Notes

Outcomes of Carotid Revascularization inside Individuals using Contralateral Carotid Artery Stoppage.
inforce that moderate-to-severe TED is difficult to treat with an unmet medical need in the United States.
Germline mutations in the succinate dehydrogenase genes (
/
/
/
,
-collectively, "
") have been implicated in paraganglioma (PGL), renal cell carcinoma (RCC), gastrointestinal stromal tumor (GIST), and pituitary adenoma (PA). PTX inhibitor price Negative SDHB tumor staining is indicative of SDH-deficient tumors, usually reflecting an underlying germline
mutation. However, approximately 20% of individuals with SDH-deficient tumors lack an identifiable germline
mutation.

We performed whole-exome sequencing (WES) of germline and tumor DNA followed by Sanger sequencing validation, transcriptome analysis, metabolomic studies, and haplotype analysis in 2 Italian-Australian families with SDH-deficient PGLs and various neoplasms, including RCC, GIST, and PA.

Germline WES revealed a novel
intronic variant, which had been missed during previous routine testing, in 4 affected siblings of the index family. Transcriptome analysis demonstrated aberrant
splicing, with the retained intronic segment introducing a premature stop codon. WES of available tumors in this family showed chromosome 1 deletion with loss of wild-type
in a PGL and a somatic gain-of-function
mutation in a GIST. The
intronic variant identified was subsequently detected in the second family, with haplotype analysis indicating a founder effect.

This is the deepest intronic variant to be reported among the
genes. Intronic variants beyond the limits of standard gene sequencing analysis should be considered in patients with SDH-deficient tumors but negative genetic test results.
This is the deepest intronic variant to be reported among the SDHx genes. Intronic variants beyond the limits of standard gene sequencing analysis should be considered in patients with SDH-deficient tumors but negative genetic test results.Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white-matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step toward the ability to manipulate the brain's large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends on the distribution of network edge weights. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we demonstrate that weighted subgraph centrality, a measure rooted in the graph spectrum, and which captures higher order graph architecture, is a stronger and more consistent predictor of controllability. Our study contributes to an understanding of how the brain's diverse mesoscale structure supports transient communication dynamics.The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here we investigate how this intrinsic hierarchical organization of the brain shapes the transmission of information among regions. The hierarchical positioning of individual regions was quantified by applying diffusion map embedding to resting-state functional MRI networks. Structural networks were reconstructed from diffusion spectrum imaging and topological shortest paths among all brain regions were computed. Sequences of nodes encountered along a path were then labeled by their hierarchical position, tracing out path motifs. We find that the cortical hierarchy guides communication in the network. Specifically, nodes are more likely to forward signals to nodes closer in the hierarchy and cover a range of unimodal and transmodal regions, potentially enriching or diversifying signals en route. We also find evidence of systematic detours, particularly in attention networks, where communication is rerouted. Altogether, the present work highlights how the cortical hierarchy shapes signal exchange and imparts behaviorally relevant communication patterns in brain networks.Signal interactions in brain network communication have been little studied. We describe how nonlinear collision rules on simulated mammal brain networks can result in sparse activity dynamics characteristic of mammalian neural systems. We tested the effects of collisions in "information spreading" (IS) routing models and in standard random walk (RW) routing models. Simulations employed synchronous agents on tracer-based mesoscale mammal connectomes at a range of signal loads. We find that RW models have high average activity that increases with load. Activity in RW models is also densely distributed over nodes a substantial fraction is highly active in a given time window, and this fraction increases with load. Surprisingly, while IS models make many more attempts to pass signals, they show lower net activity due to collisions compared to RW, and activity in IS increases little as function of load. Activity in IS also shows greater sparseness than RW, and sparseness decreases slowly with load. Results hold on two networks of the monkey cortex and one of the mouse whole-brain.
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