Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis

A rapid and accurate detection of ventricular arrhythmias is essential to take appropriate therapeutic actions when cardiac arrhythmias occur. Furthermore, the accurate discrimination between arrhythmias is also important, provided that the required shocking therapy would not be the same. In this work, the main novelty is the use of the mathematical method known as Topological Data Analysis (TDA) to generate new types of features which can contribute to the improvement of the detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from the standard MIT-BIH and AHA databases. Two input data to the classify are evaluated: TDA features, and Persistence Diagram Image (PDI). Using the reduced TDA-obtained features, a high average accuracy near 99% was observed when discriminating four types of rhythms (98.68% to VF; 99.05% to VT; 98.76% to normal sinus; and 99.09% to Other rhythms) with specificity values higher than 97.16% in all cases. In addition, a higher accuracy of 99.51% was obtained when discriminating between shockable (VT/VF) and non-shockable rhythms (99.03% sensitivity and 99.67% specificity). These results show that the use of TDA-derived geometric features, combined in this case this the k-Nearest Neighbor (kNN) classifier, raises the classification performance above results in previous works. Considering that these results have been achieved without preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapie

An Scalable matrix computing unit architecture for FPGA and SCUMO user design interface

High dimensional matrix algebra is essential in numerous signal processing and machine learning algorithms. This work describes a scalable square matrix-computing unit designed on the basis of circulant matrices. It optimizes data flow for the computation of any sequence of matrix operations removing the need for data movement for intermediate results, together with the individual matrix operations' performance in direct or transposed form (the transpose matrix operation only requires a data addressing modification). The allowed matrix operations are: matrix-by-matrix addition, subtraction, dot product and multiplication, matrix-by-vector multiplication, and matrix by scalar multiplication. The proposed architecture is fully scalable with the maximum matrix dimension limited by the available resources. In addition, a design environment is also developed, permitting assistance, through a friendly interface, from the customization of the hardware computing unit to the generation of the final synthesizable IP core. For N x N matrices, the architecture requires N ALU-RAM blocks and performs O(N*N), requiring N*N +7 and N +7 clock cycles for matrix-matrix and matrix-vector operations, respectively. For the tested Virtex7 FPGA device, the computation for 500 x 500 matrices allows a maximum clock frequency of 346 MHz, achieving an overall performance of 173 GOPS. This architecture shows higher performance than other state-of-the-art matrix computing units

A Novel Systolic Parallel Hardware Architecture for the FPGA Acceleration of Feedforward Neural Networks

New chips for machine learning applications appear, they are tuned for a specific topology, being efficient by using highly parallel designs at the cost of high power or large complex devices. However, the computational demands of deep neural networks require flexible and efficient hardware architectures able to fit different applications, neural network types, number of inputs, outputs, layers, and units in each layer, making the migration from software to hardware easy. This paper describes novel hardware implementing any feedforward neural network (FFNN): multilayer perceptron, autoencoder, and logistic regression. The architecture admits an arbitrary input and output number, units in layers, and a number of layers. The hardware combines matrix algebra concepts with serial-parallel computation. It is based on a systolic ring of neural processing elements (NPE), only requiring as many NPEs as neuron units in the largest layer, no matter the number of layers. The use of resources grows linearly with the number of NPEs. This versatile architecture serves as an accelerator in real-time applications and its size does not affect the system clock frequency. Unlike most approaches, a single activation function block (AFB) for the whole FFNN is required. Performance, resource usage, and accuracy for several network topologies and activation functions are evaluated. The architecture reaches 550 MHz clock speed in a Virtex7 FPGA. The proposed implementation uses 18-bit fixed point achieving similar classification performance to a floating point approach. A reduced weight bit size does not affect the accuracy, allowing more weights in the same memory. Different FFNN for Iris and MNIST datasets were evaluated and, for a real-time application of abnormal cardiac detection, a x256 acceleration was achieved. The proposed architecture can perform up to 1980 Giga operations per second (GOPS), implementing the multilayer FFNN of up to 3600 neurons per layer in a single chip. The architecture can be extended to bigger capacity devices or multi-chip by the simple NPE ring extension

