A survey of transposable element classification systems--a call for a fundamental update to meet the challenge of their diversity and complexity.

The increase of publicly available sequencing data has allowed for rapid progress in our understanding of genome composition. As new information becomes available we should constantly be updating and reanalyzing existing and newly acquired data. In this report we focus on transposable elements (TEs) which make up a significant portion of nearly all sequenced genomes. Our ability to accurately identify and classify these sequences is critical to understanding their impact on host genomes. At the same time, as we demonstrate in this report, problems with existing classification schemes have led to significant misunderstandings of the evolution of both TE sequences and their host genomes. In a pioneering publication Finnegan (1989) proposed classifying all TE sequences into two classes based on transposition mechanisms and structural features: the retrotransposons (class I) and the DNA transposons (class II). We have retraced how ideas regarding TE classification and annotation in both prokaryotic and eukaryotic scientific communities have changed over time. This has led us to observe that: (1) a number of TEs have convergent structural features and/or transposition mechanisms that have led to misleading conclusions regarding their classification, (2) the evolution of TEs is similar to that of viruses by having several unrelated origins, (3) there might be at least 8 classes and 12 orders of TEs including 10 novel orders. In an effort to address these classification issues we propose: (1) the outline of a universal TE classification, (2) a set of methods and classification rules that could be used by all scientific communities involved in the study of TEs, and (3) a 5-year schedule for the establishment of an International Committee for Taxonomy of Transposable Elements (ICTTE).

Quantifying the time for accurate EEG decoding of single value-based decisions.

BACKGROUND: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. NEW METHOD: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. RESULTS: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-by-trial basis. Typical latencies of when decisions are made were detected at ∼500ms for 'easy' vs. ∼700ms for 'hard' decisions, well before subjects' response (∼340ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. COMPARISON WITH EXISTING METHOD(S): We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. CONCLUSIONS: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

Transcranial Magnetic Stimulation Combined with EEG Reveals Covert States of Elevated Excitability in the Human Epileptic Brain.

BACKGROUND: Transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) can be used to explore the dynamical state of neuronal networks. In patients with epilepsy, TMS can induce epileptiform discharges (EDs) with a stochastic occurrence despite constant stimulation parameters. This observation raises the possibility that the pre-stimulation period contains multiple covert states of brain excitability some of which are associated with the generation of EDs. OBJECTIVE: To investigate whether the interictal period contains "high excitability" states that upon brain stimulation produce EDs and can be differentiated from "low excitability" states producing normal appearing TMS-EEG responses. METHODS: In a cohort of 25 patients with Genetic Generalized Epilepsies (GGE) we identified two subjects characterized by the intermittent development of TMS-induced EDs. The high-excitability in the pre-stimulation period was assessed using multiple measures of univariate time series analysis. Measures providing optimal discrimination were identified by feature selection techniques. The "high excitability" states emerged in multiple loci (indicating diffuse cortical hyperexcitability) and were clearly differentiated on the basis of 14 measures from "low excitability" states (accuracy = 0.7). CONCLUSION: In GGE, the interictal period contains multiple, quasi-stable covert states of excitability a class of which is associated with the generation of TMS-induced EDs. The relevance of these findings to theoretical models of ictogenesis is discussed.

Predicting the outcome of post-anoxic comatose patients based on single-trial EEG analysis

Les maladies cardio-vasculaires représentent la première cause de mortalité en Suisse. Après un arrêt cardio-respiratoire, une minorité des patients survit sans ou avec peu de séquelles fonctionnelles. L'évaluation du pronostic se fait classiquement selon des critères établis par l'Académie Américaine de Neurologie (AAN) en 2006, soit précédant l'introduction de l'hypothermie thérapeutique. Depuis, ces critères semblent insuffisants, et de nouveaux examens para-cliniques sont nécessaires afin d'identifier les patients ayant un pronostic favorable. La détection d'irrégularités auditives, et plus particulièrement l'évolution de cette détection sur plusieurs jours, pourrait être un indicateur du pronostic de patients comateux suite à une anoxie cérébrale. En effet, lors d'une violation de la régularité établie par des séries de sons identiques, deux signaux sont détectables à l'électro- encéphalographie (EEG). Le premier, dénommé «Mismatch negativity» (MMN), peut être enregistré après une violation locale d'une régularité établie au niveau de chaque son. Il reflète un processus inconscient et ne demandant pas de ressources attentionnelles. Le deuxième, dénommé « complexe P300 » survient par contre après une violation globale d'une régularité établie au niveau de groupes de sons. La littérature actuelle indique que ce deuxième phénomène requerrait la présence de capacités attentionnelles. Dans notre étude, nous avons testé l'existence de cette détection d'irrégularités auditives globales chez des patients dans une phase précoce de coma post-anoxique, sous hypothermie thérapeutique. Nous avons enregistré la réponse électro-encéphalographique lors de violations de régularités auditives globales, à l'aide d'un protocole expérimental qui intégrait en plus un paradigme de MMN classique, afin de tester la détection d'irrégularités auditives locales également. Notre analyse finale inclut 24 patients comateux ayant subi un arrêt cardio-respiratoire, et bénéficié du protocole hypothermie du Centre Hospitalier Universitaire Vaudois (CHUV) à Lausanne. Après une analyse multivariée des réponses électro-encéphalographiques de chaque tracé individuellement (« single-trial »), nous avons trouvé que 8 patients sur 24 pouvaient discriminer une irrégularité globale, alors qu'étant définis comateux selon l'échelle de Glasgow (GCS). De plus, l'amélioration de la détection d' irrégularités auditives entre deux EEG consécutifs (en hypo- puis normothermie), était un facteur de bon pronostic. Notre test pourrait ainsi être un complément para-clinique dans l'évaluation du pronostic de patients en coma post- anoxique.

