Classification of kinematic and electromyographic signals associated with pathological tremor using machine and deep learning.

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps

LDL-Induced Impairment of Human Vascular Smooth Muscle Cells Repair Function Is Reversed by HMG-CoA Reductase Inhibition

Growing human atherosclerotic plaques show a progressive loss of vascular smooth muscle cells (VSMC) becoming soft and vulnerable. Lipid loaded-VSMC show impaired vascular repair function and motility due to changes in cytoskeleton proteins involved in cell-migration. Clinical benefits of statins reducing coronary events have been related to repopulation of vulnerable plaques with VSMC. Here, we investigated whether HMG-CoA reductase inhibition with rosuvastatin can reverse the effects induced by atherogenic concentrations of LDL either in the native (nLDL) form or modified by aggregation (agLDL) on human VSMC motility. Using a model of wound repair, we showed that treatment of human coronary VSMC with rosuvastatin significantly prevented (and reversed) the inhibitory effect of nLDL and agLDL in the repair of the cell depleted areas. In addition, rosuvastatin significantly abolished the agLDL-induced dephosphorylation of myosin regulatory light chain as demonstrated by 2DE-electrophoresis and mass spectrometry. Besides, confocal microscopy showed that rosuvastatin enhances actin-cytoskeleton reorganization during lipid-loaded-VSMC attachment and spreading. The effects of rosuvastatin on actin-cytoskeleton dynamics and cell migration were dependent on ROCK-signalling. Furthermore, rosuvastatin caused a significant increase in RhoA-GTP in the cytosol of VSMC. Taken together, our study demonstrated that inhibition of HMG-CoA reductase restores the migratory capacity and repair function of VSMC that is impaired by native and aggregated LDL. This mechanism may contribute to the stabilization of lipid-rich atherosclerotic plaques afforded by statins

Gene therapy for monogenic liver diseases: clinical successes, current challenges and future prospects

Over the last decade, pioneering liver-directed gene therapy trials for haemophilia B have achieved sustained clinical improvement after a single systemic injection of adeno-associated virus (AAV) derived vectors encoding the human factor IX cDNA. These trials demonstrate the potential of AAV technology to provide long-lasting clinical benefit in the treatment of monogenic liver disorders. Indeed, with more than ten ongoing or planned clinical trials for haemophilia A and B and dozens of trials planned for other inherited genetic/metabolic liver diseases, clinical translation is expanding rapidly. Gene therapy is likely to become an option for routine care of a subset of severe inherited genetic/metabolic liver diseases in the relatively near term. In this review, we aim to summarise the milestones in the development of gene therapy, present the different vector tools and their clinical applications for liver-directed gene therapy. AAV-derived vectors are emerging as the leading candidates for clinical translation of gene delivery to the liver. Therefore, we focus on clinical applications of AAV vectors in providing the most recent update on clinical outcomes of completed and ongoing gene therapy trials and comment on the current challenges that the field is facing for large-scale clinical translation. There is clearly an urgent need for more efficient therapies in many severe monogenic liver disorders, which will require careful risk-benefit analysis for each indication, especially in paediatrics

Single-cell analysis tools for drug discovery and development

The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed

Environmental effects of ozone depletion, UV radiation and interactions with climate change : UNEP Environmental Effects Assessment Panel, update 2017

Peer reviewe

Approximate Reconstructions of Perturbed Rational Planar Cubics

This paper is devoted to a problem from geometric modelling and related applications when exact symbolic computations are sometimes used also on objects given inexactly, i.e., when it is not adequately respected that numerical or input errors may significantly influence fundamental properties of considered algebraic varieties, including e.g. their rationality. We formulate a simple algorithm for an approximation of a non-rational planar cubic which is assumed to be a perturbation of some unknown rational planar cubic. The input curve is given by a perturbed polynomial or by perturbed points sampled from the original curve. The algorithm consists of two main parts. First, we suggest geometric methods for the estimation of a singular point of the original curve. Then we select from the six-dimensional subspace of all rational cubics with a given singular point a suitable one that may also satisfy some further criteria. The designed method is presented on several commented examples