Structured RNAs in the ENCODE selected regions of the human genome.

Functional RNA structures play an important role both in the context of noncoding RNA transcripts as well as regulatory elements in mRNAs. Here we present a computational study to detect functional RNA structures within the ENCODE regions of the human genome. Since structural RNAs in general lack characteristic signals in primary sequence, comparative approaches evaluating evolutionary conservation of structures are most promising. We have used three recently introduced programs based on either phylogenetic-stochastic context-free grammar (EvoFold) or energy directed folding (RNAz and AlifoldZ), yielding several thousand candidate structures (corresponding to approximately 2.7% of the ENCODE regions). EvoFold has its highest sensitivity in highly conserved and relatively AU-rich regions, while RNAz favors slightly GC-rich regions, resulting in a relatively small overlap between methods. Comparison with the GENCODE annotation points to functional RNAs in all genomic contexts, with a slightly increased density in 3'-UTRs. While we estimate a significant false discovery rate of approximately 50%-70% many of the predictions can be further substantiated by additional criteria: 248 loci are predicted by both RNAz and EvoFold, and an additional 239 RNAz or EvoFold predictions are supported by the (more stringent) AlifoldZ algorithm. Five hundred seventy RNAz structure predictions fall into regions that show signs of selection pressure also on the sequence level (i.e., conserved elements). More than 700 predictions overlap with noncoding transcripts detected by oligonucleotide tiling arrays. One hundred seventy-five selected candidates were tested by RT-PCR in six tissues, and expression could be verified in 43 cases (24.6%).

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.

Multivariate QST-FST comparisons: a neutrality test for the evolution of the g matrix in structured populations.

Neutrality tests in quantitative genetics provide a statistical framework for the detection of selection on polygenic traits in wild populations. However, the existing method based on comparisons of divergence at neutral markers and quantitative traits (Q(st)-F(st)) suffers from several limitations that hinder a clear interpretation of the results with typical empirical designs. In this article, we propose a multivariate extension of this neutrality test based on empirical estimates of the among-populations (D) and within-populations (G) covariance matrices by MANOVA. A simple pattern is expected under neutrality: D = 2F(st)/(1 - F(st))G, so that neutrality implies both proportionality of the two matrices and a specific value of the proportionality coefficient. This pattern is tested using Flury's framework for matrix comparison [common principal-component (CPC) analysis], a well-known tool in G matrix evolution studies. We show the importance of using a Bartlett adjustment of the test for the small sample sizes typically found in empirical studies. We propose a dual test: (i) that the proportionality coefficient is not different from its neutral expectation [2F(st)/(1 - F(st))] and (ii) that the MANOVA estimates of mean square matrices between and among populations are proportional. These two tests combined provide a more stringent test for neutrality than the classic Q(st)-F(st) comparison and avoid several statistical problems. Extensive simulations of realistic empirical designs suggest that these tests correctly detect the expected pattern under neutrality and have enough power to efficiently detect mild to strong selection (homogeneous, heterogeneous, or mixed) when it is occurring on a set of traits. This method also provides a rigorous and quantitative framework for disentangling the effects of different selection regimes and of drift on the evolution of the G matrix. We discuss practical requirements for the proper application of our test in empirical studies and potential extensions.

HLA-Bw4 identifies a population of HIV-infected patients with an increased capacity to control viral replication after structured treatment interruption.

