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Direct band gap III-V semiconductors, emitting efficiently in the amber-green region of the visible spectrum, are still missing, causing loss in efficiency in light emitting diodes operating in this region, a phenomenon known as the "green gap". Novel geometries and crystal symmetries however show strong promise in overcoming this limit. Here we develop a novel material system, consisting of wurtzite AlxIn1-xP nanowires, which is predicted to have a direct band gap in the green region. The nanowires are grown with selective area metalorganic vapor phase epitaxy and show wurtzite crystal purity from transmission electron microscopy. We show strong light emission at room temperature between the near-infrared 875 nm (1.42 eV) and the "pure green" 555 nm (2.23 eV). We investigate the band structure of wurtzite AlxIn1-xP using time-resolved and temperature-dependent photoluminescence measurements and compare the experimental results with density functional theory simulations, obtaining excellent agreement. Our work paves the way for high-efficiency green light emitting diodes based on wurtzite III-phosphide nanowires. [ABSTRACT FROM AUTHOR]

Thanks to their uniqueness, nanowires allow the realization of novel semiconductor crystal structures with yet unexplored properties, which can be key to overcome current technological limits. Here we develop the growth of wurtzite GaP/InxGa1-xP core-shell nanowires with tunable indium concentration and optical emission in the visible region from 590 nm (2.1 eV) to 760 nm (1.6 eV). We demonstrate a pseudodirect (Γ8c-Γ9v) to direct (Γ7c-Γ9v) transition crossover through experimental and theoretical approach. Time resolved and temperature dependent photoluminescence measurements were used, which led to the observation of a steep change in carrier lifetime and temperature dependence by respectively one and 3 orders of magnitude in the range 0.28 ± 0.04 ≤ x ≤ 0.41 ± 0.04. Our work reveals the electronic properties of wurtzite InxGa1-xP. [ABSTRACT FROM AUTHOR]

The present article reviews the latest information of the endothelial organs. The endothelium is a thin layer of cells that recover the lumen of the blood vessels, and in the human being represent an area of approximately 400 m² and weight 1500 g with capacity of secretion of either microor macro molecules. [ABSTRACT FROM AUTHOR]

The objective of this work was to evaluate the efficiency to use atmospheric air as a substitute of oxygen in anaesthetic dogs with halothane. The work was carried out in two groups of fife animals each using either oxygen or compressed air. Physiologic parameters such as clinical signs and arterial blood analysed were done. To evaluate the breath efficiency blood arterial samples were taken at 30, 60 and 90 minutes trough measurement of the partial pressure of PCO2, PO2 and pH. Under the present condition the inhalatory anaesthesia with halothane and assisted breath using atmospheric air with quirurgic time until 90 minutes shown comparative parameter as that using medicinal oxygen. [ABSTRACT FROM AUTHOR]

Objective: Non-invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure-day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt., Methods: Seventy-eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high-frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo-prospective seizure-day forecasting, with forecasting horizons of 16- and 24-h to include both diurnal and nocturnal seizures (24-h) or diurnal seizures only (16-h). The algorithm's performance was assessed using a nested leave-one-patient-out cross-validation approach., Results: Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16- and 24-h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16-h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24-h horizon., Significance: Smart shirt-based nocturnal sleep analysis holds promise as a non-invasive approach for seizure-day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long-term recordings, could pave the way for the development of innovative and practical seizure forecasting devices., Plain Language Summary: Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach., (© 2024 The Author(s). Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Wearable-based seizure detection devices hold promise in reducing seizure-related adverse events and relieving the daily stress experienced by people with epilepsy. In this work, we present the latest evidence regarding the performance of three seizure detection wearables (eight studies) commercially available in Canada to provide guidance to clinicians. Overall, their ability to detect focal-to-bilateral and/or generalized tonic-clonic seizures ranges between 21.0% and 98.15% in sensitivity, with the 24h false alarm rates ranging from 0 to 1.28. While performance in epilepsy monitoring units show promise, the lack of evidence in outpatient settings precludes strong recommendations for their use in daily life.

Connectivity analyses of intracranial electroencephalography (iEEG) could guide surgical planning for epilepsy surgery by improving the delineation of the seizure onset zone. Traditional approaches fail to quantify important interactions between frequency components. To assess if effective connectivity based on cross-bispectrum -a measure of nonlinear multivariate cross-frequency coupling- can quantitatively identify generators of seizure activity, cross-bispectrum connectivity between channels was computed from iEEG recordings of 5 patients (34 seizures) with good postsurgical outcome. Personalized thresholds of 50% and 80% of the maximum coupling values were used to identify generating electrode channels. In all patients, outflow coupling between α (8-15 Hz) and β (16-31 Hz) frequencies identified at least one electrode inside the resected seizure onset zone. With the 50% and 80% thresholds respectively, an average of 5 (44.7%; specificity = 82.6%) and 2 (22.5%; specificity = 99.0%) resected electrodes were correctly identified. Results show promise for the automatic identification of the seizure onset zone based on cross-bispectrum connectivity analysis.

