METHOD FOR VISUALISING ENERGY SOURCES POWERING BLOCKCHAIN TRANSACTIONS AND APPARATUS THEREOF

The present disclosure relates to a technique for providing visualization of energy sources powering the blockchain transactions. The technique involves determining the energy consumption pattern of each participating node during a transaction. The health of a card associated with a user is displayed by comparing the energy consumption associated with the blockchain transactions performed by the user with a pre-determined threshold. In one embodiment, the health of the card is determined based on the energy consumption report and carbon footprints associated with the transactions. Further, the technique further comprises triggering car linked offers (CLOs) for the user to offset the non-renewable energy consumed. The visualization of energy profile triggers green financial decisions at user end and it additionally offers loyalty points to users for offsetting their unsustainable energy consumption and thus gamifies the whole experience of attaining a greener planet

Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with Experimental Data

Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the protein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-1 and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies.Comment: Pacific Symposium on Biocomputing, Hawaii, In press, Accepted, 200

Élodie Bouygues et France Marchal-Ninosque (dir.), Genèse des seuils, Besançon, Presses universitaires de Franche-Comté, coll. « Annales Littéraires », 2019, 254 p.

« Dans l’ouvrage de Genette, le paratexte n’est jamais envisagé en relation au texte qu’il entoure […], mais comme objet autonome dont il s’agit d’analyser l’effet produit en termes de communication et de pragmatique. Voici une brèche dans laquelle une nouvelle perspective de travail peut s’ouvrir, afin d’analyser l’articulation herméneutique du paratexte et du texte. » C’est dans cette brèche, entre autres, que s’engouffre l’ouvrage collectif Genèse des seuils codirigé par Élodie Bouygues et..

«Molière est comme le chocolat». Fortunes du dramaturge en Suisse romande (1997‑2021)

This paper examines the shared conceptions and current uses of Molière in Western Switzerland (or “Suisse romande”). It aims not only to analyze the reception of his theatre in a French-speaking theatrical field, but also to describe the way in which artists themselves conceive the heritage of this “héros national français”. Based on a series of interviews conducted in the spring of 2021 with seventeen directors based in the region, the contribution first shows that their interest with this playwright mainly lies in the “playfulness” that his theatre allows on stage, beyond any memorial or political question. By looking at some of their recent productions, it then examines how a large number of directors are inspired by the farcical and Italian heritages transmitted by his work in order to anchor their artistic research on the side of theatricality, or even a form of metatheatricality

Predicting DNA-binding sites of proteins from amino acid sequence

BACKGROUND: Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions. RESULTS: We start with a Naïve Bayes classifier trained to predict whether a given amino acid residue is a DNA-binding residue based on its identity and the identities of its sequence neighbors. The input to the classifier consists of the identities of the target residue and 4 sequence neighbors on each side of the target residue. The classifier is trained and evaluated (using leave-one-out cross-validation) on a non-redundant set of 171 proteins. Our results indicate the feasibility of identifying interface residues based on local sequence information. The classifier achieves 71% overall accuracy with a correlation coefficient of 0.24, 35% specificity and 53% sensitivity in identifying interface residues as evaluated by leave-one-out cross-validation. We show that the performance of the classifier is improved by using sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) as additional input. The classifier achieves 78% overall accuracy with a correlation coefficient of 0.28, 44% specificity and 41% sensitivity in identifying interface residues. Examination of the predictions in the context of 3-dimensional structures of proteins demonstrates the effectiveness of this method in identifying DNA-binding sites from sequence information. In 33% (56 out of 171) of the proteins, the classifier identifies the interaction sites by correctly recognizing at least half of the interface residues. In 87% (149 out of 171) of the proteins, the classifier correctly identifies at least 20% of the interface residues. This suggests the possibility of using such classifiers to identify potential DNA-binding motifs and to gain potentially useful insights into sequence correlates of protein-DNA interactions. CONCLUSION: Naïve Bayes classifiers trained to identify DNA-binding residues using sequence information offer a computationally efficient approach to identifying putative DNA-binding sites in DNA-binding proteins and recognizing potential DNA-binding motifs

Identifying Interaction Sites in Recalcitrant Proteins: Predicted Protein and RNA Binding Sites in Rev Proteins of HIV-1 and EIAV Agree with Experimental Data

Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the protein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-1 and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies

RNABindR: a server for analyzing and predicting RNA-binding sites in proteins

Understanding interactions between proteins and RNA is key to deciphering the mechanisms of many important biological processes. Here we describe RNABindR, a web-based server that identifies and displays RNA-binding residues in known protein–RNA complexes and predicts RNA-binding residues in proteins of unknown structure. RNABindR uses a distance cutoff to identify which amino acids contact RNA in solved complex structures (from the Protein Data Bank) and provides a labeled amino acid sequence and a Jmol graphical viewer in which RNA-binding residues are displayed in the context of the three-dimensional structure. Alternatively, RNABindR can use a Naive Bayes classifier trained on a non-redundant set of protein–RNA complexes from the PDB to predict which amino acids in a protein sequence of unknown structure are most likely to bind RNA. RNABindR automatically displays ‘high specificity’ and ‘high sensitivity’ predictions of RNA-binding residues. RNABindR is freely available at http://bindr.gdcb.iastate.edu/RNABindR