A new mechanism to render clinical isolates of Escherichia coli non-susceptible to imipenem: substitutions in the PBP2 penicillin-binding domain

International audienceObjectives: So far, two types of mechanism are known to be involved in carbapenem non-susceptibility of Escherichia coli clinical isolates: reduced outer membrane permeability associated with production of ESBLs and/or overproduction of class C beta-lactamases; and production of carbapenemases. Non-susceptibility to only imipenem observed in two clinical isolates suggested a new mechanism, described in the present study.Methods: The ST was determined for the two isolates of E. coli (strains LSNy and VSBj), and their chromosomal region encoding the penicillin-binding domain of PBP2 was amplified, sequenced and then used for recombination experiments in E. coli K12 C600. Antibiotic MICs were determined using the Etest method.Results: Strains LSNy and VSBj, which displayed ST23 and ST345, respectively, showed amino acid substitutions in their PBP2 penicillin-binding domain. Substitution Ala388Ser located in motif 2 (SXD) was common to the two strains. Two additional substitutions (Ala488Thr and Leu573Val) located outside the two other motifs were identified in strain LSNy, whereas another one (Thr331Pro) located in motif 1 was identified in strain VSBj. Recombination experiments to reproduce non-susceptibility to imipenem in E. coli K12 C600 were not successful when only the common substitution was transferred, whereas recombination with DNA fragments including either the three substitutions (strain LSNy) or the two substitutions (strain VSBj) were successful.Conclusions: Substitution of amino acids in the penicillin-binding domain of PBP2 is a new mechanism by which E. coli clinical isolates specifically resist imipenem

Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models

The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models

Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing

Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop state during the vegetation period. Traditional ways of analytical BMP determination are based on fermentation trials and require a minimum of 30 days. Here, we present a faster method for BMP retrievals using near infrared spectroscopy and partial least square regression (PLSR). PLSR prediction models were developed based on two different sets of spectral reflectance data: (i) laboratory spectra of silage samples and (ii) airborne imaging spectra (HyMap) of maize canopies under field (in situ) conditions. Biomass was sampled from 35 plots covering different maize varieties and the BMP was determined as BMP per mass (BMPFM, Nm3 biogas/t fresh matter (Nm3/t FM)) and BMP per area (BMParea, Nm3 biogas/ha (Nm3/ha)). We found that BMPFM significantly differs among maize varieties; it could be well retrieved from silage samples in the laboratory approach (Rcv2 = 0.82, n = 35), especially at levels >190 Nm3/t. In the in situ approach PLSR prediction quality declined (Rcv2 = 0.50, n = 20). BMParea, on the other hand, was found to be strongly correlated with total biomass, but could not be satisfactorily predicted using airborne HyMap imaging data and PLSR

Assessment of factors influencing the biomethane yield of maize silages

A large set of maize silage samples was produced to assess the major traits influencing the biomethane production of this crop. The biomass yield, the volatile solids contents and the biochemical methane potential (BMP) were measured to calculate the biomethane yield per hectare (average=7266m3ha-1). The most influential factor controlling the biomethane yield was the cropping environment. The biomass yield had more impact than the anaerobic digestibility. Nevertheless, the anaerobic digestibility of maize silages was negatively affected by high VS content in mature maize. Late maturing maize varieties produced high biomass yield with high digestibility resulting in high biomethane yield per hectare. The BMP was predicted with good accuracy using solely the VS conte

First measurement of the in-pixel electron multiplying with a standard imaging CMOS technology: Study of the EMCMOS concept

International audienceScientific low light imaging devices benefit today from designs for pushing the mean noise to the single electron level. When readout noise reduction reaches its limit, signal-to-noise ratio improvement can be driven by an electron multiplication process, driven by impact ionization, before adding the readout noises. This concept already implemented in CCD structures using extra-pixel shift registers can today be integrated inside each pixel in CMOS technology. The EBCMOS group at IPNL is in charge of the characterization of new prototypes developed by E2V using this concept: the electron multiplying CMOS (EMCMOS). The CMOS technology enables electron multiplication inside the photodiode itself, and thus, an overlap of the charge integration and multiplication. A new modeling has been developed to describe the output signal mean and variance after the impact ionization process in such a case. In this paper the feasibility of impact ionization process inside a View the MathML source8μm-pitch pixel is demonstrated. The new modeling is also validated by data and a value of 0.32% is obtained for the impact ionization parameter α with an electric field intensity of View the MathML source24V/μm