Introduction: While it is known that poor sleep is a seizure precipitant, this association remains poorly quantified. This study investigated whether seizures are preceded by significant changes in sleep efficiency as measured by a wearable equipped with an electrocardiogram, respiratory bands, and an accelerometer., Methods: Nocturnal recordings from 47 people with epilepsy hospitalized at our epilepsy monitoring unit were analyzed (304 nights). Sleep metrics during nights followed by epileptic seizures (24 h post-awakening) were compared to those of nights which were not., Results: Lower sleep efficiency (percentage of sleep during the night) was found in the nights preceding seizure days ( p < 0.05). Each standard deviation decrease in sleep efficiency and increase in wake after sleep onset was respectively associated with a 1.25-fold (95 % CI: 1.05 to 1.42, p < 0.05) and 1.49-fold (95 % CI: 1.17 to 1.92, p < 0.01) increased odds of seizure occurrence the following day. Furthermore, nocturnal seizures were associated with significantly lower sleep efficiency and higher wake after sleep onset ( p < 0.05), as well as increased odds of seizure occurrence following wake (OR: 5.86, 95 % CI: 2.99 to 11.77, p < 0.001)., Discussion: Findings indicate lower sleep efficiency during nights preceding seizures, suggesting that wearable sensors could be promising tools for sleep-based seizure-day forecasting in people with epilepsy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gagliano, Ding, Toffa, Beauregard, Robert, Lesage, Sawan, Nguyen and Bou Assi.)

Objective: Uncontrolled epilepsy creates a constant source of worry for patients and puts them at a high risk of injury. Identifying recurrent "premonitory" symptoms of seizures and using them to recalibrate seizure prediction algorithms may improve prediction performances. This study aimed to investigate patients' ability to predict oncoming seizures based on preictal symptoms., Methods: Through an online survey, demographics and clinical characteristics (e.g., seizure frequency, epilepsy duration, and postictal symptom duration) were collected from people with epilepsy and caregivers across Canada. Respondents were asked to answer questions regarding their ability to predict seizures through warning symptoms. A total of 196 patients and 150 caregivers were included and were separated into three groups: those who reported warning symptoms within the 5 minutes preceding a seizure, prodromes (symptoms earlier than 5 minutes before seizure), and no warning symptoms., Results: Overall, 12.2% of patients and 12.0% of caregivers reported predictive prodromes ranging from 5 minutes to more than 24 hours before the seizures (median of 2 hours). The most common were dizziness/vertigo (28%), mood changes (26%), and cognitive changes (21%). Statistical testing showed that respondents who reported prodromes also reported significantly longer postictal recovery periods compared to those who did not report predictive prodromes ( P < 0.05)., Conclusion: Findings suggest that patients who present predictive seizure prodromes may be characterized by longer patient-reported postictal recovery periods. Studying the correlation between seizure severity and predictability and investigating the electrical activity underlying prodromes may improve our understanding of preictal mechanisms and ability to predict seizures.

Over a third of patients suffering from epilepsy continue to live with recurrent disabling seizures and would greatly benefit from personalized seizure forecasting. While electroencephalography (EEG) remains most popular for studying subject-specific epileptic precursors, dysfunctions of the autonomous nervous system, notably cardiac activity measured in heart rate variability (HRV), have also been associated with epileptic seizures. This work proposes an unsupervised clustering technique which aims to automatically identify preictal HRV changes in 9 patients who underwent simultaneous electrocardiography (ECG) and intracranial EEG presurgical monitoring at the University of Montreal Hospital Center. A 2-class k-means clustering combined with a quantitative preictal HRV change detection technique were adopted in a subject- and seizure-specific manner. Results indicate inter and intra-patient variability in preictal HRV changes (between 3.5 and 6.5 min before seizure onset) and a statistically significant negative correlation between the time of change in HRV state and the duration of seizures (p<0.05). The presented findings show promise for new avenues of research regarding multimodal seizure prediction and unsupervised preictal time assessment.Clinical Relevance- This study proposed an unsupervised technique for quantitatively identifying preictal HRV changes which can be eventually used to implement an ECG-based seizure forecasting algorithm.

According to Fourier's law, a temperature difference across a material results in a linear temperature profile and a thermal conductance that decreases inversely proportional to the system length. These are the hallmarks of diffusive heat flow. Here, we report heat flow in ultrathin (25 nm) GaP nanowires in the absence of a temperature gradient within the wire and find that the heat conductance is independent of wire length. These observations deviate from Fourier's law and are direct proof of ballistic heat flow, persisting for wire lengths up to at least 15 μm at room temperature. When doubling the wire diameter, a remarkably sudden transition to diffusive heat flow is observed. The ballistic heat flow in the ultrathin wires can be modeled within Landauer's formalism by ballistic phonons with an extraordinarily long mean free path.