Moving Learning Machine Towards Fast Real-Time Applications: A High-Speed FPGA-based Implementation of the OS-ELM Training Algorithm

Currently, there are some emerging online learning applications handling data streams in real-time. The On-line Sequential Extreme Learning Machine (OS-ELM) has been successfully used in real-time condition prediction applications because of its good generalization performance at an extreme learning speed, but the number of trainings by a second (training frequency) achieved in these continuous learning applications has to be further reduced. This paper proposes a performance-optimized implementation of the OS-ELM training algorithm when it is applied to real-time applications. In this case, the natural way of feeding the training of the neural network is one-by-one, i.e., training the neural network for each new incoming training input vector. Applying this restriction, the computational needs are drastically reduced. An FPGA-based implementation of the tailored OS-ELMalgorithm is used to analyze, in a parameterized way, the level of optimization achieved. We observed that the tailored algorithm drastically reduces the number of clock cycles consumed for the training execution up to approximately the 1%. This performance enables high-speed sequential training ratios, such as 14 KHz of sequential training frequency for a 40 hidden neurons SLFN, or 180 Hz of sequential training frequency for a 500 hidden neurons SLFN. In practice, the proposed implementation computes the training almost 100 times faster, or more, than other applications in the bibliography. Besides, clock cycles follows a quadratic complexity O(N 2), with N the number of hidden neurons, and are poorly influenced by the number of input neurons. However, it shows a pronounced sensitivity to data type precision even facing small-size problems, which force to use double floating-point precision data types to avoid finite precision arithmetic effects. In addition, it has been found that distributed memory is the limiting resource and, thus, it can be stated that current FPGA devices can support OS-ELM-based on-chip learning of up to 500 hidden neurons. Concluding, the proposed hardware implementation of the OS-ELM offers great possibilities for on-chip learning in portable systems and real-time applications where frequent and fast training is required

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitoring. In turn, this paper proposes an automatic classification procedure capable of assisting neurosurgeons online, during the resective epilepsy surgery, to refine the localization of the epileptogenic area to be resected, if they have doubts. This goal requires a real-time implementation with as low a computational cost as possible. For that reason, this work proposes both a feature set and a classifier model that minimizes the computational load while preserving the classification accuracy at 95.5%, a level similar to previous works. In addition, the classification procedure has been implemented on a FPGA device to determine its resource needs and throughput. Thus, it can be concluded that such a device can embed the whole classification process, from accepting raw signals to the delivery of the classification results in a cost-effective Xilinx Spartan-6 FPGA device. This real-time implementation begins providing results after a 5 s latency, and later, can deliver floating-point classification results at 3.5 Hz rate, using overlapped time-windows

GoldenBraid 2.0: a comprehensive DNA assembly framework for plant synthetic biology

[EN] Plant synthetic biology aims to apply engineering principles to plant genetic design. One strategic requirement of plant synthetic biology is the adoption of common standardized technologies that facilitate the construction of increasingly complex multigene structures at the DNA level while enabling the exchange of genetic building blocks among plant bioengineers. Here, we describe GoldenBraid 2.0 (GB2.0), a comprehensive technological framework that aims to foster the exchange of standard DNA parts for plant synthetic biology. GB2.0 relies on the use of type IIS restriction enzymes for DNA assembly and proposes a modular cloning schema with positional notation that resembles the grammar of natural languages. Apart from providing an optimized cloning strategy that generates fully exchangeable genetic elements for multigene engineering, the GB2.0 toolkit offers an ever-growing open collection of DNA parts, including a group of functionally tested, premade genetic modules to build frequently used modules like constitutive and inducible expression cassettes, endogenous gene silencing and protein-protein interaction tools, etc. Use of the GB2.0 framework is facilitated by a number of Web resources that include a publicly available database, tutorials, and a software package that provides in silico simulations and laboratory protocols for GB2.0 part domestication and multigene engineering. In short, GB2.0 provides a framework to exchange both information and physical DNA elements among bioengineers to help implement plant synthetic biology projects.This work was supported by the Spanish Ministry of Economy and Competitiveness (grant no. BIO2010-15384), by a Research Personnel in Training fellowship to A.S.-P., and by a Junta de Ampliacion de Estudios fellowship to M.V.-V.Sarrion-Perdigones, A.; Vázquez Vilar, M.; Palací Bataller, J.; Castelijns, B.; Forment Millet, JJ.; Ziarsolo Areitioaurtena, P.; Blanca Postigo, JM.... (2013). GoldenBraid 2.0: a comprehensive DNA assembly framework for plant synthetic biology. Plant Physiology. 162(3):1618-1631. https://doi.org/10.1104/pp.113.217661S16181631162

