Search

International audience; The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis-Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders.

The cell cycle is the fundamental process of cell populations, it is regulated by environmental cues and by intracellular checkpoints. Cell cycle variability in clonal cell population is caused by stochastic processes such as random partitioning of cellular components to progeny cells at division and random interactions among biomolecules in cells. One of the important biological questions is how the dynamics at the cell cycle scale, which is related to family dependencies between the cell and its descendants, affects cell population behavior in the long-run. We address this question using a “mechanistic” model, built based on observations of single cells over several cell generations, and then extrapolated in time. We used cell pedigree observations of NIH 3T3 cells including FUCCI markers, to determine patterns of inheritance of cell-cycle phase durations and single-cell protein dynamics. Based on that information we developed a hybrid mathematical model, involving bifurcating autoregression to describe stochasticity of partitioning and inheritance of cell-cycle-phase times, and an ordinary differential equation system to capture single-cell protein dynamics. Long-term simulations, concordant with in vitro experiments, demonstrated the model reproduced the main features of our data and had homeostatic properties. Moreover, heterogeneity of cell cycle may have important consequences during population development. We discovered an effect similar to genetic drift, amplified by family relationships among cells. In consequence, the progeny of a single cell with a short cell cycle time had a high probability of eventually dominating the population, due to the heritability of cell-cycle phases. Patterns of epigenetic heritability in proliferating cells are important for understanding long-term trends of cell populations which are either required to provide the influx of maturing cells (such as hematopoietic stem cells) or which started proliferating uncontrollably (such as cancer cells)., Author summary All cells in multicellular organisms obey orchestrated sequences of signals to ensure developmental and homeostatic fitness under a variety of external stimuli. However, there also exist self-perpetuating stem-cell populations, the function of which is to provide a steady supply of differentiated progenitors that in turn ensure persistence of organism functions. This “cell production engine” is an important element of biological homeostasis. A similar process, albeit distorted in many respects, plays a major role in cancer development; here the robustness of homeostasis contributes to difficulty in eradication of malignancy. An important role in homeostasis seems to be played by generation of heterogeneity among cell phenotypes, which then can be shaped by selection and other genetic forces. In the present paper, we present a model of a cultured cell population, which factors in relationships among related cells and the dynamics of cell growth and important proteins regulating cell division. We find that the model not only maintains homeostasis, but that it also responds to perturbations in a manner that is similar to that exhibited by the Wright-Fisher model of population genetics. The model-cell population can become dominated by the progeny of the fittest individuals, without invoking advantageous mutations. If confirmed, this may provide an alternative mode of evolution of cell populations.

International audience; Microcontact printing has become a versatile soft lithography technique used to produce molecular micro-and nano-patterns consisting of a large range of different biomolecules. Despite intensive research over the last decade and numerous applications in the fields of biosensors, microarrays and biomedical applications, the large-scale implementation of microcontact printing is still an issue. It is hindered by the stamp-inking step that is critical to ensure a reproducible and uniform transfer of inked molecules over large areas. This is particularly important when addressing application such as cell microarray manufacturing, which are currently used for a wide range of analytical and pharmaceutical applications. In this paper, we present a large-scale and multiplexed microcontact printing process of extra-cellular matrix proteins for the fabrication of cell microarrays. We have developed a micro-fluidic inking approach combined with a magnetic clamping technology that can be adapted to most standard substrates used in biology. We have demonstrated a significant improvement of homogeneity of printed protein patterns on surfaces larger than 1 cm 2 through the control of both the flow rate and the wetting mechanism of the stamp surface during micro-fluidic inking. Thanks to the reproducibility and integration capabilities provided by microflui-dics, we have achieved the printing of three different adhesion proteins in one-step transfer. Selective cell adhesion and cell shape adaptation on the produced patterns were observed, showing the suitability of this approach for producing on-demand large-scale cell microarrays.

