Terbinafine Resistance of Trichophyton Clinical Isolates Caused by Specific Point Mutations in the Squalene Epoxidase Gene.

Terbinafine is one of the allylamine antifungal agents whose target is squalene epoxidase (SQLE). This agent has been extensively used in the therapy of dermatophyte infections. The incidence of patients with tinea pedis or unguium tolerant to terbinafine treatment prompted us to screen the terbinafine resistance of all javax.xml.bind.JAXBElement@dc06fb4 clinical isolates from the laboratory of the Centre Hospitalier Universitaire Vaudois collected over a 3-year period and to identify their mechanism of resistance. Among 2,056 tested isolates, 17 (≈1%) showed reduced terbinafine susceptibility, and all of these were found to harbor javax.xml.bind.JAXBElement@374d721c gene alleles with different single point mutations, leading to single amino acid substitutions at one of four positions (Leu javax.xml.bind.JAXBElement@4655f570 , Phe javax.xml.bind.JAXBElement@112b804a , Phe javax.xml.bind.JAXBElement@1f18e014 , and His javax.xml.bind.JAXBElement@4319ac79 ) of the SQLE protein. Point mutations leading to the corresponding amino acid substitutions were introduced into the endogenous javax.xml.bind.JAXBElement@2a0e3f1f gene of a terbinafine-sensitive javax.xml.bind.JAXBElement@67eac3c4 (formerly javax.xml.bind.JAXBElement@3f2a876d ) strain. All of the generated javax.xml.bind.JAXBElement@315e9e95 transformants expressing mutated SQLE proteins exhibited obvious terbinafine-resistant phenotypes compared to the phenotypes of the parent strain and of transformants expressing wild-type SQLE proteins. Nearly identical phenotypes were also observed in javax.xml.bind.JAXBElement@6af3a966 transformants expressing mutant forms of javax.xml.bind.JAXBElement@5bb6b31f SQLE proteins. Considering that the genome size of dermatophytes is about 22 Mb, the frequency of terbinafine-resistant clinical isolates was strikingly high. Increased exposure to antifungal drugs could favor the generation of resistant strains

Multikernel convolutional neural network for sEMG based hand gesture classification

openIl riconoscimento dei gesti della mano è un argomento ampiamente discusso in letteratura, dove vengono analizzate diverse tecniche sia in termini di tipi di segnale in ingresso che di algoritmi. Tra i più utilizzati ci sono i segnali elettromiografici (sEMG), già ampiamente sfruttati nelle applicazioni di interazione uomo-macchina (HMI). Determinare come decodificare le informazioni contenute nei segnali EMG in modo robusto e accurato è un problema chiave per il quale è urgente trovare una soluzione. Recentemente, molti incarichi di riconoscimento dei pattern EMG sono stati affrontati utilizzando metodi di deep learning. Nonostante le elevate prestazioni di questi ultimi, le loro capacità di generalizzazione sono spesso limitate dall'elevata eterogeneità tra i soggetti, l'impedenza cutanea, il posizionamento dei sensori, ecc. Inoltre, poiché questo progetto è focalizzato sull'applicazione in tempo reale di protesi, ci sono maggiori vincoli sui tempi di risposta del sistema che riducono la complessità dei modelli. In questa tesi è stata testata una rete neurale convoluzionale multi-kernel su diversi dataset pubblici per verificare la sua generalizzabilità. Inoltre, è stata analizzata la capacità del modello di superare i limiti inter-soggetto e inter-sessione in giorni diversi, preservando i vincoli legati a un sistema embedded. I risultati confermano le difficoltà incontrate nell'estrazione di informazioni dai segnali emg; tuttavia, dimostrano la possibilità di ottenere buone prestazioni per un uso robusto di mani prostetiche. Inoltre, è possibile ottenere prestazioni migliori personalizzando il modello con tecniche di transfer learning e di adattamento al dominio.Hand gesture recognition is a widely discussed topic in the literature, where different techniques are analyzed in terms of both input signal types and algorithms. Among the most widely used are electromyographic signals (sEMG), which are already widely exploited in human-computer interaction (HMI) applications. Determining how to decode the information contained in EMG signals robustly and accurately is a key problem for which a solution is urgently needed. Recently, many EMG pattern recognition tasks have been addressed using deep learning methods. Despite their high performance, their generalization capabilities are often limited by high heterogeneity among subjects, skin impedance, sensor placement, etc. In addition, because this project is focused on the real-time application of prostheses, there are greater constraints on the system response times that reduce the complexity of the models. In this thesis, a multi-kernel convolutional neural network was tested on several public datasets to verify its generalizability. In addition, the model's ability to overcome inter-subject and inter-session constraints on different days while preserving the constraints associated with an embedded system was analyzed. The results confirm the difficulties encountered in extracting information from emg signals; however, they demonstrate the possibility of achieving good performance for robust use of prosthetic hands. In addition, better performance can be achieved by customizing the model with transfer learning and domain-adaptationtechniques

Lipoarabinomannan mannose caps do not affect mycobacterial virulence or the induction of protective immunity in experimental animal models of infection and have minimal impact on in vitro inflammatory responses

Mannose-capped lipoarabinomannan (ManLAM) is considered an important virulence factor of Mycobacterium tuberculosis. However, while mannose caps have been reported to be responsible for various immunosuppressive activities of ManLAMobserved in vitro, there is conflicting evidence about their contribution to mycobacterial virulence in vivo. Therefore, we used Mycobacterium bovis BCG and M.?tuberculosis mutants that lack the mannose cap of LAM to assess the role of ManLAM in the interaction of mycobacteria with the host cells, to evaluate vaccine-induced protection and to determine its importance in M.?tuberculosis virulence. Deletion of the mannose cap did not affect BCG survival and replication in macrophages, although the capless mutant induced a somewhat higher production of TNF. In dendritic cells, the capless mutant was able to induce the upregulation of co-stimulatory molecules and the only difference we detected was the secretion of slightly higher amounts of IL-10 as compared to the wild type strain. In mice, capless BCG survived equally well and induced an immune response similar to the parental strain. Furthermore, the efficacy of vaccination against a M. tuberculosis challenge in low-dose aerosol infection models in mice and guinea pigs was not affected by the absence of the mannose caps in the BCG. Finally, the lack of the mannose cap in M. tuberculosis did not affect its virulence in mice nor its interaction with macrophages in vitro. Thus, these results do not support a major role for the mannose caps of LAM in determining mycobacterial virulence and immunogenicity in vivo in experimental animal models of infection, possibly because of redundancy of function.This work was supported by grant ImmunovacTB, ref. 37388 of the FP6 from the European Union, the NEWTBVAC project, ref. 241745 of the FP7 from the EU and by a grant from the Gulbenkian Foundation and TBVI. AAB, GTR, SSG, CN and SVC were supported by fellowships from Fundacao para a Ciencia e a Tecnologia (FCT) from the Portuguese Government. FM was supported by Wellcome Trust grant 073237. JG is financially supported by the Netherlands Organization for Scientific Research (NWO) through a VENI research grant (016.101.001). AAB is enrolled in the PhD Program in Experimental Biology and Biomedicine (PDBEB), Center for Neuroscience and Cell Biology, University of Coimbra, Portugal. We thank Marion Sparrius, Amsterdam, for technical assistance