Abstract: An identification by mass spectrometry of a microorganism from among reference microorganisms represented by reference data sets includes: determining a set of data of the microorganism according to a spectrum; for each reference microorganism, calculating a distance between the determined and reference sets; and calculating a probability ƒ(m) according to relation f ? ( m ) = pN ? ( m | ? , ? ) pN ? ( m | ? , ? ) + ( 1 - p ) ? N ? ( m | ? _ , ? _ ) where: m is the distance calculated for the reference microorganism; N(m|?,?) is the value, for m, of a random variable modeling the distance between a reference microorganism to be identified and the reference microorganism, when the microorganism is the reference microorganism; N(m|?,?) is the value, for m, of a random variable modeling the distance between a microorganism to be identified and the reference microorganism, when the microorganism is not the reference microorganism; and p is a scalar in the range fr

Abstract: A method and device for compressing audio signals forming, over time, a succession of sample frames, in each of N channels of an ambisonic representation of order higher than 0. The method includes: forming, based on the channels and for a current frame, a matrix of inter-channel covariance, and searching for eigenvectors of the covariance matrix with a view to obtaining a matrix of eigenvectors; testing the matrix of eigenvectors to verify that it represents a rotation in an N-dimensional space, and if not, correcting the matrix of eigenvectors until a rotation matrix is obtained, for the current frame; and applying the rotation matrix to the signals of the N channels before separate-channel encoding of the signals.

Abstract: A method for classifying at least one input image representing a target particle in a sample, involves implementing, by data processing of a client, steps of: (b) extracting the characteristic map of the target particle by a convolutional neural network pre-trained on a base of public images; (c) classifying the input image according to the extracted characteristic map.

Abstract: A method for classifying at least one input image representing a target particle in a sample involves implementing, by data processing a client, steps of: (B) extracting a characteristic map of the target particle from the input image; (c) reducing the number of variables in the extracted characteristic map, using the t-SNE algorithm; (d) classifying, unsupervised, the input image based on the characteristic map having a reduced number of variables.

Abstract: A method for classifying a sequence of input images representing a target particle in a sample over time, includes the following steps performed by the data processing of a client, namely: (b) concatenation of the input images in the sequence as a three-dimensional stack; (c) direct classification of the three-dimensional stack using a convolutional neural network, CNN

Abstract: A method for classifying at least one input image containing a target particle in a sample, involves implementing, via data-processing of a client, steps of: (b) extracting a vector of characteristics of the target particle, the characteristics being numerical coefficients each associated with one elementary image of a set of elementary images each representing a reference particle, such that a linear combination of the elementary images weighted by the coefficients approximates the representation of the target particle in the input image; (c) classifying the input image depending on the extracted vector of characteristics.

Abstract: A method for identifying a pathogen contained in a metagenomic sample and for identifying pathogenic markers in the genome of the pathogen includes: processing the sample to extract DNA from pathogens, sequencing the extracted DNA, thereby producing a set of reads, comparing the reads to a database of genomes of known pathogens to assign reads to the pathogens; producing a pool of reads and assembling them to produce contigs, comparing the contigs to a second database of markers to check whether they contain a marker. The method further includes the step of comparing the reads to the second database to assign reads to the markers, a read being assigned to a marker if it falls entirely into or is astride the marker, and the pool also includes the reads assigned to the markers, the contigs thereby being assembled from reads assigned to a pathogen and reads assigned to markers.