Preoperative assessment and classification of benign laryngotracheal stenosis: a consensus paper of the European Laryngological Society.

Adult and pediatric laryngotracheal stenoses (LTS) comprise a wide array of various conditions that require precise preoperative assessment and classification to improve comparison of different therapeutic modalities in a matched series of patients. This consensus paper of the European Laryngological Society proposes a five-step endoscopic airway assessment and a standardized reporting system to better differentiate fresh, incipient from mature, cicatricial LTSs, simple one-level from complex multilevel LTSs and finally "healthy" from "severely morbid" patients. The proposed scoring system, which integrates all of these parameters, may be used to help define different groups of LTS patients, choose the best treatment modality for each individual patient and assess distinct post-treatment outcomes accordingly.

Towards high-quality simultaneous EEG-fMRI at 7T: Detection and reduction of EEG artifacts due to head motion.

The enhanced functional sensitivity offered by ultra-high field imaging may significantly benefit simultaneous EEG-fMRI studies, but the concurrent increases in artifact contamination can strongly compromise EEG data quality. In the present study, we focus on EEG artifacts created by head motion in the static B0 field. A novel approach for motion artifact detection is proposed, based on a simple modification of a commercial EEG cap, in which four electrodes are non-permanently adapted to record only magnetic induction effects. Simultaneous EEG-fMRI data were acquired with this setup, at 7T, from healthy volunteers undergoing a reversing-checkerboard visual stimulation paradigm. Data analysis assisted by the motion sensors revealed that, after gradient artifact correction, EEG signal variance was largely dominated by pulse artifacts (81-93%), but contributions from spontaneous motion (4-13%) were still comparable to or even larger than those of actual neuronal activity (3-9%). Multiple approaches were tested to determine the most effective procedure for denoising EEG data incorporating motion sensor information. Optimal results were obtained by applying an initial pulse artifact correction step (AAS-based), followed by motion artifact correction (based on the motion sensors) and ICA denoising. On average, motion artifact correction (after AAS) yielded a 61% reduction in signal power and a 62% increase in VEP trial-by-trial consistency. Combined with ICA, these improvements rose to a 74% power reduction and an 86% increase in trial consistency. Overall, the improvements achieved were well appreciable at single-subject and single-trial levels, and set an encouraging quality mark for simultaneous EEG-fMRI at ultra-high field.

EEG reactivity to pain in comatose patients: Importance of the stimulus type.

INTRODUCTION: Electroencephalogram (EEG) background reactivity is a potentially interesting outcome predictor in comatose patients, especially after cardiac arrest, but recent studies report only fair interrater reliability. Furthermore, there are no definite guidelines for its testing. We therefore investigated the EEG effect of standardized noxious stimuli in comatose patients not reactive to auditory stimuli. METHODS: In this prospective study we applied a protocol using three different painful stimuli (bilateral nipple pinching, pinprick at the nose base, finger-nail compression on each side), grouped in three distinct clusters with an alternated sequence, during EEG recordings in comatose patients. We only analyzed recordings showing any reactivity to pain. Fisher and χ2 tests were used as needed to assess contingency tables. RESULTS: Of 42 studies, 12 did not show any background reactivity, 2 presented SIRPIDs, and 2 had massive artefacts; we thus analyzed 26 EEGs recorded in 17 patients (4 women, 24%). Nipple pinching more frequently induced a change in EEG background activity (p<0.001), with a sensitivity of 97.4% for reactivity. Neither the order of the stimuli in the cluster (p=0.723), nor the cluster order (p=0.901) influenced the results. CONCLUSION: In this pilot study, bilateral, synchronous nipple pinching seems to be the most efficient method to test nociceptive EEG reactivity in comatose patients. This approach may enhance interrater reliability, but deserves confirmation in larger cohorts.

