Googling the brain: discovering hierarchical and asymmetric network structures, with applications in neuroscience

Hierarchical organisation is a common feature of many directed networks arising in nature and technology. For example, a well-defined message-passing framework based on managerial status typically exists in a business organisation. However, in many real-world networks such patterns of hierarchy are unlikely to be quite so transparent. Due to the nature in which empirical data is collated the nodes will often be ordered so as to obscure any underlying structure. In addition, the possibility of even a small number of links violating any overall “chain of command” makes the determination of such structures extremely challenging. Here we address the issue of how to reorder a directed network in order to reveal this type of hierarchy. In doing so we also look at the task of quantifying the level of hierarchy, given a particular node ordering. We look at a variety of approaches. Using ideas from the graph Laplacian literature, we show that a relevant discrete optimization problem leads to a natural hierarchical node ranking. We also show that this ranking arises via a maximum likelihood problem associated with a new range-dependent hierarchical random graph model. This random graph insight allows us to compute a likelihood ratio that quantifies the overall tendency for a given network to be hierarchical. We also develop a generalization of this node ordering algorithm based on the combinatorics of directed walks. In passing, we note that Google’s PageRank algorithm tackles a closely related problem, and may also be motivated from a combinatoric, walk-counting viewpoint. We illustrate the performance of the resulting algorithms on synthetic network data, and on a real-world network from neuroscience where results may be validated biologically

Requirement for YAP1 signaling in myxoid liposarcoma

Myxoid liposarcomas (MLS), malignant tumors of adipocyte origin, are driven by the FUS-DDIT3 fusion gene encoding an aberrant transcription factor. The mechanisms whereby FUS-DDIT3 mediates sarcomagenesis are incompletely understood, and strategies to selectively target MLS cells remain elusive. Here we show, using an unbiased functional genomic approach, that FUS-DDIT3-expressing mesenchymal stem cells and MLS cell lines are dependent on YAP1, a transcriptional co-activator and central effector of the Hippo pathway involved in tissue growth and tumorigenesis, and that increased YAP1 activity is a hallmark of human MLS Mechanistically, FUS-DDIT3 promotes YAP1 expression, nuclear localization, and transcriptional activity and physically associates with YAP1 in the nucleus of MLS cells. Pharmacologic inhibition of YAP1 activity impairs the growth of MLS cells in vitro and in vivo These findings identify overactive YAP1 signaling as unifying feature of MLS development that could represent a novel target for therapeutic intervention

Verification of Program Transformations with Inductive Refinement Types

International audienceHigh-level transformation languages like Rascal include expressive features for manipulating large abstract syntax trees: first-class traversals, expressive pattern matching, backtracking, and generalized iterators. We present the design and implementation of an abstract interpretation tool, Rabit, for verifying inductive type and shape properties for transformations written in such languages. We describe how to perform abstract interpretation based on operational semantics, specifically focusing on the challenges arising when analyzing the expressive traversals and pattern matching. Finally, we evaluate Rabit on a series of transformations (normalization, desugaring, refactoring, code generators, type inference, etc.) showing that we can effectively verify stated properties

Behavioral outcome measures used for human neural stem cell transplantation in rat stroke models

Stroke is a leading cause of death and disability, leading to the development of various stroke models to test new treatments, most commonly in the rat. Human stroke trials focus on disability, related primarily to neurological deficits. To better model the clinical application of these treatments, many behavioral tests have been developed using the rat stroke model. We performed a systematic review of all the behavioral outcome measures used in published studies of human neural stem cell transplantation in rat stroke models. The reviewed tests include motor, sensory, cognitive, activity, and combination tests. For each test, we give a brief description, trace the origin of the test, and discuss test performance in the reviewed studies. We conclude that while many behavioral tests are available for this purpose, there does not appear to be consensus on an optimal testing strategy

The experience of new teachers

노트 : The OECD Teaching and Learning International Surve