Abstract: A method for quantifying the sensibility of a test microorganism to a concentration of an antimicrobial agent includes: preparing two liquid samples including the microorganism, one having the antimicrobial agent and one without; for each sample acquiring, by a flow cytometer, a digital set values including a fluorescence, forward, or side scatter distribution, and computing: a first coordinate value corresponding to the acquired distribution main mode and an acquired distribution first area for values greater than the first coordinate value, and a second coordinate value, greater than the first, for which an acquired distribution second area between the values equals a first area predefined percentage over 50%; computing a ratio according to: Q = QT ? ( ATB ) - Mode ? ( ATB ) QT ? ( no ? ? ATB ) - Mode ? ( no ? ? ATB ) where Mode(ATB) and QT(ATB) are the first and second coordinate values with the antimicrobial agent concentration, and Mode(no ATB) and QT(no ATB) are r

Abstract: A process of determining a phenotypic trait of a bacterial strain comprises sequencing part or all of the genome of the strain, and applying to the sequenced genome a predetermined model for predicting the trait, the model having groups of genome sequences as variables. According to the invention, the groups are chosen so that a co-occurrence rate in the genome of the bacterial species of the constituent genome sequences of the groups is above a predetermined threshold, and the groups are clusterings of the genome sequences according to their co-occurrence rates in the genome of the bacterial species.

Abstract: A computer-implemented method for detecting a genome sequence in digital form in a genome of a microorganism in digital form, including:—storing, in a computer memory, a set of digital genome sequences of constant length k, or ‘k-mers’, the set being obtained by sliding, at a constant pitch, a window of length k over the genome sequence;—for each k-mer, determining its absence or presence in the genome;—determining that the genome sequence is present in the genome if the percentage of k-mers detected as present in the genome is higher than a predetermined threshold.

Abstract: A method for determining a quantity Ginhib quantifying the inhibitory capacity of a molecule on a type of microorganism includes: preparing a plurality of samples, including microorganisms of the type, a nutrient medium for the microorganism and an initial amount of the molecule per microorganism increasing in a range [Qmin,Qmax] as a function of a classification of the samples; measuring the growth of the microorganisms in the samples as a function of time; and determining the quantity Ginhib as a function of the measurements of the growth. Determination of the quantity Ginhib includes: for each sample, calculating a value reflecting the growth of the microorganism of said type based on measurements of growth; classifying the values calculated for the samples as a function of the classification of the samples; and determining the quantity Ginhib as a function of the variation of the classified values.

Abstract: A detection of the Gram type of a bacterial strain includes: illumination, in the wavelength range 415 nm-440 nm, of at least one bacterium of said strain having a natural electromagnetic response in said range; acquisition, in the range 415 nm-440 nm, of a light intensity reflected by, or transmitted through, said illuminated bacterium; and determination of the Gram type of the bacterial strain as a function of the light intensity acquired in the range 415 nm-440 nm.

Abstract: A method and device for compressing audio signals forming, over time, a succession of sample frames, in each of N channels of an ambisonic representation of order higher than 0. The method includes: forming, based on the channels and for a current frame, a matrix of inter-channel covariance, and searching for eigenvectors of the covariance matrix with a view to obtaining a matrix of eigenvectors; testing the matrix of eigenvectors to verify that it represents a rotation in an N-dimensional space, and if not, correcting the matrix of eigenvectors until a rotation matrix is obtained, for the current frame; and applying the rotation matrix to the signals of the N channels before separate-channel encoding of the signals.

Abstract: An identification by mass spectrometry of a microorganism from among reference microorganisms represented by reference data sets includes: determining a set of data of the microorganism according to a spectrum; for each reference microorganism, calculating a distance between the determined and reference sets; and calculating a probability f(m) according to relation f ? ( m ) = pN ? ( m | ? , ? ) pN ? ( m | ? , ? ) + ( 1 - p ) ? N ? ( m | ? _ , ? _ ) where: m is the distance calculated for the reference microorganism; N(m|?,?) is the value, for m, of a random variable modeling the distance between a reference microorganism to be identified and the reference microorganism, when the microorganism is the reference microorganism; N(m|?,?) is the value, for m, of a random variable modeling the distance between a microorganism to be identified and the reference microorganism, when the microorganism is not the reference microorganism; and p is a scalar in the range fr

