ABSTRACT
Chronic obstructive pulmonary disease (COPD) is a chronic illness that is widely prevalent within the United States and has been frequently associated with heart failure (HF). COPD is associated with progressive damage and inflammation of the airways leading to airflow obstruction and inadequate gas exchange. HF represents a decline in the normal functioning of the heart resulting in insufficient pumping of blood through the circulatory system. COPD and HF present with similar signs and symptoms with some variation. There are many specific diagnostic tests and treatment modalities which we use to diagnose COPD and HF, but it becomes an issue when you come across a patient who has both conditions simultaneously. For example, attempting to use an X-ray to diagnose HF in a COPD patient is next to impossible because the results are manipulated by the COPD disease process. This is the case with many other diagnostic tests such as an electrocardiogram (ECG), chest radiography (X-ray), B-type natriuretic peptide (BNP), echocardiogram, cardiac magnetic resonance imaging (CMR), pulmonary function test (PFT), arterial blood gas (ABG), and exercise stress testing. When a patient has both COPD and HF, it becomes more difficult to treat. Many treatments for HF have negative impacts on COPD patients and vice-versa, whereas some have also shown positive clinical outcomes in both diseases. It is agreeable that treatment has to be patient-centered and it can vary from case to case depending on the severity of the disease. Ultimately, in this review, we discuss COPD and HF and how they interplay in their diagnostic and treatment modalities to gain a better understanding of how to effectively manage patients who have been diagnosed with both conditions.
ABSTRACT
More than half of the Top 10 supercomputing sites worldwide use GPU accelerators and they are becoming ubiquitous in workstations and edge computing devices. GeNN is a C++ library for generating efficient spiking neural network simulation code for GPUs. However, until now, the full flexibility of GeNN could only be harnessed by writing model descriptions and simulation code in C++. Here we present PyGeNN, a Python package which exposes all of GeNN's functionality to Python with minimal overhead. This provides an alternative, arguably more user-friendly, way of using GeNN and allows modelers to use GeNN within the growing Python-based machine learning and computational neuroscience ecosystems. In addition, we demonstrate that, in both Python and C++ GeNN simulations, the overheads of recording spiking data can strongly affect runtimes and show how a new spike recording system can reduce these overheads by up to 10×. Using the new recording system, we demonstrate that by using PyGeNN on a modern GPU, we can simulate a full-scale model of a cortical column faster even than real-time neuromorphic systems. Finally, we show that long simulations of a smaller model with complex stimuli and a custom three-factor learning rule defined in PyGeNN can be simulated almost two orders of magnitude faster than real-time.
