Detection of interictal discharges with convolutional neural networks using discrete ordered multichannel intracranial EEG

Detection algorithms for electroencephalography (EEG) data, especially in the field of interictal epileptiform discharge (IED) detection, have traditionally employed handcrafted features which utilised specific characteristics of neural responses. Although these algorithms achieve high accuracy, mere detection of an IED holds little clinical significance. In this work, we consider deep learning for epileptic subjects to accommodate automatic feature generation from intracranial EEG data, while also providing clinical insight. Convolutional neural networks are trained in a subject independent fashion to demonstrate how meaningful features are automatically learned in a hierarchical process. We illustrate how the convolved filters in the deepest layers provide insight towards the different types of IEDs within the group, as confirmed by our expert clinicians. The morphology of the IEDs found in filters can help evaluate the treatment of a patient. To improve the learning of the deep model, moderately different score classes are utilised as opposed to binary IED and non-IED labels. The resulting model achieves state of the art classification performance and is also invariant to time differences between the IEDs. This study suggests that deep learning is suitable for automatic feature generation from intracranial EEG data, while also providing insight into the dat

Deep learning for epileptic intracranial EEG data

Detection algorithms for electroencephalography (EEG) data typically employ handcrafted features that take advantage of the signal's specific properties. In the field of interictal epileptic discharge (IED) detection, the feature representation that provides optimal classification performance is still an unresolved issue. In this paper, we consider deep learning for automatic feature generation from epileptic intracranial EEG data in the time domain. Specifically, we consider convolutional neural networks (CNNs) in a subject independent fashion and demonstrate that meaningful features, representing IEDs are automatically learned. The resulting model achieves state of the art classification performance, provides insights for the different types of IEDs within the group, and is invariant to time differences between the IEDs. This study suggests that automatic feature generation via deep learning is suitable for IEDs and EEG in genera

Elevated circulating amyloid concentrations in obesity and diabetes promote vascular dysfunction

Diabetes, obesity and Alzheimer’s disease (AD) are associated with vascular complications and impaired nitric oxide (NO) production. Furthermore, increased β-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1), APP and β-amyloid (Aβ) are linked with vascular disease development and raised BACE1 and Aβ accompany hyperglycemia and hyperlipidemia. However, the causal relationship between obesity and diabetes, raised Aβ and vascular dysfunction is unclear. We report that diet-induced obesity (DIO) in mice raised plasma and vascular Aβ42 that correlated with decreased NO bioavailability, endothelial dysfunction and raised blood pressure. Genetic or pharmacological reduction of BACE1 activity and Aβ42 prevented and reversed, respectively, these outcomes. In contrast, expression of human mutant APP in mice or Aβ42 infusion into control diet-fed mice to mimic obese levels impaired NO production, vascular relaxation and raised blood pressure. In humans, raised plasma Aβ42 correlated with diabetes and endothelial dysfunction. Mechanistically, higher Aβ42 reduced endothelial NO synthase (eNOS), cyclic GMP and protein kinase G (PKG) activity independently of diet whereas endothelin-1 was increased by diet and Aβ42. Lowering Aβ42 reversed the DIO deficit in the eNOS-cGMP-PKG pathway and decreased endothelin-1. Our findings suggest that BACE1 inhibitors may have therapeutic value in the treatment of vascular disease associated with diabetes

The timing and widespread effects of the largest Holocene volcanic eruption in Antarctica.

The caldera collapse of Deception Island Volcano, Antarctica, was comparable in scale to some of the largest eruptions on Earth over the last several millennia. Despite its magnitude and potential for far-reaching environmental effects, the age of this event has never been established, with estimates ranging from the late Pleistocene to 3370 years before present. Here we analyse nearby lake sediments in which we identify a singular event produced by Deception Island's caldera collapse that occurred 3980 ± 125 calibrated years before present. The erupted tephra record the distinct geochemical composition of ejecta from the caldera-forming eruption, whilst an extreme seismic episode is recorded by lake sediments immediately overlying the collapse tephra. The newly constrained caldera collapse is now the largest volcanic eruption confirmed in Antarctica during the Holocene. An examination of palaeorecords reveals evidence in marine and lacustrine sediments for contemporaneous seismicity around the Antarctic Peninsula; synchronous glaciochemical volcanic signatures also record the eruption in ice cores spread around Antarctica, reaching >4600 km from source. The widespread footprint suggests that this eruption would have had significant climatic and ecological effects across a vast area of the south polar region