OBJECTIVES: After structured treatment interruption (STI) of treatment for HIV-1, a fraction of patients maintain suppressed viral loads. Prospective identification of such patients might improve HIV-1 treatment, if selected patients are offered STI. METHODS: We analysed the effect of previously identified genetic modulators of HIV-1 disease progression on patients' ability to suppress viral replication after STI. Polymorphisms in the genes killer cell immunoglobulin-like receptor 3DLI (KIR3DL1)/KIR3DS1, human leucocyte antigen B (HLA-B) and HLA Complex P5 (HCP5), and a polymorphism affecting HLA-C surface expression were analysed in 130 Swiss HIV Cohort Study patients undergoing STI. Genotypes were correlated with viral load levels after STI. RESULTS: We observed a statistically significant reduction in viral load after STI in carriers of HLA-B alleles containing either the Bw480Thr or the Bw480Ile epitope (mean adjusted effect on post-STI viral load: -0.82 log HIV-1 RNA copies/ml, P < 0.001; and -1.12 log copies/ml, P < 0.001, respectively). No significant effects were detected for the other polymorphisms analysed. The likelihood of being able to control HIV-1 replication using a prespecified cut-off (viral load increase < 1000 copies/ml) increased from 39% in Bw4-negative patients to 53% in patients carrying Bw4-80Thr, and to 65% in patients carrying Bw4-80Ile (P = 0.02). CONCLUSIONS: These data establish a significant impact of HLA-Bw4 on the control of viral replication after STI.

Genome-Wide Prediction Methods in Highly Diverse and Heterozygous Species: Proof-of-Concept through Simulation in Grapevine.

Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.

The stationary distribution of a continuously varying strategy in a class-structured population under mutation-selection-drift balance.

Many traits and/or strategies expressed by organisms are quantitative phenotypes. Because populations are of finite size and genomes are subject to mutations, these continuously varying phenotypes are under the joint pressure of mutation, natural selection and random genetic drift. This article derives the stationary distribution for such a phenotype under a mutation-selection-drift balance in a class-structured population allowing for demographically varying class sizes and/or changing environmental conditions. The salient feature of the stationary distribution is that it can be entirely characterized in terms of the average size of the gene pool and Hamilton's inclusive fitness effect. The exploration of the phenotypic space varies exponentially with the cumulative inclusive fitness effect over state space, which determines an adaptive landscape. The peaks of the landscapes are those phenotypes that are candidate evolutionary stable strategies and can be determined by standard phenotypic selection gradient methods (e.g. evolutionary game theory, kin selection theory, adaptive dynamics). The curvature of the stationary distribution provides a measure of the stability by convergence of candidate evolutionary stable strategies, and it is evaluated explicitly for two biological scenarios: first, a coordination game, which illustrates that, for a multipeaked adaptive landscape, stochastically stable strategies can be singled out by letting the size of the gene pool grow large; second, a sex-allocation game for diploids and haplo-diploids, which suggests that the equilibrium sex ratio follows a Beta distribution with parameters depending on the features of the genetic system.

Refining abacavir hypersensitivity diagnoses using a structured clinical assessment and genetic testing in the Swiss HIV Cohort Study.

BACKGROUND: We aimed to assess the value of a structured clinical assessment and genetic testing for refining the diagnosis of abacavir hypersensitivity reactions (ABC-HSRs) in a routine clinical setting. METHODS: We performed a diagnostic reassessment using a structured patient chart review in individuals who had stopped ABC because of suspected HSR. Two HIV physicians blinded to the human leukocyte antigen (HLA) typing results independently classified these individuals on a scale between 3 (ABC-HSR highly likely) and -3 (ABC-HSR highly unlikely). Scoring was based on symptoms, onset of symptoms and comedication use. Patients were classified as clinically likely (mean score > or =2), uncertain (mean score > or = -1 and < or = 1) and unlikely (mean score < or = -2). HLA typing was performed using sequence-based methods. RESULTS: From 131 reassessed individuals, 27 (21%) were classified as likely, 43 (33%) as unlikely and 61 (47%) as uncertain ABC-HSR. Of the 131 individuals with suspected ABC-HSR, 31% were HLA-B*5701-positive compared with 1% of 140 ABC-tolerant controls (P < 0.001). HLA-B*5701 carriage rate was higher in individuals with likely ABC-HSR compared with those with uncertain or unlikely ABC-HSR (78%, 30% and 5%, respectively, P < 0.001). Only six (7%) HLA-B*5701-negative individuals were classified as likely HSR after reassessment. CONCLUSIONS: HLA-B*5701 carriage is highly predictive of clinically diagnosed ABC-HSR. The high proportion of HLA-B*5701-negative individuals with minor symptoms among individuals with suspected HSR indicates overdiagnosis of ABC-HSR in the era preceding genetic screening. A structured clinical assessment and genetic testing could reduce the rate of inappropriate ABC discontinuation and identify individuals at high risk for ABC-HSR.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Structured running program for health and performance promotion in hospital employees.

Introduction Our institution (University hospital) is encouraging physical activities for health through various popular sporting events in the city of Lausanne, the biggest of which is a road race of 2, 4, 10 and 20km. Objective To create an efficient and sustainable training program in preparation of the race for a group of motivated hospital employees without any prior experience with structured training and to identifying the benefits and limitations encountered.. Methods Subjects of various fitness levels were recruited by add and agreed to undergo lab and field testing before a 12-week 3 times/week running program, based on maximal aerobic speed (MAS-30/30 sec intervals), running technique exercises and endurance training. The interval session was the only one supervised. Their goal was the 10km (11 subjects) and the 20km (6 subjects). Results A group of 17 subjects (7 male and 10 female), mean age 36.6±7.3 years, VO2max 44.0±5.5 ml/kg/min, filed test interval MAS 15.1±2.4 km/h started the program. 2 were lost because of injury (while skiing). Adherence to interval sessions was excellent, although 3 weekly training sessions proved to be difficult for most of the subjects. Performance in the race was satisfying for all of them, 6/7 subjects having improved their running time from the previous year, the others participated for the first time and 7/8 completed the race satisfyingly, one DNF-ed because of sinusitis. Repeat MAS field test was available for 6 subjects, who improved by 5.9% (p<0.01). Subjectively, all of the participants were very satisfied with improvement, interaction with colleagues from various professions, and with self achievement and confidence. Conclusions Implementation of a structured training program for recreational or non-athletes can be very successful in creating a better self-confidence, a better working environment inside a hospital facility and obviously in improvement of physical fitness and athletic performance. Above all, it can only encourage health institutions to promote the health of their own employees through physical activity, which can allow people to connect through sports. As a result, subjects in this study tend to encourage other employees to be more active and are hungry for more advice and continued offers for physical activities benefiting both them and the institution through better efficiency at work and less absenteeism common to more active people.

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.

We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.

Exploration of the visual system : Part 2. In vivo analysis methods : virtual-reality optomotor system, fundus examination and fluorescent angiography

The mouse has emerged as an animal model for many diseases. At IRO, we have used this animal to understand the development of many eye diseases and treatment of some of them. Precise evaluation of vision is a prerequisite for both these approaches. In this unit we describe three ways to measure vision: testing the optokinetic response, and evaluating the fundus by direct observation and by fluorescent angiography.

Liquid chromatography-tandem mass spectrometry (LC/APCI-MS/MS) methods for the quantification of captan and folpet phthalimide metabolites in human plasma and urine.

Captan and folpet are fungicides largely used in agriculture. They have similar chemical structures, except that folpet has an aromatic ring unlike captan. Their half-lives in blood are very short, given that they are readily broken down to tetrahydrophthalimide (THPI) and phthalimide (PI), respectively. Few authors measured these biomarkers in plasma or urine, and analysis was conducted either by gas chromatography coupled to mass spectrometry or liquid chromatography with UV detection. The objective of this study was thus to develop simple, sensitive and specific liquid chromatography-atmospheric pressure chemical ionization-tandem mass spectrometry (LC/APCI-MS/MS) methods to quantify both THPI and PI in human plasma and urine. Briefly, deuterated THPI was added as an internal standard and purification was performed by solid-phase extraction followed by LC/APCI-MS/MS analysis in negative ion mode for both compounds. Validation of the methods was conducted using spiked blank plasma and urine samples at concentrations ranging from 1 to 250 μg/L and 1 to 50 μg/L, respectively, along with samples of volunteers and workers exposed to captan or folpet. The methods showed a good linearity (R (2) > 0.99), recovery (on average 90% for THPI and 75% for PI), intra- and inter-day precision (RSD, <15%) and accuracy (<20%), and stability. The limit of detection was 0.58 μg/L in urine and 1.47 μg/L in plasma for THPI and 1.14 and 2.17 μg/L, respectively, for PI. The described methods proved to be accurate and suitable to determine the toxicokinetics of both metabolites in human plasma and urine.

