Astrocytes reverted to a neural progenitor-like state with transforming growth factor alpha are sensitized to cancerous transformation.

International audienceGliomas, the most frequent primitive central nervous system tumors, have been suggested to originate from astrocytes or from neural progenitors/stem cells. However, the precise identity of the cells at the origin of gliomas remains a matter of debate because no pre-neoplastic state has been yet identified. Transforming growth factor (TGF)-alpha, an epidermal growth factor family member, is frequently overexpressed in the early stages of glioma progression. We previously demonstrated that prolonged exposure of astrocytes to TGF-alpha is sufficient to trigger their reversion to a neural progenitor-like state. To determine whether TGF-alpha dedifferentiating effects are associated with cancerous transforming effects, we grafted intracerebrally dedifferentiated astrocytes. We show that these cells had the same cytogenomic profile as astrocytes, survived in vivo, and did not give birth to tumors. When astrocytes dedifferentiated with TGF-alpha were submitted to oncogenic stress using gamma irradiation, they acquired cancerous properties: they were immortalized, showed cytogenomic abnormalities, and formed high-grade glioma-like tumors after brain grafting. In contrast, irradiation did not modify the lifespan of astrocytes cultivated in serum-free medium. Addition of TGF-alpha after irradiation did not promote their transformation but decreased their lifespan. These results demonstrate that reversion of mature astrocytes to an embryonic state without genomic manipulation is sufficient to sensitize them to oncogenic stress

Clinical Relevance of Tumor Cells with Stem-Like Properties in Pediatric Brain Tumors

BACKGROUND: Primitive brain tumors are the leading cause of cancer-related death in children. Tumor cells with stem-like properties (TSCs), thought to account for tumorigenesis and therapeutic resistance, have been isolated from high-grade gliomas in adults. Whether TSCs are a common component of pediatric brain tumors and are of clinical relevance remains to be determined. METHODOLOGY/PRINCIPAL FINDINGS: Tumor cells with self-renewal properties were isolated with cell biology techniques from a majority of 55 pediatric brain tumors samples, regardless of their histopathologies and grades of malignancy (57% of embryonal tumors, 57% of low-grade gliomas and neuro-glial tumors, 70% of ependymomas, 91% of high-grade gliomas). Most high-grade glioma-derived oncospheres (10/12) sustained long-term self-renewal akin to neural stem cells (>7 self-renewals), whereas cells with limited renewing abilities akin to neural progenitors dominated in all other tumors. Regardless of tumor entities, the young age group was associated with self-renewal properties akin to neural stem cells (P = 0.05, chi-square test). Survival analysis of the cohort showed an association between isolation of cells with long-term self-renewal abilities and a higher patient mortality rate (P = 0.013, log-rank test). Sampling of low- and high-grade glioma cultures showed that self-renewing cells forming oncospheres shared a molecular profile comprising embryonic and neural stem cell markers. Further characterization performed on subsets of high-grade gliomas and one low-grade glioma culture showed combination of this profile with mesenchymal markers, the radio-chemoresistance of the cells and the formation of aggressive tumors after intracerebral grafting. CONCLUSIONS/SIGNIFICANCE: In brain tumors affecting adult patients, TSCs have been isolated only from high-grade gliomas. In contrast, our data show that tumor cells with stem cell-like or progenitor-like properties can be isolated from a wide range of histological sub-types and grades of pediatric brain tumors. They suggest that cellular mechanisms fueling tumor development differ between adult and pediatric brain tumors

DETECTION OF A GEAR COUPLING MISALIGNMENT IN A GEAR TESTING DEVICE

International audienceOn the gear testing device of the LIS laboratory (Grenoble, France), a total of 20 signals are synchronously recorded including shaft acceleration signals in several positions, torque, rotation speed, optical encoder signal of both shafts and currents and tensions of the non-synchronous training motor. After about 3500 hours of using this device, the driving gear shaft broke at the gear coupling position with the training motor shaft, which created an impressive helicoïdal crack . This failure could be due to a flexion strain caused by a misalignment of the two shafts, but this fault was not detected before. In this paper we compare results of different signal processing methods for the detection of this fault. Particularly, we use the phase spectrogram whose advantage is to be able to highlight a slight phase modulation in signals that is not detectable on the amplitude spectrogram. We also attempt to detect the fault using spectral analysis, a non-stationary modelling based on Prony's model and comparison of signals recorded at different times before the crack: the acceleration signals, the torque, and the torsional vibration signal obtained from the shaft encoder signal. The detection results are compared and discussed between the different methods

