Porous gravity currents: A survey to determine the joint influence of fluid rheology and variations of medium properties

We develop a model to grasp the combined effect of rheology and spatial stratifications on two- dimensional non-Newtonian gravity-driven flow in porous media. We consider a power-law constitutive equation for the fluid, and a monomial variation of permeability and porosity along the vertical direction (transverse to the flow) or horizontal direction (parallel to the flow). Under these assumptions, similar- ity solutions are derived in semi-analytical form for thin gravity currents injected into a two-dimensional porous medium and having constant or time-varying volume. The extent and shape of the porous domain affected by the injection is significantly influenced by the interplay of model parameters. These describe the fluid (flow behaviour index n ), the spatial heterogeneity (coefficients β, γ, δ, ω for variations of per- meability and porosity in the horizontal or vertical direction), and the type of release (volume exponent α). Theoretical results are validated against two sets of experiments with α= 1 (constant inflow) con- ducted with a stratified porous medium (simulated by superimposing layers of glass beads of different diameter) and a Hele-Shaw analogue for power-law fluid flow, respectively. In the latter case, a recently established Hele-Shaw analogy is extended to the variation of properties parallel to the flow direction. Comparison with experimental results shows that the proposed model is able to capture the propagation of the current front and the current profile

Materiali per il secondo volume della Introduzione alle scienze dello spirito: Scritti inediti 1880-1893

[English]:As is well-known to the scholars of European philosophical thinking between the eighteenth and twentieth centuries, the great and ambitious plan of diltheyan criticism of the reason of history condensed in a pond-pink Introduction to the Human Sciences appeared in 1883 only the first volume especially the first two Books. The volume, which we present here, encloses in chronological order the various drafts which, as early as 1880, Dilthey made the work of the second volume of the work, which would have to contain the other four Books, the dedicated ones to the gnoseological, logical and methodological aspects of the philosophical foundation of the so-called sciences of the spirit. An absorbing commitment, which Dilthey will pursue throughout his scientific life, without saving power, will cost him a little in terms of psycho-physical health. In addition to the volume, which besides Breslau drafts also contains the famous Berlin Plan dating back to the early nineties, we also thought it appropriate to include the two dafts of the "Althoff Letter" dating back to mid-1882, as they also contain useful observations on the designed Introduction to the Human Sciences. / [Italiano]: Com’è noto agli studiosi del pensiero filosofico europeo tra Otto e Novecento, del grande e ambizioso piano diltheyano di una critica della ragione storica condensato in una ponderosa Introduzione alle scienze dello spirito apparve, nel 1883, solamente il primo volume, contenente segnatamente i primi due Libri. Il volume, che qui presentiamo, racchiude in ordine cronologico le varie elaborazioni che, già a partire dal 1880, Dilthey realizzò in funzione della stesura del secondo volume dell’opera, che avrebbe dovuto contenere gli altri quattro Libri, quelli dedicati agli aspetti gnoseologici, logici e metodologici della fondazione filosoficadelle cosiddette scienze dello spirito. Un impegno assorbente, che Dilthey perseguirà per tutta la propria vitascientifica, senza risparmio di forza, che gli costerà non poco in termini di salute psico-fisica. In Appendice al volume, che oltre alle elaborazioni di Breslavia contiene anche il noto Progetto di Berlino risalente ai primi anni Novanta, abbiamo ritenuto opportuno inserire anche le due minute della Lettera ad Althoff, databili intorno alla metà del 1882, poiché contengono anch’esse utili osservazioni sulla progettata Introduzione alle scienze dello spirito

Frequent 4EBP1 Amplification Induces Synthetic Dependence on FGFR Signaling in Cancer