Financial crises and the attainment of the SDGs: an adjusted multidimensional poverty approach

This paper analyses the impact of financial crises on the Sustainable Development Goal of eradicating poverty. To do so, we develop an adjusted Multidimensional Poverty Framework (MPF) that includes 15 indicators that span across key poverty aspects related to income, basic needs, health, education and the environment. We then use an econometric model that allows us to examine the impact of financial crises on these indicators in 150 countries over the period 1980–2015. Our analysis produces new estimates on the impact of financial crises on poverty’s multiple social, economic and environmental aspects and equally important captures dynamic linkages between these aspects. Thus, we offer a better understanding of the potential impact of current debt dynamics on Multidimensional Poverty and demonstrate the need to move beyond the boundaries of SDG1, if we are to meet the target of eradicating poverty. Our results indicate that the current financial distress experienced by many low-income countries may reverse the progress that has been made hitherto in reducing poverty. We find that financial crises are associated with an approximately 10% increase of extreme poor in low-income countries. The impact is even stronger in some other poverty aspects. For instance, crises are associated with an average decrease of government spending in education by 17.72% in low-income countries. The dynamic linkages between most of the Multidimensional Poverty indicators, warn of a negative domino effect on a number of SDGs related to poverty, if there is a financial crisis shock. To pre-empt such a domino effect, the specific SDG target 17.4 on attaining long-term debt sustainability through coordinated policies plays a key role and requires urgent attention by the international community

Prototyping open digital tools for urban commoning

The paper will discuss an experimental co-design approach to the development of a digital toolkit prototype and a resulting set of co-design principles, which are put forward as a way of infrastructuring future design of digital tools for urban commoning. Focus is placed on the case study of a commoning hub in a Parisian suburb where the toolkit was co-designed through a series of prototyping workshops, carried out with hub users and addressing key hub needs. The prototyping process explored possibilities for re-appropriating and re-framing existing digital technologies as open toolkits, which can be further re-purposed by users, here and beyond, after the design of an initial toolkit prototype

Expression of AXL receptor tyrosine kinase relates to monocyte dysfunction and severity of cirrhosis

Infectious complications in patients with cirrhosis frequently initiate episodes of decompensation and substantially contribute to the high mortality. Mechanisms of the underlying immuneparesis remain underexplored. TAM receptors (TYRO3/AXL/MERTK) are important inhibitors of innate immune responses. To understand the pathophysiology of immuneparesis in cirrhosis, we detailed TAM receptor expression in relation to monocyte function and disease severity prior to the onset of acute decompensation. TNF-α/IL-6 responses to lipopolysaccharide were attenuated in monocytes from patients with cirrhosis (n = 96) compared with controls (n = 27) and decreased in parallel with disease severity. Concurrently, an AXL-expressing (AXL+) monocyte population expanded. AXL+ cells (CD14+CD16highHLA-DRhigh) were characterised by attenuated TNF-α/IL-6 responses and T cell activation but enhanced efferocytosis and preserved phagocytosis of Escherichia coli. Their expansion correlated with disease severity, complications, infection, and 1-yr mortality. AXL+ monocytes were generated in response to microbial products and efferocytosis in vitro. AXL kinase inhibition and down-regulation reversed attenuated monocyte inflammatory responses in cirrhosis ex vivo. AXL may thus serve as prognostic marker and deserves evaluation as immunotherapeutic target in cirrhosis

The Sliding-singlet Mechanism Revived

It is shown, using a modification of an idea of Sen, that completely realistic supersymmetric grand-unified theories based on SU(6) or larger unitary groups can be constructed using the sliding-singlet mechanism. These models have a simple structure, preserve the successful prediction of $\sin^2 \theta_W$, and can suppress Higgsino-mediated proton decay to an acceptable level in a simple way.Comment: xx pages, LaTeX. Note adde