Maximum Likelihood Parameter Estimation Of Short-Time Multicomponent Signals With Nonlinear Am/Fm Modulation

International audienceParameter estimation for closely spaced or crossing frequency trajectories is a difficult signal processing problem, especially in the presence of both nonlinear amplitude and frequency modulations. In this paper, polynomial models are assumed for the instantaneous frequencies and amplitudes (IF/IA). We suggest two different strategies to process multicomponent signals. In the first one, which is optimal, all model parameters are simultaneously estimated using a maximum likelihood procedure (ML), maximized via a stochastic technique called Simulated Annealing (SA). In the second strategy, which is suboptimal, the signal is iteratively reconstructed component by component. At each iteration, the IF and IA of one component are estimated using the ML procedure and the SA technique. To evaluate the accuracy of the proposed strategies, Monte Carlo simulations are presented and compared to the derived Cramer-Rao Bounds for closely spaced and crossing frequency trajectories. The results show the proposed algorithms perform well compared to existing techniques

A AM/FM Single Component Signal Reconstruction using a Nonsequential Time Segmentation and Polynomial Modeling

International audienceThe problem of estimating nonstationary signals has been considered in many previous publications. In this paper we propose an alternative algorithm in order to accurately estimate AM/FM1 signals. Only single component signals are considered. We perform local polynomial modeling on short time segments using a nonsequential strategy. The degree of polynomial approximation is limited due to the shortness of each time segment. The time support of a segment is controlled by a criterion defined on the spectrogram. To keep optimality a maximum likelihood procedure estimates the local model parameters leading to a non linear equation system in R7. This is solved by a Simulated Annealing technique. Finally, the local polynomial models are merged to reconstruct the entire signal model. The proposed algorithm enables highly nonlinear AM/FM estimation and shows robustness even when Signal to Noise Ratio (SNR) is low. The appropriate Cramer Rao Bounds (CRB) are presented for both polynomial phase and amplitude signals. Monte Carlo simulations show that the proposed algorithm performs well. Finally, our proposed method is illustrated using both numerical simulations and a real signal of whale sound

Estimation et Reconstruction des Signaux Courts à Multicomposantes Modulées à la fois en Amplitude et en Fréquence

National audienceIn this paper, we consider nonstationary signals with nonlinear amplitude and frequency modulation on short time-windows. Motivated by published works [3, 4], where we obtain high performances for monocomponent signals, we propose to extend the approach to multicomponent signals. Both the instantaneous amplitude and frequency were modeled by polynomial functions. The maximization of the likelihood function was achieved using a stochastic optimization technique: the Simulated Annealing. We compare two different strategies. The first one, we simultaneously estimate all model parameters. This is a highly computational strategy. The second one consists in iteratively recontructing the signal component by component. At each iteration, the parameters of only one component are estimated. Monte Carlo simulations are presented and compared to the appropriate Cramer-Rao Bounds. It proves the efficiency of the algorithm. Moreover it underscores the performance to estimate crossing frequency trajectories which is a great challenge related to the low sample number

Estimation of the Amplitude and the Frequency of Nonstationary Short-time Signals

