Correlation anatomo-scannographique dans le cancer du larynx

Introduction : Le but de la chirurgie du larynx est de conserver dans la mesure du possible un larynx fonctionnel tout en répondant aux impératifs carcinologiques. Les limites d'extension tumorale doivent être finement analysées en pré-opératoire pour adopter la technique opératoire appropriée. dans ce domaine, l'apport de la TdM est incontestable pour l'étude de cette extension en profondeur.Patients et méthodes : il s'agit d'une étude rétrospective des dossiers de 43 patients explorés et traités respectivement aux services d'imagerie médicale et d'ORL de l'hôpital Tahar Sfar de Mahdia sur une période de 14 ans entre Janvier 1995 et décembre 2008.Résultats : La classification pré-opératoire des tumeurs pharyngo-laryngées est plus fiable en se basant à la fois sur l’endoscopie et la TdM que sur l’endoscopie seule. La fiabilité de cette association était de 76%. L'étude de certaines zones dites d'intérêt a montré une efficacité de 72% pour l'atteinte de la commissure antérieure, de 79% pour les bandes ventriculaires, de 81% pour la sous glotte, de 88% pour la loge HTE, de 83% pour les espaces para-glottiques et de 79% pour les cartilages. Nos résultats étaient concordants à ceux de la littérature.Discussion et conclusion : la TdM constitue une partie intégrante du bilan préopératoire des cancers du pharyngolarynx. Elle précise l'extension tumorale en profondeur aux espaces graisseux, aux cartilages et aux tissus extralaryngés. L’analyse fine des images tomodensitométriques permet donc d’orienter la décision thérapeutique.Mots clés : cancer, larynx, anatomopathologie, chirurgie

Neuro-GPT: Developing A Foundation Model for EEG

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG

Facteurs Predictifs De Malignite D\'un Nodule Thyroidien

Buts : étudier les facteurs prédictifs de malignité des nodules thyroïdiens et comparer nos résultats à ceux de la littérature. Patients et méthodes : Il s\'agit d\'une étude rétrospective a propos de 282 cas de nodules thyroïdiens opérés à l\' hôpital de Mahdia entre 1988 et 2003. Résultats : L\'âge moyen était de 44 ans. Le risque de malignité des nodules thyroïdiens était de 15,6% . Ce risque était plus important chez les hommes (50%) que chez les femmes (13,3%). Certains facteurs étaient hautement prédictifs de malignité comme l\'âge supérieur à 60 ans, les signes de compression, les adénopathies cervicales et le caractère fixe et dure du nodule thyroidien Conclusion : Certains signes cliniques et para cliniques ont une grande valeur en matière de bénignité ou de malignité des nodules thyroïdiens.Aim : Study the predictive factors of malignancy of thyroid gland nodules and compare our results to those of the literature. Patients and methods : A retrospective study about 282 cases of thyroid gland nodules treated in Madhya hospital between 1988 and 2003. Results : The middle age was 44 years. The risk of malignancy was 15,6 %. This risk was higher in men (50 %) then in women (13,3 %). Some factors were highly predictive of malignancy like age superior then 60 years, neck lymph nodes … Conclusion: Some clinic and para clinic signs have an important value in benignancy or malignancy of thyroid gland nodules. Journal Tunisien d\'ORL et de chirurgie cervico-faciale Vol. 18 2007: pp. 20-2

Differential electrophysiological response during rest, self-referential, and non-self-referential tasks in human posteromedial cortex

The electrophysiological basis for higher brain activity during rest and internally directed cognition within the human default mode network (DMN) remains largely unknown. Here we use intracranial recordings in the human posteromedial cortex (PMC), a core node within the DMN, during conditions of cued rest, autobiographical judgments, and arithmetic processing. We found a heterogeneous profile of PMC responses in functional, spatial, and temporal domains. Although the majority of PMC sites showed increased broad gamma band activity (30-180 Hz) during rest, some PMC sites, proximal to the retrosplenial cortex, responded selectively to autobiographical stimuli. However, no site responded to both conditions, even though they were located within the boundaries of the DMN identified with resting-state functional imaging and similarly deactivated during arithmetic processing. These findings, which provide electrophysiological evidence for heterogeneity within the core of the DMN, will have important implications for neuroimaging studies of the DMN

Operationally meaningful representations of physical systems in neural networks

To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. The representations learnt by most current machine learning techniques reflect statistical structure present in the training data; however, these methods do not allow us to specify explicit and operationally meaningful requirements on the representation. Here, we present a neural network architecture based on the notion that agents dealing with different aspects of a physical system should be able to communicate relevant information as efficiently as possible to one another. This produces representations that separate different parameters which are useful for making statements about the physical system in different experimental settings. We present examples involving both classical and quantum physics. For instance, our architecture finds a compact representation of an arbitrary two-qubit system that separates local parameters from parameters describing quantum correlations. We further show that this method can be combined with reinforcement learning to enable representation learning within interactive scenarios where agents need to explore experimental settings to identify relevant variables.Comment: 24 pages, 13 figure

Increased Evoked Potentials to Arousing Auditory Stimuli during Sleep: Implication for the Understanding of Dream Recall