Quantifying consistency of Event Related Potentials across subjects based on single-trial topographic analysis

Introduction: Non-invasive brain imaging techniques often contrast experimental conditions across a cohort of participants, obfuscating distinctions in individual performance and brain mechanisms that are better characterised by the inter-trial variability. To overcome such limitations, we developed topographic analysis methods for single-trial EEG data [1]. So far this was typically based on time-frequency analysis of single-electrode data or single independent components. The method's efficacy is demonstrated for event-related responses to environmental sounds, hitherto studied at an average event-related potential (ERP) level. Methods: Nine healthy subjects participated to the experiment. Auditory meaningful sounds of common objects were used for a target detection task [2]. On each block, subjects were asked to discriminate target sounds, which were living or man-made auditory objects. Continuous 64-channel EEG was acquired during the task. Two datasets were considered for each subject including single-trial of the two conditions, living and man-made. The analysis comprised two steps. In the first part, a mixture of Gaussians analysis [3] provided representative topographies for each subject. In the second step, conditional probabilities for each Gaussian provided statistical inference on the structure of these topographies across trials, time, and experimental conditions. Similar analysis was conducted at group-level. Results: Results show that the occurrence of each map is structured in time and consistent across trials both at the single-subject and at group level. Conducting separate analyses of ERPs at single-subject and group levels, we could quantify the consistency of identified topographies and their time course of activation within and across participants as well as experimental conditions. A general agreement was found with previous analysis at average ERP level. Conclusions: This novel approach to single-trial analysis promises to have impact on several domains. In clinical research, it gives the possibility to statistically evaluate single-subject data, an essential tool for analysing patients with specific deficits and impairments and their deviation from normative standards. In cognitive neuroscience, it provides a novel tool for understanding behaviour and brain activity interdependencies at both single-subject and at group levels. In basic neurophysiology, it provides a new representation of ERPs and promises to cast light on the mechanisms of its generation and inter-individual variability.

New methods for detecting antibiotic resistant bacteria and preventing their spread

