Antimicrob Agents Chemother

A strain of the opportunistic pathogenic yeast was genetically modified for use as a cellular model for assessing by allele replacement the impact of lanosterol C14α-demethylase mutations on azole resistance. was chosen because it is susceptible to azole antifungals, it belongs to the CTG clade of yeast, which includes most of the species pathogenic for humans, and it is haploid and easily amenable to genetic transformation and molecular modeling. In this work, allelic replacement is targeted at the locus by the reconstitution of a functional auxotrophic marker in the 3' intergenic region of Homologous and heterologous alleles are expressed from the resident promoter of , allowing accurate comparison of the phenotypic change in azole susceptibility. As a proof of concept, we successfully expressed in different alleles, either bearing or not bearing mutations retrieved from a clinical context, from two phylogenetically distant yeasts, and constitutes a high-fidelity expression system, giving specific Erg11p-dependent fluconazole MICs very close to those observed with the donor strain. This work led us to characterize the phenotypic effect of two kinds of mutation: mutation conferring decreased fluconazole susceptibility in a species-specific manner and mutation conferring fluconazole resistance in several yeast species. In particular, a missense mutation affecting amino acid K143 of Erg11p in species, and the equivalent position K151 in , plays a critical role in fluconazole resistance

Calculation Methods for the Heat Release Rate of Materials of Unknown Composition

The Heat Release Rate (HRR) is a critical parameter to characterise a fire. Different methods have been developed to estimate it. The most widespread techniques are based on mass balance. If the heat of combustion of the fuel is known, the measure of the mass loss allows its evaluation. If the burning material can not be identified, calorimetric principles can be used. They rely on oxygen consumption (OC) or carbon dioxide and carbon monoxide generation (CDG) measurements. Their asset comes from the observation that the amount of energy release per unit mass of O2 consumed or per unit mass of CO2 produced is relatively constant for a large number of materials. Thus, an accurate HRR can be obtained without knowing the composition of the burning fuel. The aim of this work is to assess this last statement and define how essential the knowledge of the chemistry to calculate HRR for complex materials such as polymers including fire retardants and/or nanocomposites, energetic materials or pine needles is. This assessment ends in an OC and CDG calorimetry comparison of several materials in order to investigate the propensity to determine whether converging or diverging HRR results when average energy constants are used

Murine Models for Trypanosoma brucei gambiense Disease Progression—From Silent to Chronic Infections and Early Brain Tropism

Trypanosoma brucei gambiense is responsible for more than 90% of reported cases of human African trypanosomosis (HAT). Infection can last for months or even years without major signs or symptoms of infection, but if left untreated, sleeping sickness is always fatal. In the present study, different T. b. gambiense field isolates from the cerebrospinal fluid of patients with HAT were adapted to growth in vitro. These isolates belong to the homogeneous Group 1 of T. b. gambiense, which is known to induce a chronic infection in humans. In spite of this, these isolates induced infections ranging from chronic to silent in mice, with variations in parasitaemia, mouse lifespan, their ability to invade the CNS and to elicit specific immune responses. In addition, during infection, an unexpected early tropism for the brain as well as the spleen and lungs was observed using bioluminescence analysis. The murine models presented in this work provide new insights into our understanding of HAT and allow further studies of parasite tropism during infection, which will be very useful for the treatment and the diagnosis of the disease

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 1018.5, eV and 1020 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration

Combined fit to the spectrum and composition data measured by the Pierre Auger Observatory including magnetic horizon effects

The measurements by the Pierre Auger Observatory of the energy spectrum and mass composition of cosmic rays can be interpreted assuming the presence of two extragalactic source populations, one dominating the flux at energies above a few EeV and the other below. To fit the data ignoring magnetic field effects, the high-energy population needs to accelerate a mixture of nuclei with very hard spectra, at odds with the approximate E$^{-2}$ shape expected from diffusive shock acceleration. The presence of turbulent extragalactic magnetic fields in the region between the closest sources and the Earth can significantly modify the observed CR spectrum with respect to that emitted by the sources, reducing the flux of low-rigidity particles that reach the Earth. We here take into account this magnetic horizon effect in the combined fit of the spectrum and shower depth distributions, exploring the possibility that a spectrum for the high-energy population sources with a shape closer to E$^{-2}$ be able to explain the observations