L'ENDEMIE A BARTONELLA QUINTANA CHEZ LES SDF DE MARSEILLE

AIX-MARSEILLE2-BU Méd/Odontol. (130552103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities

International audienceHumans face a dynamic world that requires them to constantly update their knowledge. Each observation should influence their knowledge to a varying degree depending on whether it arises from a stochastic fluctuation or an environmental change. Thus, humans should dynamically adapt their learning rate based on each observation. Although crucial for characterizing the learning process, these dynamic adjustments have only been investigated empirically in magnitude learning. Another important type of learning is probability learning. The latter differs from the former in that individual observations are much less informative and a single one is insufficient to distinguish environmental changes from stochasticity. Do humans dynamically adapt their learning rate for probabilities? What determinants drive their dynamic adjustments in magnitude and probability learning? To answer these questions, we measured the subjects’ learning rate dynamics directly through real-time continuous reports during magnitude and probability learning. We found that subjects dynamically adapt their learning rate in both types of learning. After a change point, they increase their learning rate suddenly for magnitudes and prolongedly for probabilities. Their dynamics are driven differentially by two determinants: change-point probability, the main determinant for magnitudes, and prior uncertainty, the main determinant for probabilities. These results are fully in line with normative theory, both qualitatively and quantitatively. Overall, our findings demonstrate a remarkable human ability for dynamic adaptive learning under uncertainty, and guide studies of the neural mechanisms of learning, highlighting different determinants for magnitudes and probabilities

Detection of Linear Frequency Modulation, Phase-Coded and Multicarrier Radar Waveforms in Electronic Warfare Context

International audienceInterception of radar signals is analyzed. The waveforms of interest are the Linear Frequency Modulation (LFM), the Phase-Coded (PC) and multicarrier (MC) waveforms. The problematic is to perform detection with real-Time requirement and the possibility to integrate on the longest pulse width (LFM). The well-known ambiguity function is proposed as quadratic time-frequency detector which is able to help waveform recognition among the analyzed set of waveforms. Its detection performances are evaluated and some estimators are proposed to facilitate the intrapulse estimation step

Report of Two Fatal Cases of Mycobacterium mucogenicum Central Nervous System Infection in Immunocompetent Patients

Neurological infections due to rapidly growing mycobacteria (RGM) have rarely been reported. We recently investigated two unrelated immunocompetent patients, one with community-acquired lymphocytic meningitis and the other with cerebral thrombophlebitis. Mycobacterium mucogenicum was isolated in pure culture and detected by PCR sequencing of cerebrospinal fluid samples. Both patients eventually died. The two isolates exhibited an overlapping antimicrobial susceptibility pattern. They were susceptible in vitro to tetracyclines, macrolides, quinolones, amikacin, imipenem, cefoxitin, and trimethoprim-sulfamethoxazole and resistant to ceftriaxone. They shared 100% 16S rRNA gene sequence similarity with M. mucogenicum ATCC 49650(T) over 1,482 bp. Their partial rpoB sequences shared 97.8% and 98.1% similarity with M. mucogenicum ATCC 49650(T), suggesting that the two isolates were representative of two sequevars of M. mucogenicum species. This case report should make clinicians aware that M. mucogenicum, an RGM frequently isolated from tap water or from respiratory specimens and mostly without clinical significance, can even be encountered in the central nervous system of immunocompetent patients

Non-destructive technique to detect local buried defects in metal sample by scanning microwave microscopy

International audienceBased on the skin effect, our recent developments using scanning microwave microscopy lead to propose a non-destructive method to detect located buried defect in metal samples like stainless steel. A 3D tomography is possible by taking advantage of microwave measurement, using a vector network analyzer in bandwidth frequencies, and the nanometer resolution positioning capabilities with atomic force microscopy. At each used frequency, an incident electromagnetic wave is sent to the sample and the reflected wave gives information on a specific depth layer in the material. With diagnostic tools of nanotechnologies (SEM. AFM, etc.), different stainless steel samples (from Areva NP) presenting local buried defects are studied. The materials used in this study are conventional stainless steels like a 304 stainless steel which is the most versatile and widely used stainless steel. The results of the tomography applied on these samples are in accordance with the skin effect equation