High dream recallers (HR) show a larger brain reactivity to auditory stimuli during wakefulness and sleep as compared to low dream recallers (LR) and also more intra-sleep wakefulness (ISW), but no other modification of the sleep macrostructure. To further understand the possible causal link between brain responses, ISW and dream recall, we investigated the sleep microstructure of HR and LR, and tested whether the amplitude of auditory evoked potentials (AEPs) was predictive of arousing reactions during sleep. Participants (18 HR, 18 LR) were presented with sounds during a whole night of sleep in the lab and polysomnographic data were recorded. Sleep microstructure (arousals, rapid eye movements (REMs), muscle twitches (MTs), spindles, KCs) was assessed using visual, semi-automatic and automatic validated methods. AEPs to arousing (awakenings or arousals) and non-arousing stimuli were subsequently computed. No between-group difference in the microstructure of sleep was found. In N2 sleep, auditory arousing stimuli elicited a larger parieto-occipital positivity and an increased late frontal negativity as compared to non-arousing stimuli. As compared to LR, HR showed more arousing stimuli and more long awakenings, regardless of the sleep stage but did not show more numerous or longer arousals. These results suggest that the amplitude of the brain response to stimuli during sleep determine subsequent awakening and that awakening duration (and not arousal) is the critical parameter for dream recall. Notably, our results led us to propose that the minimum necessary duration of an awakening during sleep for a successful encoding of dreams into long-term memory is approximately 2 min

High biomass yield increases in a primary effluent wastewater phytofiltration are associated to altered leaf morphology and stomatal size in Salix miyabeana

Municipal wastewater treatment using willow ‘phyto’-filtration has the potential for reduced environmental impact compared to conventional treatment practices. However, the physiological adaptations underpinning tolerance to high wastewater irrigation in willow are unknown. A one-hectare phytofiltration plantation established using the Salix miyabeana cultivar ‘SX67’ in Saint-Roch-de-l'Achigan, Quebec, Canada, tested the impact of unirrigated, potable water or two loads of primary effluent wastewater 19 and 30 ML ha−1 yr−1. A nitrogen load of 817 kg N ha−1 from wastewater did not increase soil pore water nitrogen concentrations beyond Quebec drinking water standards. The willow phytofiltration phenotype had increased leaf area (+106–142%) and leaf nitrogen (+94%) which were accompanied by significant increases in chlorophyll a + b content. Wastewater irrigated trees had higher stomatal sizes and a higher stomatal pore index, despite lower stomatal density, resulting in increased stomatal conductance (+42–78%). These developmental responses led to substantial increases in biomass yields of 56–207% and potable water controls revealed the nitrogen load to be necessary for the high productivity of 28–40 t ha−1 yr−1 in wastewater irrigated trees. Collectively, this study suggests phytofiltration plantations could treat primary effluent municipal wastewater at volumes of at least 19 million litres per hectare and benefit from increased yields of sustainable biomass over a two-year coppice cycle. Added-value cultivation practices, such as phytofiltration, have the potential to mitigate negative local and global environmental impact of wastewater treatment while providing valuable services and sustainable bioproducts

Human Gamma Oscillations during Slow Wave Sleep

Neocortical local field potentials have shown that gamma oscillations occur spontaneously during slow-wave sleep (SWS). At the macroscopic EEG level in the human brain, no evidences were reported so far. In this study, by using simultaneous scalp and intracranial EEG recordings in 20 epileptic subjects, we examined gamma oscillations in cerebral cortex during SWS. We report that gamma oscillations in low (30–50 Hz) and high (60–120 Hz) frequency bands recurrently emerged in all investigated regions and their amplitudes coincided with specific phases of the cortical slow wave. In most of the cases, multiple oscillatory bursts in different frequency bands from 30 to 120 Hz were correlated with positive peaks of scalp slow waves (“IN-phase” pattern), confirming previous animal findings. In addition, we report another gamma pattern that appears preferentially during the negative phase of the slow wave (“ANTI-phase” pattern). This new pattern presented dominant peaks in the high gamma range and was preferentially expressed in the temporal cortex. Finally, we found that the spatial coherence between cortical sites exhibiting gamma activities was local and fell off quickly when computed between distant sites. Overall, these results provide the first human evidences that gamma oscillations can be observed in macroscopic EEG recordings during sleep. They support the concept that these high-frequency activities might be associated with phasic increases of neural activity during slow oscillations. Such patterned activity in the sleeping brain could play a role in off-line processing of cortical networks

Moving magnetoencephalography towards real-world applications with a wearable system

Imaging human brain function with techniques such as magnetoencephalography1 (MEG) typically requires a subject to perform tasks whilst their head remains still within a restrictive scanner. This artificial environment makes the technique inaccessible to many people, and limits the experimental questions that can be addressed. For example, it has been difficult to apply neuroimaging to investigation of the neural substrates of cognitive development in babies and children, or in adult studies that require unconstrained head movement (e.g. spatial navigation). Here, we develop a new type of MEG system that can be worn like a helmet, allowing free and natural movement during scanning. This is possible due to the integration of new quantum sensors2,3 that do not rely on superconducting technology, with a novel system for nulling background magnetic fields. We demonstrate human electrophysiological measurement at millisecond resolution whilst subjects make natural movements, including head nodding, stretching, drinking and playing a ball game. Results compare well to the current state-of-the-art, even when subjects make large head movements. The system opens up new possibilities for scanning any subject or patient group, with myriad applications such as characterisation of the neurodevelopmental connectome, imaging subjects moving naturally in a virtual environment, and understanding the pathophysiology of movement disorders