Simple Summary Our work establishes that amplification of 4EBP1, as a part of Chr. 8p11, creates a synthetic dependency on FGFR1 signaling in cancer. 4EBP1 is phosphorylated by FGFR1 and PI3K signaling, and accordingly cancer with 4EBP1-FGFR1 amplification is more sensitive to FGFR1 and PI3K inhibition due to inhibition of 4EBP1 phosphorylation. Moreover, we characterize the translational targets of 4EBP1 and identify that 4EBP1 specifically regulates the translation of genes involved in insulin signaling, glucose metabolism, and the inositol pathway that plays a role in cancer progression. The eIF4E translation initiation factor has oncogenic properties and concordantly, the inhibitory eIF4E-binding protein (4EBP1) is considered a tumor suppressor. The exact molecular effects of 4EBP1 activation in cancer are still unknown. Surprisingly, 4EBP1 is a target of genomic copy number gains (Chr. 8p11) in breast and lung cancer. We noticed that 4EBP1 gains are genetically linked to gains in neighboring genes, including WHSC1L1 and FGFR1. Our results show that FGFR1 gains act to attenuate the function of 4EBP1 via PI3K-mediated phosphorylation at Thr37/46, Ser65, and Thr70 sites. This implies that not 4EBP1 but instead FGFR1 is the genetic target of Chr. 8p11 gains in breast and lung cancer. Accordingly, these tumors show increased sensitivity to FGFR1 and PI3K inhibition, and this is a therapeutic vulnerability through restoring the tumor-suppressive function of 4EBP1. Ribosome profiling reveals genes involved in insulin signaling, glucose metabolism, and the inositol pathway to be the relevant translational targets of 4EBP1. These mRNAs are among the top 200 translation targets and are highly enriched for structure and sequence motifs in their 5 ' UTR, which depends on the 4EBP1-EIF4E activity. In summary, we identified the translational targets of 4EBP1-EIF4E that facilitate the tumor suppressor function of 4EBP1 in cancer

The molecular diversity of Luminal A breast tumors

Breast cancer is a collection of diseases with distinct molecular traits, prognosis, and therapeutic options. Luminal A breast cancer is the most heterogeneous, both molecularly and clinically. Using genomic data from over 1,000 Luminal Atumors from multiple studies, we analyzed the copy number and mutational landscape of this tumor subtype. This integrated analysis revealed four major subtypes defined by distinct copy-number and mutation profiles. We identified an atypical Luminal A subtype characterized by high genomic instability, TP53 mutations, and increased Aurora kinase signaling; these genomic alterations lead to a worse clinical prognosis. Aberrations of chromosomes 1, 8, and 16, together with PIK3CA, GATA3, AKT1, and MAP3K1 mutations drive the other subtypes. Finally, an unbiased pathway analysis revealed multiple rare, but mutually exclusive, alterations linked to loss of activity of co-repressor complexes N-Cor and SMRT. These rare alterations were the most prevalent in Luminal A tumors and may predict resistance to endocrine therapy. Our work provides for a further molecular stratification of Luminal A breast tumors, with potential direct clinical implications.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-013-2699-3) contains supplementary material, which is available to authorized users

Analysis of interactions between ribosomal proteins and RNA structural motifs

<p>Abstract</p> <p>Background</p> <p>One important goal of structural bioinformatics is to recognize and predict the interactions between protein binding sites and RNA. Recently, a comprehensive analysis of ribosomal proteins and their interactions with rRNA has been done. Interesting results emerged from the comparison of r-proteins within the small subunit in <it>T. thermophilus </it>and <it>E. coli</it>, supporting the idea of a core made by both RNA and proteins, conserved by evolution. Recent work showed also that ribosomal RNA is modularly composed. Motifs are generally single-stranded sequences of consecutive nucleotides (ssRNA) with characteristic folding. The role of these motifs in protein-RNA interactions has been so far only sparsely investigated.</p> <p>Results</p> <p>This work explores the role of RNA structural motifs in the interaction of proteins with ribosomal RNA (rRNA). We analyze composition, local geometries and conformation of interface regions involving motifs such as tetraloops, kink turns and single extruded nucleotides. We construct an interaction map of protein binding sites that allows us to identify the common types of shared 3-D physicochemical binding patterns for tetraloops. Furthermore, we investigate the protein binding pockets that accommodate single extruded nucleotides either involved in kink-turns or in arbitrary RNA strands. This analysis reveals a new structural motif, called <it>tripod</it>.</p> <p>It corresponds to small pockets consisting of three aminoacids arranged at the vertices of an almost equilateral triangle. We developed a search procedure for the recognition of tripods, based on an empirical tripod fingerprint.</p> <p>Conclusion</p> <p>A comparative analysis with the overall RNA surface and interfaces shows that contact surfaces involving RNA motifs have distinctive features that may be useful for the recognition and prediction of interactions.</p

Emerging landscape of oncogenic signatures across human cancers.

