NLC-2 graph recognition and isomorphism

NLC-width is a variant of clique-width with many application in graph algorithmic. This paper is devoted to graphs of NLC-width two. After giving new structural properties of the class, we propose a $O(n^2 m)$-time algorithm, improving Johansson's algorithm \cite{Johansson00}. Moreover, our alogrithm is simple to understand. The above properties and algorithm allow us to propose a robust $O(n^2 m)$-time isomorphism algorithm for NLC-2 graphs. As far as we know, it is the first polynomial-time algorithm.Comment: soumis \`{a} WG 2007; 12

Endothelin-1-induced spreading depression in rats is associated with a microarea of selective neuronal necrosis.

NoTwo different theories of migraine aura exist: In the vascular theory of Wolff, intracerebral vasoconstriction causes migraine aura via energy deficiency, whereas in the neuronal theory of Leão and Morison, spreading depression (SD) initiates the aura. Recently, it has been shown that the cerebrovascular constrictor endothelin-1 (ET-1) elicits SD when applied to the cortical surface, a finding that could provide a bridge between the vascular and the neuronal theories of migraine aura. Several arguments support the notion that ET-1¿induced SD results from local vasoconstriction, but definite proof is missing. If ET-1 induces SD via vasoconstriction/ischemia, then neuronal damage is likely to occur, contrasting with the fact that SD in the otherwise normal cortex is not associated with any lesion. To test this hypothesis, we have performed a comprehensive histologic study of the effects of ET-1 when applied topically to the cerebral cortex of halothane-anesthetized rats. Our assessment included histologic stainings and immunohistochemistry for glial fibrillary acidic protein, heat shock protein 70, and transferase dUTP nick-end labeling assay. During ET-1 application, we recorded (i) subarachnoid direct current (DC) electroencephalogram, (ii) local cerebral blood flow by laser-Doppler flowmetry, and (iii) changes of oxyhemoglobin and deoxyhemoglobin by spectroscopy. At an ET-1 concentration of 1 µM, at which only 6 of 12 animals generated SD, a microarea with selective neuronal death was found only in those animals demonstrating SD. In another five selected animals, which had not shown SD in response to ET-1, SD was triggered at a second cranial window by KCl and propagated from there to the window exposed to ET-1. This treatment also resulted in a microarea of neuronal damage. In contrast, SD invading from outside did not induce neuronal damage in the absence of ET-1 (n = 4) or in the presence of ET-1 if ET-1 was coapplied with BQ-123, an ETA receptor antagonist (n = 4). In conclusion, SD in presence of ET-1 induced a microarea of selective neuronal necrosis no matter where the SD originated. This effect of ET-1 appears to be mediated by the ETA receptor

Extending Partial Representations of Circle Graphs

The partial representation extension problem is a recently introduced generalization of the recognition problem. A circle graph is an intersection graph of chords of a circle. We study the partial representation extension problem for circle graphs, where the input consists of a graph G and a partial representation R′ giving some pre-drawn chords that represent an induced subgraph of G. The question is whether one can extend R′ to a representation R of the entire G, i.e., whether one can draw the remaining chords into a partially pre-drawn representation. Our main result is a polynomial-time algorithm for partial representation extension of circle graphs. To show this, we describe the structure of all representation a circle graph based on split decomposition. This can be of an independent interest

Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms

Abstract Purpose: While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers, such as tumor mutational burden (TMB). Experimental Design: Tumors from a cohort of patients with late-stage melanoma (n = 51) were profiled using an immune-enhanced exome and transcriptome platform. We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB. Results: Our neoantigen burden score, which integrates both exome and transcriptome features, more significantly stratified responders and nonresponders (P = 0.016) than TMB alone (P = 0.049). Extension of this model to include immune-related resistance mechanisms affecting the antigen presentation machinery, such as HLA allele-specific LOH, resulted in a composite neoantigen presentation score (NEOPS) that demonstrated further increased association with therapy response (P = 0.002). Conclusions: NEOPS proved the statistically strongest biomarker compared with all single-gene biomarkers, expression signatures, and TMB biomarkers evaluated in this cohort. Subsequent confirmation of these findings in an independent cohort of patients (n = 110) suggests that NEOPS is a robust, novel biomarker of ICB response in melanoma. </jats:sec

Supplementary Table from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms

Supplementary Table from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanism

Supplementary Table from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms

Supplementary Table from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanism

Supplementary Table from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms

Supplementary Table from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanism

Supplementary Data from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms

Supplementary Data from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanism

Supplementary Table from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms

Supplementary Table from Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanism