Graphene based nanosensor for aqueous phase detection of nitroaromatics

A graphene-based nanosensor was fabricated to selectively detect nitrotriazolone (NTO) molecules with a molecularly imprinted film via simple electrical measurements. Molecularly imprinted polymer comprising chitosan was used as sensitive layer. Gold electrodes for electrical measurements were lithographically fabricated on Si/SiO2 substrate, followed by monolayer graphene transfer and polymeric film coating. Monolayer graphene and molecularly imprinted polymer were characterized by ATR-FTIR, UV-Vis, SEM and Raman spectroscopy. Transfer-length measurements (TLM) indicate that the sensor selectively and linearly responds against aqueous NTO solutions within a wide range of concentration of 0.01-0.1 mg mL(-1) that covers the lowest toxic level of NTO determined by USEPA. This nanosensor with embedded electrodes is re-usable and suitable for field applications, offering real-time electrical measurements unlike current techniques where complex analytics are required

Classification d'expressions vocales passives versus actives

Six expressions sont généralement considérées pour caractériser les états émotifs humains : Sourire, Surprise, Colère, Tristesse, dégoût et Neutre. Différentes mesures peuvent être extraites à partir du signal de parole pour caractériser ces expressions, à savoir la fréquence fondamentale, l'énergie, le SPI (rapport des énergies des HF et des BF dans le signal) et le débit de parole. Une classification automatique des cinq expressions basées sur ces caractéristiques présente des conflits entre la Colère, la Surprise et le Sourire d'une part et le Neutre et la Tristesse d'autre part. Ce conflit entre classes d'expressions est également retrouvé chez le classifieur humain. Nous proposons donc de définir deux classes d'expressions: Active regroupant le Sourire, la Surprise et la Colère et Passive regroupant le Neutre et la Tristesse. Une telle classification est également plus réaliste et plus appropriée pour l'intégration d'information de parole dans un système de classification multimodale combinant la parole et la vidéo, ce qui est à long terme le but de notre travail. Dans ce papier, différentes méthodes de classification sont testées: un classifieur Bayésien, une Analyse Discriminante Linéaire (ADL), le classifieur au K plus proches vosins(KNN) et un classifieur à Machine à Vecteur de Support (SVM) avec une fonction de base gaussienne. Pour les deux classes considérées, les meilleurs taux de classification sont obtenus avec le classificateur SVM avec un taux de reconnaissance de 89.74% pour l'état Actif et de 86.54 % pour l'état Passif

Dynamic localization of a helper NLR at the plant-pathogen interface underpins pathogen recognition

Plants employ sensor-helper pairs of NLR immune receptors to recognize pathogen effectors and activate immune responses (1). Yet the subcellular localization of NLRs pre- and post-activation during pathogen infection remains poorly understood. Here we show that NRC4, from the ‘NRC’ solanaceous helper NLR family (1), undergoes dynamic changes in subcellular localization by shuttling to and from the plant-pathogen haustorium interface established during infection by the Irish potato famine pathogen Phytophthora infestans. Specifically, prior to activation, NRC4 accumulates at the extra-haustorial membrane (EHM), presumably to mediate response to perihaustorial effectors, that are recognized by NRC4- dependent sensor NLRs. However not all NLRs accumulate at the EHM, as the closely related helper NRC2, and the distantly related ZAR1, did not accumulate at the EHM. NRC4 required an intact N-terminal coiled coil domain to accumulate at the EHM, whereas the functionally conserved MADA motif implicated in cell death activation and membrane insertion was dispensable for this process. Strikingly, a constitutively autoactive NRC4 mutant did not accumulate at the EHM and showed punctate distribution that mainly associated with the plasma membrane, suggesting that post-activation, NRC4 may undergo a conformation switch to form clusters that do not preferentially associate with the EHM. When NRC4 is activated by a sensor NLR during infection however, NRC4 forms puncta mainly at the EHM and to a lesser extent at the plasma membrane. We conclude that following activation at the EHM, NRC4 may spread to other cellular membranes from its primary site of activation to trigger immune responses