International audienceWe consider the modeling of non-stationary discrete signals whose amplitude and frequency are assumed to be nonlinearly modulated over very short-time duration. We investigate the case where both instantaneous amplitude and frequency can be approximated by orthonormal polynomials. Previous works dealing with polynomial approximations refer to orthonormal bases built from a discretization of continuous-time orthonormal polynomials. As this leads to a loss of the orthonormal property, we propose to use discrete or- thonormal polynomial bases: the discrete orthonormal Legendre polynomials and a discrete base we have derived using Gram-Schmidt procedure. We show that in the context of short-time signals the use of these discrete bases leads to a significant improvement in the estimation accuracy. We manage the model parameter estimation by applying two approaches. The first is maximization of the likelihood function. This function being highly nonlinear, we propose to apply a stochastic optimization technique based on the simulated annealing algorithm. The problem can also be considered as a Bayesian estimation which leads us to apply another stochastic technique based on Monte Carlo Markov Chains. We propose to use a Metropolis Hastings algorithm. Both approaches need an algorithm parameter tuning that we discuss according our application context. Monte Carlo simulations show that the results obtained are close to the Cramer-Rao bounds we have derived. We show that the first approach is less biased than the second one. We also compared our results with the Higher Ambiguity Function-based method. The methods proposed outperform this method at low signal to noise ratios in terms of estimation accuracy and robustness. Both proposed approaches are of a great utility when scenarios in which signals having a small sample size are non-stationary at low signal to noise ratios. They provide accurate system descriptions which are achieved with only a reduced number of basis functions

Estimation et Reconstruction des Signaux Courts à Multicomposantes Modulées à la fois en Amplitude et en Fréquence

National audienceIn this paper, we consider nonstationary signals with nonlinear amplitude and frequency modulation on short time-windows. Motivated by published works [3, 4], where we obtain high performances for monocomponent signals, we propose to extend the approach to multicomponent signals. Both the instantaneous amplitude and frequency were modeled by polynomial functions. The maximization of the likelihood function was achieved using a stochastic optimization technique: the Simulated Annealing. We compare two different strategies. The first one, we simultaneously estimate all model parameters. This is a highly computational strategy. The second one consists in iteratively recontructing the signal component by component. At each iteration, the parameters of only one component are estimated. Monte Carlo simulations are presented and compared to the appropriate Cramer-Rao Bounds. It proves the efficiency of the algorithm. Moreover it underscores the performance to estimate crossing frequency trajectories which is a great challenge related to the low sample number

A New Flexible Approach to Estimate Highly Nonstationary Signals of Long Time Duration

International audienceWe propose an original strategy for estimating and reconstructing mono-component signals having a high nonstationarity and long time-duration. We locally apply to short duration intervals the strategy developed in our previous work about non-stationary short-time signals. This paper describes a non-sequential time segmentation that provides segments whose lengths are suitable for modeling both the instantaneous amplitude and frequency locally with low-order polynomials. Parameter estimation is done independently for each segment by maximizing the likelihood function by means of the simulated annealing technique. The signal is then reconstructed by merging the estimated segments. The strategy proposed is sufficiently flexible for estimating a large variety of non-stationarity and specifically applicable to high-order polynomial phase signals. The estimation of a high-order model is not necessary. The error propagation phenomenon occurring with the known approach, the Higher Ambiguity Function-based method, is avoided. The proposed strategy is evaluated using Monte-Carlo noise simulations and compared with the Cramer-Rao bounds. The signal of a songbird is used as a real example of its applicability

A AM/FM Single Component Signal Reconstruction using a Nonsequential Time Segmentation and Polynomial Modeling

International audienceThe problem of estimating nonstationary signals has been considered in many previous publications. In this paper we propose an alternative algorithm in order to accurately estimate AM/FM1 signals. Only single component signals are considered. We perform local polynomial modeling on short time segments using a nonsequential strategy. The degree of polynomial approximation is limited due to the shortness of each time segment. The time support of a segment is controlled by a criterion defined on the spectrogram. To keep optimality a maximum likelihood procedure estimates the local model parameters leading to a non linear equation system in R7. This is solved by a Simulated Annealing technique. Finally, the local polynomial models are merged to reconstruct the entire signal model. The proposed algorithm enables highly nonlinear AM/FM estimation and shows robustness even when Signal to Noise Ratio (SNR) is low. The appropriate Cramer Rao Bounds (CRB) are presented for both polynomial phase and amplitude signals. Monte Carlo simulations show that the proposed algorithm performs well. Finally, our proposed method is illustrated using both numerical simulations and a real signal of whale sound