Robust, real-time event detection from physiological signals acquired during long-term ambulatory monitoring still represents a major challenge. In this paper, we propose an original and generic multi-feature probabilistic detector (MFPD) and apply it to real-time QRS complex detection.Methods: The proposed method first derives relevant features from one or more physiological signals and estimates their probability distributions. Bayesian probabilities are then calculated for each feature before being merged through a data fusion node, with regard to the Kullback-Leibler divergence measure. To this end, we derived a KLD estimation method between two Generalized Normal Distributions (GND), in the general case. The efficiency of our method is validated on two noisy ECG databases : 1) a benchmark database by adding noise recordings to the entire MIT-BIH arrhythmia database with noise levels ranging from -6dB to 24dB, 2) an exercise stress test database composed of 54 real ECG recordings (17.8 hours).Results: In both cases significant improvements in detection performance are obtained compared with reference methods. For the benchmark noise stress database, performance gains are consistent for all noise type and SNR levels, and are increasing in low SNR levels. For the exercise stress database, detection error criterion is lowered to 20.91%, as compared with Pan-Tompkins-based (33.08%) and wavelet-based QRS detector (29.02%). Conclusion: Thanks to its multi-feature aspect and its KLD-based decision method (able to adaptively adjust the relative contribution of each feature to the final decision in real-time), the proposed method yields some interesting results, especially on highly-artifacted signals.Significance: Robust, easy to implement and with a reasonable computational cost, MFPD offers some promising perspectives for various challenging monitoring situations., La détection robuste et en temps réel d'événements à partir des signaux physiologiques acquis lors de la surveillance ambulatoire à long terme représente toujours un défi majeur. Dans cet article, nous proposons un détecteur probabiliste multicaractéristique (MFPD) original et générique et l'appliquons à la détection du complexe QRS en temps réel.Méthodes : La méthode proposée dérive d'abord les caractéristiques pertinentes d'un ou de plusieurs signaux physiologiques et estime leur distribution de probabilité. Les probabilités bayésiennes sont ensuite calculées pour chaque caractéristique avant d'être fusionnées à travers un nœud de fusion de données basé sur la pondération de la divergence de Kullback-Leibler. Pour ce faire, nous avons dérivé une méthode d'estimation du KLD entre deux distributions normales généralisées (GND), dans le cas général. L'efficacité de notre méthode est validée sur deux bases de données d'ECG bruités : 1) une base de données simulées, en ajoutant des segments bruités à l'ensemble de la base de données d'arythmie du MIT-BIH avec des niveaux de bruit allant de -6dB à 24dB, 2) une base de données d'exercices sous contraintes composée de 54 enregistrements d'ECG (17,8 heures).Résultats : Dans les deux cas, des améliorations significatives des performances de détection sont obtenues par rapport aux méthodes de référence. En ce qui concerne la base de données simulée, les gains de performance sont importants pour tous les types de bruit et tous les niveaux de SNR, et ils augmentent dans les faibles niveaux de SNR. Pour la base de données d’exercices, le critère d'erreur de détection est abaissé à 20,91 %, comparativement au détecteur QRS basé sur la méthode Pan Tompkins (33,08 %) et au détecteur QRS basé sur les ondelettes (29,02 %). Conclusion : Grâce à son aspect multicaractéristique et sa méthode de décision basée sur le KLD (capable d'ajuster de manière adaptative la contribution relative de chaque fonction à la décision finale en temps réel), la méthode proposée permet d'obtenir des résultats intéressants, en particulier sur les signaux hautement bruités.

Over the last three decades, evidence has emerged that low-intensity magnetic fields can influence biological systems. It is now well established that migratory birds have the capacity to detect the Earth's magnetic field; it has been reported that power lines are associated with childhood leukemia and that pulsed magnetic fields increase the production of reactive oxidative species (ROS) in cellular systems. Justifiably, studies in this field have been viewed with skepticism, as the underlying molecular mechanisms are unknown. In the accompanying paper, Sherrard and colleagues report that low-flux pulsed electromagnetic fields (PEMFs) result in aversive behavior in Drosophila larvae and ROS production in cell culture. They further report that these responses require the presence of cryptochrome, a putative magnetoreceptor. If correct, it is conceivable that carcinogenesis associated with power lines, PEMF-induced ROS generation, and animal magnetoreception share a common mechanistic basis., This Primer explores recent evidence as to how low-intensity magnetic fields might influence biological systems.

The well established phenomenon of ribosome drop-off plays crucial roles in translational accuracy and nutrient starvation responses during protein translation. When cells are under stress conditions, such as amino acid starvation or aminoacyl-tRNA depletion due to a high level of recombinant protein expression, ribosome drop-off can substantially affect the efficiency of protein expression. Here we introduce a mathematical model that describes the effects of ribosome drop-off on the ribosome density along the mRNA and on the concomitant protein synthesis rate. Our results show that ribosome premature termination may lead to non-intuitive ribosome density profiles, such as a ribosome density which increases from the 5’ to the 3’ end. Importantly, the model predicts that the effects of ribosome drop-off on the translation rate are mRNA-specific, and we quantify their resilience to drop-off, showing that the mRNAs which present ribosome queues are much less affected by ribosome drop-off than those which do not. Moreover, among those mRNAs that do not present ribosome queues, resilience to drop-off correlates positively with the elongation rate, so that sequences using fast codons are expected to be less affected by ribosome drop-off. This result is consistent with a genome-wide analysis of S. cerevisiae, which reveals that under favourable growth conditions mRNAs coding for proteins involved in the translation machinery, known to be highly codon biased and using preferentially fast codons, are highly resilient to ribosome drop-off. Moreover, in physiological conditions, the translation rate of mRNAs coding for regulatory, stress-related proteins, is less resilient to ribosome drop-off. This model therefore allows analysis of variations in the translational efficiency of individual mRNAs by accounting for the full range of known ribosome behaviours, as well as explaining mRNA-specific variations in ribosome density emerging from ribosome profiling studies., Author summary Ribosomes often drop off the messenger RNA (mRNA) molecules during the process of translation. This phenomenon occurs under normal conditions and it is especially aggravated if the cell is subject to stress. In this paper we show that protein production rates and polysome profiles along the mRNA can be strongly affected by ribosome drop-off. It can lead to a non-intuitive increasing ribosome density profile from the 5’ to 3’ end, as well as coexistence between low and high ribosomal density along the mRNA. Importantly, we show that the repercussions of ribosome drop-off are mRNA-dependent, with the specific codon configuration and composition determining their resilience to ribosome drop-off effects.