Cancer therapy is challenged by the diversity of molecular implementations of oncogenic processes and by the resulting variation in therapeutic responses. Projects such as The Cancer Genome Atlas (TCGA) provide molecular tumor maps in unprecedented detail. The interpretation of these maps remains a major challenge. Here we distilled thousands of genetic and epigenetic features altered in cancers to ∼500 selected functional events (SFEs). Using this simplified description, we derived a hierarchical classification of 3,299 TCGA tumors from 12 cancer types. The top classes are dominated by either mutations (M class) or copy number changes (C class). This distinction is clearest at the extremes of genomic instability, indicating the presence of different oncogenic processes. The full hierarchy shows functional event patterns characteristic of multiple cross-tissue groups of tumors, termed oncogenic signature classes. Targetable functional events in a tumor class are suggestive of class-specific combination therapy. These results may assist in the definition of clinical trials to match actionable oncogenic signatures with personalized therapies

Synaptic proximity enables NMDAR signalling to promote brain metastasis.

Metastasis-the disseminated growth of tumours in distant organs-underlies cancer mortality. Breast-to-brain metastasis (B2BM) is a common and disruptive form of cancer and is prevalent in the aggressive basal-like subtype, but is also found at varying frequencies in all cancer subtypes. Previous studies revealed parameters of breast cancer metastasis to the brain, but its preference for this site remains an enigma. Here we show that B2BM cells co-opt a neuronal signalling pathway that was recently implicated in invasive tumour growth, involving activation by glutamate ligands of N-methyl-D-aspartate receptors (NMDARs), which is key in model systems for metastatic colonization of the brain and is associated with poor prognosis. Whereas NMDAR activation is autocrine in some primary tumour types, human and mouse B2BM cells express receptors but secrete insufficient glutamate to induce signalling, which is instead achieved by the formation of pseudo-tripartite synapses between cancer cells and glutamatergic neurons, presenting a rationale for brain metastasis.This work was principally supported by grants from the Swiss National Science Foundation and the European Research Council, and by a gift from the Biltema Foundation that was administered by the ISREC Foundation, Lausanne, Switzerland

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas.

Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders

AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo

The molecular basis of breast cancer pathological phenotypes

The histopathological evaluation of morphological features in breast tumours provides prognostic information to guide therapy. Adjunct molecular analyses provide further diagnostic, prognostic and predictive information. However, there is limited knowledge of the molecular basis of morphological phenotypes in invasive breast cancer. This study integrated genomic, transcriptomic and protein data to provide a comprehensive molecular profiling of morphological features in breast cancer. Fifteen pathologists assessed 850 invasive breast cancer cases from The Cancer Genome Atlas (TCGA). Morphological features were significantly associated with genomic alteration, DNA methylation subtype, PAM50 and microRNA subtypes, proliferation scores, gene expression and/or RPPA subtype. Marked nuclear pleomorphism, necrosis, inflammation and high mitotic count were associated with Basal-like subtype and have similar molecular basis. Omics-based signatures were constructed to predict morphological features. The association of morphology transcriptome signatures with overall survival in oestrogen receptor (ER)-positive and ER-negative breast cancer was first assessed using the METABRIC dataset; signatures that remained prognostic in the METABRIC multivariate analysis were further evaluated in five additional datasets. The transcriptomic signature of epithelial tubule formation was prognostic in ER-positive breast cancer. No signature was prognostic in ER-negative. This study provided new insights into the molecular basis of breast cancer morphological phenotypes. The integration of morphological with molecular data has potential to refine breast cancer classification, predict response to therapy, enhance our understanding of breast cancer biology and improve clinical management. This work is publicly accessible at www.dx.ai/tcga_breast