http://web.stanford.edu/group/brunet/Cell1999

Akt Promotes Cell Survival by Phosphorylating and Inhibiting a Forkhead Transcription Factor

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

Mitogen-activated protein kinases (MAPKs) are important signal transducing enzymes, unique to eukaryotes, that are involved in many facets of cellular regulation. Initial research concentrated on defining the components and organization of MAPK signalling cascades, but recent studies have begun to shed light on the physiological functions of these cascades in the control of gene expression, cell proliferation and programmed cell death.

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Mammalian MAP kinase signalling cascades

Cells undergo a variety of biological responses when placed in hypoxic conditions, including activation of signalling pathways that regulate proliferation, angiogenesis and death. Cancer cells have adapted these pathways, allowing tumours to survive and even grow under hypoxic conditions, and tumour hypoxia is associated with poor prognosis and resistance to radiation therapy. Many elements of the hypoxia-response pathway are therefore good candidates for therapeutic targeting.

Hypoxia — a key regulatory factor in tumour growth

Specificity of receptor tyrosine kinase signaling: Transient versus sustained extracellular signal-regulated kinase activation

Renal Cell Carcinoma (RCC) encompasses a heterogeneous group of cancers derived from renal tubular epithelial cells and has a worldwide mortality. However, mortality rates have barely improved over the last 20 years. Novel biomarkers and biomarkers are thus urgently required for this cancer. The inventors have devised a strategy to produce mouse cancer cell lines of progressively enhanced aggressiveness and specialization. The mouse renal cancer cell line RENCA was serially passaged in vivo using multiple implantation strategies designed to replicate different aspects of primary tumour growth and metastasis. Transcriptomic and epigenomic data has been acquired for the derived cell lines and primary analyses have been performed. The inventors then selected plurality of genes with no reported role in RCC which were upregulated in their specialized cell lines, and checked their relevance in patient data and clinical samples. This approach contributes to identify 4 serum biomarkers, namely IL-34, SAA2, PONL1 and CFB that are suitable for predicting survival time in patients suffering from RCC. The inventors also validated that the 4 proteins are also biotargets for the treatment of RCC.

New biomarkers and biotargets in renal cell carcinoma

We propose two parametric approaches to price swing contracts with firm constraints. Our objective is to create approximations for the optimal control, which represents the amounts of energy purchased throughout the contract. The first approach involves explicitly defining a parametric function to model the optimal control, and the parameters using stochastic gradient descent-based algorithms. The second approach builds on the first one, replacing the parameters with neural networks. Our numerical experiments demonstrate that by using Langevin-based algorithms, both parameterizations provide, in a short computation time, better prices compared to state-of-the-art methods (like the one given by Longstaff and Schwartz).

Swing Contract Pricing: A Parametric Approach with Adjoint Automatic Differentiation and Neural Networks

La presente invention concerne une methode de prediction ou de surveillance de la sensibilite d'un sujet ayant une tumeur, notamment un carcinome a cellules renales (RCC) a un traitement du cancer, une methode de selection d'un traitement approprie du cancer, une methode de criblage ou d'identification d'un compose adapte a l'amelioration du traitement d'un cancer, et l'utilisation de kits correspondants.

Procédés et kits pour prédire la sensibilité d'un sujet souffrant d'un cancer rénal à un traitement du cancer

In The Name of Chance

We investigate the greedy version of the $L^p$-optimal vector quantization problem for an $\mathbb{R}^d$-valued random vector $X\!\in L^p$. We show the existence of a sequence $(a_N)_{N\ge 1}$ such that $a_N$ minimizes $a\mapsto\big \|\min_{1\le i\le N-1}|X-a_i|\wedge |X-a|\big\|_{L^p}$ ($L^p$-mean quantization error at level $N$ induced by $(a_1,\ldots,a_{N-1},a)$). We show that this sequence produces $L^p$-rate optimal $N$-tuples $a^{(N)}=(a_1,\ldots,a_{_N})$ ($i.e.$ the $L^p$-mean quantization error at level $N$ induced by $a^{(N)}$ goes to $0$ at rate $N^{-\frac 1d}$). Greedy optimal sequences also satisfy, under natural additional assumptions, the distortion mismatch property: the $N$-tuples $a^{(N)}$ remain rate optimal with respect to the $L^q$-norms, $p\le q <p+d$. Finally, we propose optimization methods to compute greedy sequences, adapted from usual Lloyd's I and Competitive Learning Vector Quantization procedures, either in their deterministic (implementable when $d=1$) or stochastic versions.

Gilles Pagès

Papers

New biomarkers and biotargets in renal cell carcinoma

Swing Contract Pricing: A Parametric Approach with Adjoint Automatic Differentiation and Neural Networks

Procédés et kits pour prédire la sensibilité d'un sujet souffrant d'un cancer rénal à un traitement du cancer

In The Name of Chance

Greedy vector quantization