Methicillin resistant Staphylococcus aureus (MRSA) bacteria have emerged in the early 1980's in numerous health care institutions around the world. The main transmission mechanism within hospitals and healthcare facilities is through the hands of health care workers. Resistant to several antibiotics, the MRSA is one of the most feared pathogens in the hospital setting since it is very difficult to eradicate with the standard treatments. There are still a limited number of anti-MRSA antibiotics but the first cases of resistance to these compounds have already been reported and their frequency is likely to increase in the coming years. Every year, the MRSA infections result in major human and financial costs, due to the high associated mortality and expenses related to the required care. Measures towards a faster detection of resistant bacteria and establishment of appropriate antibiotic treatment parameters are fundamental. Also as part as infection prevention, diminution of bacteria present on the commonly touched surfaces could also limit the spread and selection of antibiotic resistant bacteria. During my thesis, projects were developed around MRSA and antibiotic resistance investigation using innovative technologies. The thesis was subdivided in three main parts with the use of atomic force microscopy AFM for antibiotic resistance detection in part 1, the importance of the bacterial inoculum size in the selection of antibiotic resistance in part 2 and the testing of antimicrobial surfaces creating by sputtering copper onto polyester in part 3. In part 1 the AFM was used two different ways, first for the measurement of stiffness (elasticity) of bacteria and second as a nanosensor for antibiotic susceptibility testing. The stiffness of MRSA with different susceptibility profiles to vancomycin was investigated using the stiffness tomography mode of the AFM and results have demonstrated and increased stiffness in the vancomycin resistant strains that also paralleled with increased thickness of the bacterial cell wall. Parts of the AFM were also used to build a new antibiotic susceptibility-testing device. This nano sensor was able to measure vibrations emitted from living bacteria that ceased definitively upon antibiotic exposure to which they were susceptible but restarted after antibiotic removal to which they were resistant, allowing in a matter of minute the assessment of antibiotic susceptibility determination. In part 2 the inoculum effect (IE) of vancomycin, daptomycin and linezolid and its importance in antibiotic resistance selection was investigated with MRSA during a 15 days of cycling experiment. Results indicated that a high bacterial inoculum and a prolonged antibiotic exposure were two key factors in the in vitro antibiotic resistance selection in MRSA and should be taken into consideration when choosing the drug treatment. Finally in part 3 bactericidal textile surfaces were investigated against MRSA. Polyesters coated after 160 seconds of copper sputtering have demonstrated a high bactericidal activity reducing the bacterial load of at least 3 logio after one hour of contact. -- Au cours des dernières décennies, des bactéries multirésistantes aux antibiotiques (BMR) ont émergé dans les hôpitaux du monde entier. Depuis lors, le nombre de BMR et la prévalence des infections liées aux soins (IAS) continuent de croître et sont associés à une augmentation des taux de morbidité et de mortalité ainsi qu'à des coûts élevés. De plus, le nombre de résistance à différentes classes d'antibiotiques a également augmenté parmi les BMR, limitant ainsi les options thérapeutiques disponibles lorsqu'elles ont liées a des infections. Des mesures visant une détection plus rapide des bactéries résistantes ainsi que l'établissement des paramètres de traitement antibiotiques adéquats sont primordiales lors d'infections déjà présentes. Dans une optique de prévention, la diminution des bactéries présentes sur les surfaces communément touchées pourrait aussi freiner la dissémination et l'évolution des bactéries résistantes. Durant ma thèse, différents projets incluant des nouvelles technologies et évoluant autour de la résistance antibiotique ont été traités. Des nouvelles technologies telles que le microscope à force atomique (AFM) et la pulvérisation cathodique de cuivre (PCC) ont été utilisées, et le Staphylococcus aureus résistant à la méticilline (SARM) a été la principale BMR étudiée. Deux grandes lignes de recherche ont été développées; la première visant à détecter la résistance antibiotique plus rapidement avec l'AFM et la seconde visant à prévenir la dissémination des BMR avec des surfaces crées grâce à la PCC. L'AFM a tout d'abord été utilisé en tant que microscope à sonde locale afin d'investiguer la résistance à la vancomycine chez les SARMs. Les résultats ont démontré que la rigidité de la paroi augmentait avec la résistance à la vancomycine et que celle-ci corrélait aussi avec une augmentation de l'épaisseur des parois, vérifiée grâce à la microscopie électronique. Des parties d'un AFM ont été ensuite utilisées afin de créer un nouveau dispositif de test de sensibilité aux antibiotiques, un nanocapteur. Ce nanocapteur mesure des vibrations produites par les bactéries vivantes. Après l'ajout d'antibiotique, les vibrations cessent définitivement chez les bactéries sensibles à l'antibiotique. En revanche pour les bactéries résistantes, les vibrations reprennent après le retrait de l'antibiotique dans le milieu permettant ainsi, en l'espace de minutes de détecter la sensibilité de la bactérie à un antibiotique. La PCC a été utilisée afin de créer des surfaces bactéricides pour la prévention de la viabilité des BMR sur des surfaces inertes. Des polyesters finement recouverts de cuivre (Cu), connu pour ses propriétés bactéricides, ont été produits et testés contre des SARMs. Une méthode de détection de viabilité des bactéries sur ces surfaces a été mise au point, et les polyesters obtenus après 160 secondes de pulvérisation au Cu ont démontré une excellente activité bactéricide, diminuant la charge bactérienne d'au moins 3 logio après une heure de contact. En conclusion, l'utilisation de nouvelles technologies nous a permis d'évoluer vers de méthodes de détection de la résistance antibiotique plus rapides ainsi que vers le développement d'un nouveau type de surface bactéricide, dans le but d'améliorer le diagnostic et la gestion des BMR.