Bayesian Networks for the Age Classification of Living Individuals: a Study on Transition Analysis

Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.

Probabilistic age classification with Bayesian networks: a study on the ossification status of the medial clavicular epiphysis

In the past few decades, the rise of criminal, civil and asylum cases involving young people lacking valid identification documents has generated an increase in the demand of age estimation. The chronological age or the probability that an individual is older or younger than a given age threshold are generally estimated by means of some statistical methods based on observations performed on specific physical attributes. Among these statistical methods, those developed in the Bayesian framework allow users to provide coherent and transparent assignments which fulfill forensic and medico-legal purposes. The application of the Bayesian approach is facilitated by using probabilistic graphical tools, such as Bayesian networks. The aim of this work is to test the performances of the Bayesian network for age estimation recently presented in scientific literature in classifying individuals as older or younger than 18 years of age. For these exploratory analyses, a sample related to the ossification status of the medial clavicular epiphysis available in scientific literature was used. Results obtained in the classification are promising: in the criminal context, the Bayesian network achieved, on the average, a rate of correct classifications of approximatively 97%, whilst in the civil context, the rate is, on the average, close to the 88%. These results encourage the continuation of the development and the testing of the method in order to support its practical application in casework.

Classification supervisée multi-étiquette en actes de dialogue: analyse discriminante et transformations de Schoenberg

Abstract This work studies the multi-label classification of turns in simple English Wikipedia talk pages into dialog acts. The treated dataset was created and multi-labeled by (Ferschke et al., 2012). The first part analyses dependences between labels, in order to examine the annotation coherence and to determine a classification method. Then, a multi-label classification is computed, after transforming the problem into binary relevance. Regarding features, whereas (Ferschke et al., 2012) use features such as uni-, bi-, and trigrams, time distance between turns or the indentation level of the turn, other features are considered here: lemmas, part-of-speech tags and the meaning of verbs (according to WordNet). The dataset authors applied approaches such as Naive Bayes or Support Vector Machines. The present paper proposes, as an alternative, to use Schoenberg transformations which, following the example of kernel methods, transform original Euclidean distances into other Euclidean distances, in a space of high dimensionality. Résumé Ce travail étudie la classification supervisée multi-étiquette en actes de dialogue des tours de parole des contributeurs aux pages de discussion de Simple English Wikipedia (Wikipédia en anglais simple). Le jeu de données considéré a été créé et multi-étiqueté par (Ferschke et al., 2012). Une première partie analyse les relations entre les étiquettes pour examiner la cohérence des annotations et pour déterminer une méthode de classification. Ensuite, une classification supervisée multi-étiquette est effectuée, après recodage binaire des étiquettes. Concernant les variables, alors que (Ferschke et al., 2012) utilisent des caractéristiques telles que les uni-, bi- et trigrammes, le temps entre les tours de parole ou l'indentation d'un tour de parole, d'autres descripteurs sont considérés ici : les lemmes, les catégories morphosyntaxiques et le sens des verbes (selon WordNet). Les auteurs du jeu de données ont employé des approches telles que le Naive Bayes ou les Séparateurs à Vastes Marges (SVM) pour la classification. Cet article propose, de façon alternative, d'utiliser et d'étendre l'analyse discriminante linéaire aux transformations de Schoenberg qui, à l'instar des méthodes à noyau, transforment les distances euclidiennes originales en d'autres distances euclidiennes, dans un espace de haute dimensionnalité.

EEG as an Indicator of Cerebral Functioning in Postanoxic Coma.

Postanoxic coma after cardiac arrest is one of the most serious acute cerebral conditions and a frequent cause of admission to critical care units. Given substantial improvement of outcome over the recent years, a reliable and timely assessment of clinical evolution and prognosis is essential in this context, but may be challenging. In addition to the classic neurologic examination, EEG is increasingly emerging as an important tool to assess cerebral functions noninvasively. Although targeted temperature management and related sedation may delay clinical assessment, EEG provides accurate prognostic information in the early phase of coma. Here, the most frequently encountered EEG patterns in postanoxic coma are summarized and their relations with outcome prediction are discussed. This article also addresses the influence of targeted temperature management on brain signals and the implication of the evolution of EEG patterns over time. Finally, the article ends with a view of the future prospects for EEG in postanoxic management and prognostication.