, GAD67 and VGAT in the disease context. These results indicate that Fgf14^−/− mice recapitulate salient molecular, cellular, functional and behavioral features associated with human cognitive impairment, and FGF14 loss of function might be associated with the biology of complex brain disorders such as schizophrenia.

This paper investigates the performance of frames for the linear, redundant encoding of vectors when consecutive frame coefficients are lost due to the occurrence of random burst errors. We assume that the distribution of bursts is invariant under cyclic shifts and that the burst-length statistics are known. In analogy with rate-distortion theory, we wish to find frames of a given size, which minimize the mean-square reconstruction error for the encoding of vectors in a complex finite-dimensional Hilbert space. We obtain an upper bound for the mean-square reconstruction error for a given Parseval frame and in the case of cyclic Parseval frames, we find a family of frames which minimizes this upper bound. Under certain conditions, these minimizers are identical to complex Bose-Chaudhuri-Hocquenghem codes discussed in the literature. The accuracy of our upper bounds for the mean-square error is substantiated by complementary lower bounds. All estimates are based on convexity arguments and a discrete rearrangement inequality.