Theologicae theses ex prima parte ... Divi Thomae Aquinatis : quas sibi ex primo anno theologici cursus comparavit, publicaeque disputationi

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 2016Año en tít.: 1791Sign.: [ ]2, A-D4, E6Port. a dos tinta

Chemokine Receptor Ccr6 Deficiency Alters Hepatic Inflammatory Cell Recruitment and Promotes Liver Inflammation and Fibrosis.

Chronic liver diseases are characterized by a sustained inflammatory response in which chemokines and chemokine-receptors orchestrate inflammatory cell recruitment. In this study we investigated the role of the chemokine receptor CCR6 in acute and chronic liver injury. In the absence of liver injury Ccr6-/- mice presented a higher number of hepatic macrophages and increased expression of pro-inflammatory cytokines and M1 markers Tnf-α, Il6 and Mcp1. Inflammation and cell recruitment were increased after carbon tetrachloride-induced acute liver injury in Ccr6-/- mice. Moreover, chronic liver injury by carbon tetrachloride in Ccr6-/- mice was associated with enhanced inflammation and fibrosis, altered macrophage recruitment, enhanced CD4+ cells and a reduction in Th17 (CD4+IL17+) and mature dendritic (MHCII+CD11c+) cells recruitment. Clodronate depletion of macrophages in Ccr6-/- mice resulted in a reduction of hepatic pro-inflammatory and pro-fibrogenic markers in the absence and after liver injury. Finally, increased CCR6 hepatic expression in patients with alcoholic hepatitis was found to correlate with liver expression of CCL20 and severity of liver disease. In conclusion, CCR6 deficiency affects hepatic inflammatory cell recruitment resulting in the promotion of hepatic inflammation and fibrosis

Kinase analysis in alcoholic hepatitis identifies p90RSK as a potential mediator of liver fibrogenesis

Alcoholic hepatitis (AH) is often associated with advanced fibrosis, which negatively impacts survival. We aimed at identifying kinases deregulated in livers from patients with AH and advanced fibrosis in order to discover novel molecular targets

CCL20 mediates lipopolysaccharide induced liver injury and is a potential driver of inflammation and fibrosis in alcoholic hepatitis

OBJECTIVE: Chemokines are known to play an important role in the pathophysiology of alcoholic hepatitis (AH), a form of acute-on-chronic liver injury frequently mediated by gut derived lipopolysaccharide (LPS). In our study, we hypothesise that chemokine CCL20, one of the most upregulated chemokines in patients with AH, is implicated in the pathogenesis of AH by mediating LPS induced liver injury. DESIGN: CCL20 gene expression and serum levels and their correlation with disease severity were assessed in patients with AH. Cellular sources of CCL20 and its biological effects were evaluated in vitro and in vivo in chronic, acute and acute-on-chronic experimental models of carbon tetrachloride and LPS induced liver injury. RNA interference technology was used to knockdown CCL20 in vivo. RESULTS: CCL20 hepatic and serum levels were increased in patients with AH and correlated with the degree of fibrosis, portal hypertension, endotoxaemia, disease severity scores and short term mortality. Moreover, CCL20 expression was increased in animal models of liver injury and particularly under acute-on-chronic conditions. Macrophages and hepatic stellate cells (HSCs) were identified as the main CCL20 producing cell types. Silencing CCL20 in vivo reduced LPS induced aspartate aminotransferase and lactate dehydrogenase serum levels and hepatic proinflammatory and profibrogenic genes. CCL20 induced proinflammatory and profibrogenic effects in cultured primary HSCs. CONCLUSIONS: Our results suggest that CCL20 upregulation is strongly associated with LPS and may not only represent a new potential biomarker to predict outcome in patients with AH but also an important mediator linking hepatic inflammation, injury and fibrosis in AH