Abstract: An identification by mass spectrometry of a microorganism from among reference microorganisms represented by reference data sets includes: determining a set of data of the microorganism according to a spectrum; for each reference microorganism, calculating a distance between the determined and reference sets; and calculating a probability f(m) according to relation f ? ( m ) = pN ? ( m ? ? , ? ) pN ? ( m ? ? , ? ) + ( 1 - p ) ? N ? ( m ? ? _ , ? _ ) where: m is the distance calculated for the reference microorganism; N(m|?,?) is the value, for m, of a random variable modeling the distance between a reference microorganism to be identified and the reference microorganism, when the microorganism is the reference microorganism; N(m|?,?) is the value, for m, of a random variable modeling the distance between a microorganism to be identified and the reference microorganism, when the microorganism is not the reference microorganism; and p is a scalar in the range fr

Abstract: A method for predicting the sensibility phenotype of a test microorganism to an antimicrobial agent amongst susceptible, intermediate and resistant phenotypes, including a learning stage and a prediction stage. The learning stage includes selecting a wide set of different strains having different known sensibility phenotypes determined according EUCAST or CLSI method, acquiring FCM distributions for each of the strain alicoted in liquid samples with fluorescent markers and different concentrations of the antibiotic, and performing a learning machine computing on mono or multidimensional spaces derived from the FCM acquisition to derive a prediction model of the sensibility phenotype to the antibiotic.

Abstract: A method for quantifying the sensibility of a test microorganism to a concentration of an antimicrobial agent includes: preparing two liquid samples including the microorganism, one having the antimicrobial agent and one without; for each sample acquiring, by a flow cytometer, a digital set values including a fluorescence, forward, or side scatter distribution, and computing: a first coordinate value corresponding to the acquired distribution main mode and an acquired distribution first area for values greater than the first coordinate value, and a second coordinate value, greater than the first, for which an acquired distribution second area between the values equals a first area predefined percentage over 50%; computing a ratio according to: Q = QT ? ( ATB ) - Mode ? ( ATB ) QT ? ( no ? ? ATB ) - Mode ? ( no ? ? ATB ) where Mode(ATB) and QT(ATB) are the first and second coordinate values with the antimicrobial agent concentration, and Mode(no ATB) and QT(no ATB) are r

Abstract: A detection of the Gram type of a bacterial strain includes: illumination, in the wavelength range 415 nm-440 nm, of at least one bacterium of said strain having a natural electromagnetic response in said range; acquisition, in the range 415 nm-440 nm, of a light intensity reflected by, or transmitted through, said illuminated bacterium; and determination of the Gram type of the bacterial strain as a function of the light intensity acquired in the range 415 nm-440 nm.

Abstract: A method of identifying by spectrometry unknown microorganisms from among a set of reference species, including a first step of supervised learning of a classification model of the reference species, a second step of predicting an unknown microorganism to be identified, including acquiring a spectrum of the unknown microorganism; and inferring from said spectrum and to the classification model at least one type of microorganism to which the unknown microorganism belong. The classification model is calculated by means of a structured multi-class SVM algorithm applied to the nodes of a tree-like hierarchical representation of the reference species in terms of evolution and/or of clinical phenotype and having margin constraints including so-called “loss” functions quantifying a proximity between the tree nodes.

Abstract: A method for identifying a pathogen contained in a metagenomic sample and for identifying pathogenic markers in the genome of the pathogen includes: processing the sample to extract DNA from pathogens, sequencing the extracted DNA, thereby producing a set of reads, comparing the reads to a database of genomes of known pathogens to assign reads to the pathogens; producing a pool of reads and assembling them to produce contigs, comparing the contigs to a second database of markers to check whether they contain a marker. The method further includes the step of comparing the reads to the second database to assign reads to the markers, a read being assigned to a marker if it falls entirely into or is astride the marker, and the pool also includes the reads assigned to the markers, the contigs thereby being assembled from reads assigned to a pathogen and reads assigned to markers.