Supplementary Table 9, column 'Edoxaban', row 'eGFR category', '95 mL/min' (page 15). The cell should be coloured green instead of yellow. It should also read "60 mg"instead of "60 mg (use with caution in 'supranormal' renal function)."In the above-indicated cell, a footnote has also been added to state: "Edoxaban should be used in patients with high creatinine clearance only after a careful evaluation of the individual thromboembolic and bleeding risk."Supplementary Table 9, column 'Edoxaban', row 'Dose reduction in selected patients' (page 16). The cell should read "Edoxaban 60 mg reduced to 30 mg once daily if any of the following: creatinine clearance 15-50 mL/min, body weight <60 kg, concomitant use of dronedarone, erythromycin, ciclosporine or ketokonazole"instead of "Edoxaban 60 mg reduced to 30 mg once daily, and edoxaban 30 mg reduced to 15mg once daily, if any of the following: creatinine clearance of 30-50 mL/min, body weight <60 kg, concomitant us of verapamil or quinidine or dronedarone."

/pdf/2020-esc-guidelines-for-the-diagnosis-and-management-of-38dpic1xu8.pdf

2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS).

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

https://link.springer.com/content/pdf/10.1007/s10618-019-00619-1.pdf

Deep learning for time series classification: a review

The 14-3-3 proteins are a family of conserved regulatory molecules expressed in all eukaryotic cells. A striking feature of the 14-3-3 proteins is their ability to bind a multitude of functionally diverse signaling proteins, including kinases, phosphatases, and transmembrane receptors. This plethora of interacting proteins allows 14-3-3 to play important roles in a wide range of vital regulatory processes, such as mitogenic signal transduction, apoptotic cell death, and cell cycle control. In this review, we examine the structural basis for 14-3-3-ligand interactions, proposed functions of 14-3-3 in various signaling pathways, and emerging views of mechanisms that regulate 14-3-3 actions.

14-3-3 Proteins: Structure, Function, and Regulation

After encountering antigen, helper T (T(H)) cells undergo differentiation to effector cells, which can secrete high levels of interferon-gamma, interleukin-4 (IL-4), IL-10 and other immunomodulators. How T(H) cells acquire, and remember, new patterns of gene expression is an area of intensive investigation. The process is remarkably plastic, with cytokines being key regulators. Extrinsic signals seem to be integrated into cell-intrinsic programming, in what is becoming an intriguing story of regulated development. We summarize the latest insights into mechanisms that govern the lineage choices that are made during T(H)-cell responses to foreign pathogens.

/pdf/the-lineage-decisions-of-helper-t-cells-5g789x1rf0.pdf

The lineage decisions of helper T cells

Engagement of the NKG2D receptor by tumour-associated ligands may promote tumour rejection by stimulating innate and adaptive lymphocyte responses. In humans, NKG2D is expressed on most natural killer cells, gammadelta T cells and CD8alphabeta T cells. Ligands of NKG2D include the major histocompatibility complex class I homologues MICA and MICB, which function as signals of cellular stress. These molecules are absent from most cells and tissues but can be induced by viral and bacterial infections and are frequently expressed in epithelial tumours. MIC engagement of NKG2D triggers natural killer cells and costimulates antigen-specific effector T cells. Here we show that binding of MIC induces endocytosis and degradation of NKG2D. Expression of NKG2D is reduced markedly on large numbers of tumour-infiltrating and matched peripheral blood T cells from individuals with cancer. This systemic deficiency is associated with circulating tumour-derived soluble MICA, causing the downregulation of NKG2D and in turn severe impairment of the responsiveness of tumour-antigen-specific effector T cells. This mode of T-cell silencing may promote tumour immune evasion and, by inference, compromise host resistance to infections.

