Search results for "I.5"
showing 10 items of 399 documents
Therapeutic administration of 3,4,5-trimethoxy-4'-fluorochalcone, a selective inhibitor of iNOS expression, attenuates the development of adjuvant-in…
2003
We have previously investigated the effects of a series of dimethoxy- and trimethoxychalcone derivatives, with various patterns of fluorination, on nitric oxide production in LPS-stimulated murine RAW 264.7. The present study was designed to determine if 3,4,5-trimethoxy-4'-fluorochalcone (CH 17) could modulate the production of NO and/or prostaglandins in vivo. On the mouse macrophage cell line RAW 264.7 CH 17 inhibited dose-dependently NO production, with an IC(50) value in the nanomolar range, and reduced PGE(2) levels by a 58% at 10 microM. This compound had no direct inhibitory effect on iNOS and COX-2 activities. NO reduction was the consequence of inhibition of the expression of iNOS…
Hypusinated eIF5A is required for the translation of collagen
2021
AbstractThe evolutionary conserved elongation factor eIF5A is required for the translation of mRNAs that encode protein sequences with consecutive prolines or combined with glycine and charged amino acids. Mammalian collagens are enriched in putative eIF5A-dependent Pro-Gly-containing tripeptides. Here, we show that eIF5A is needed for heterologous expression of collagen in yeast, and using a dual luciferase reporter system we confirmed that eIF5A depletion interrupts translation at Pro-Gly-collagenic motifs. Using mouse fibroblasts, we showed that depletion of active eIF5A reduced collagen 1α (Col1a1) content, which became concentrated around the nuclei, in contrast to a stronger and all o…
Linear density-based clustering with a discrete density model
2018
Density-based clustering techniques are used in a wide range of data mining applications. One of their most attractive features con- sists in not making use of prior knowledge of the number of clusters that a dataset contains along with their shape. In this paper we propose a new algorithm named Linear DBSCAN (Lin-DBSCAN), a simple approach to clustering inspired by the density model introduced with the well known algorithm DBSCAN. Designed to minimize the computational cost of density based clustering on geospatial data, Lin-DBSCAN features a linear time complexity that makes it suitable for real-time applications on low-resource devices. Lin-DBSCAN uses a discrete version of the density m…
Graph Embedding via High Dimensional Model Representation for Hyperspectral Images
2021
Learning the manifold structure of remote sensing images is of paramount relevance for modeling and understanding processes, as well as to encapsulate the high dimensionality in a reduced set of informative features for subsequent classification, regression, or unmixing. Manifold learning methods have shown excellent performance to deal with hyperspectral image (HSI) analysis but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample data. A common assumption to deal with the problem is that the transformation between the high-dimensional input space and the (typically low) latent space is linear. This is a particularly strong assump…
Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop
2018
Active inference is an ambitious theory that treats perception, inference and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g.\ different environments or agent morphologies. In the literature, paradigms that share this independence have been summarised under the notion of in…
Visualization of Jacques Lacan's Registers of the Psychoanalytic Field, and Discovery of Metaphor and of Metonymy. Analytical Case Study of Edgar All…
2016
We start with a description of Lacan's work that we then take into our analytics methodology. In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more comprehensive investigation, we develop an approach for revealing, that is, uncovering, Lacanian register relationships. Objectives of this work include the wide and general application of our met…
Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines
2021
Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word-level is crucial to provide a more fine-grained analysis than what is available at the document level. In this work, we propose a Tsetlin machine (TM)-based architecture for scoring individual words according to their contribution to novelty. Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses. We then adopt this description to measure how …
Explainable Tsetlin Machine framework for fake news detection with credibility score assessment
2021
The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and quality-assure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to capture lexical and…
Time Difference of Arrival Estimation from Frequency-Sliding Generalized Cross-Correlations Using Convolutional Neural Networks
2020
The interest in deep learning methods for solving traditional signal processing tasks has been steadily growing in the last years. Time delay estimation (TDE) in adverse scenarios is a challenging problem, where classical approaches based on generalized cross-correlations (GCCs) have been widely used for decades. Recently, the frequency-sliding GCC (FS-GCC) was proposed as a novel technique for TDE based on a sub-band analysis of the cross-power spectrum phase, providing a structured two-dimensional representation of the time delay information contained across different frequency bands. Inspired by deep-learning-based image denoising solutions, we propose in this paper the use of convolutio…
Innføring av CRM i offentlig sektor
2007
Masteroppgave i informasjonssystemer 2007 - Høgskolen i Agder, Kristiansand Modernisering av offentlig sektor basert på informasjons- og kommunikasjonsteknologi (IKT) er i økende grad et fokusområde under begrepet e-forvaltning. Dette har ført til nye teknologiske muligheter og arbeidsmåter internt og eksternt mellom offentlige organisasjoner, næringslivet og innbyggere. Integrering av isolerte systemer til standardiserte og virksomhetsomfattende informasjonssystemer har potensiale til å samle informasjonsflyten i en organisasjon. Dette kan skape radikale endringer både fra et organisatorisk og et teknologisk perspektiv. Customer Relationship Management (CRM) er et konsept fra privat sektor…