Tumour-derived soluble MIC ligands impair expression of NKG2D and T-cell activation

http://www.diva-portal.org/smash/get/diva2:710518/FULLTEXT02

A review of unsupervised feature learning and deep learning for time-series modeling ☆

Histamine mediates its physiological function through binding to four known histamine receptors. Here, we describe the first selective antagonist of the histamine H4 receptor, the newest member of the histamine receptor family, and provide evidence that such antagonists have anti-inflammatory activity in vivo. 1-[(5-chloro-1 H -indol-2-yl)carbonyl]-4-methylpiperazine (JNJ 7777120) has a K i of 4.5 nM versus the human receptor and a p A 2 of 8.1. It is equipotent against the human, mouse, and rat receptors. It exhibits at least 1000-fold selectivity over H1, H2, or H3 receptors and has no cross-reactivity against 50 other targets. This compound has an oral bioavailability of ∼30% in rats and 100% in dogs, with a half-life of ∼3 h in both species. JNJ 7777120 blocks histamine-induced chemotaxis and calcium influx in mouse bone marrow-derived mast cells. In addition, it can block the histamine-induced migration of tracheal mast cells from the connective tissue toward the epithelium in mice. JNJ 7777120 significantly blocks neutrophil infiltration in a mouse zymosan-induced peritonitis model. This model is reported to be mast cell-dependent, which suggests that the compound effect may be mediated by mast cells. These results indicate that the histamine H4 receptor plays a role in the inflammatory process. Selective H4 receptor antagonists like JNJ 7777120 may have the potential to be useful in treating inflammation in humans.

A Potent and Selective Histamine H4 Receptor Antagonist with Anti-Inflammatory Properties

Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets.

/pdf/sleep-stage-classification-using-unsupervised-feature-23u6thm8ox.pdf

Sleep stage classification using unsupervised feature learning

Major histocompatibility complex (MHC) class II molecules are cell surface proteins that present peptides to CD4(+) T cells. In addition to these wellcharacterized molecules, two other class II-like proteins are produced from the class II region of the MHC, HLA-DM (DM) and HLA-DO (DO) (called H2-M, or H2-DM and H2-O in the mouse). The function of DM is well established; it promotes peptide loading of class II molecules in the endosomal/lysosomal system by catalyzing the release of CLIP peptides (derived from the class II-associated invariant chain) in exchange for more stably binding peptides. While DM is present in all class II- expressing antigen presenting cells, DO is expressed mainly in B cells. In this cell type the majority of DM molecules are not present as free heterodimers but are instead associated with DO in tight heterotetrameric complexes. The association with DM is essential for the intracellular transport of DO, and the two molecules remain associated in the endosomal system. DO can clearly modify the peptide exchange activity of DM both in vitro and in vivo, but the physiological relevance of this interaction is still only partly understood.

Nonclassical MHC Class II Molecules

Natural killer cell tolerance is maintained by the interaction of killer inhibitory receptors (KIRs) with self-major histocompatibility complex class I gene products. A subset of T cells also expresses inhibitory receptors, but the functional significance of these receptors on T cells is unclear1,2,3. Here we show that, in the absence of T-cell receptor (TCR) engagement, KIRs expressed on CD8+ T cells are slowly downregulated by KIR ligands expressed on antigen-presenting cells. The resulting expression levels of KIR are no longer able to inhibit T-cell function. In contrast, TCR engagement sustains KIR expression, and re-induces functional levels of KIR expression after ligand-induced downregulation of KIR. Our data indicate that KIR expression on CD8+ T cells in vivo may be maintained through continuous encounters with antigen. As KIR-mediated inhibition of T-cell activation can be bypassed at high antigen concentrations, dynamic KIR expression may mediate T-cell tolerance to self-antigens by sparing self-reactive T cells, thus enabling them to mediate potentially useful immune functions to quantitatively or qualitatively different antigens.

Lars Karlsson

Papers

A review of unsupervised feature learning and deep learning for time-series modeling ☆

A Potent and Selective Histamine H4 Receptor Antagonist with Anti-Inflammatory Properties

Sleep stage classification using unsupervised feature learning

Nonclassical MHC Class II Molecules

KIR expression on self-reactive CD8 + T cells is controlled by T-cell receptor engagement