One-dimensional modelling of pulse wave propagation in human airway bifurcations in space-time variables

Airflow in the respiratory system is complicated as it goes through various regions with different geometries and mechanical properties. Three-dimensional (3-D) simulations are typically limited to local areas of the system because of their high computational cost. On the other hand, the one-dimensional (1-D) equations of flow in compliant tubes offer a good compromise between accuracy and computational cost when a global assessment of airflow in the system is required. The aim of the current study is to apply the 1-D formulation in space and time variables to study the propagation of a pulse wave in human airways; first in a simple system composed of just one bifurcation, trachea-main bronchi, according to the symmetrical Weibel model. Then extending the system to include a further generation, the bronchi branches. Pulse waveforms carry information about the functionality and morphology of the respiratory system and the 1-D modelling, in terms of space and time variables, represents an innovative approach for respiratory response interpretation. 1-D modelling in space-time variables has been extensively applied to simulate blood pressure and flow in the cardiovascular system. This work represents the first attempt to apply this formulation to study pulse waveforms in the human bronchial tree

Newtonian Flow in Converging-Diverging Capillaries

The one-dimensional Navier-Stokes equations are used to derive analytical expressions for the relation between pressure and volumetric flow rate in capillaries of five different converging-diverging axisymmetric geometries for Newtonian fluids. The results are compared to previously-derived expressions for the same geometries using the lubrication approximation. The results of the one-dimensional Navier-Stokes are identical to those obtained from the lubrication approximation within a non-dimensional numerical factor. The derived flow expressions have also been validated by comparison to numerical solutions obtained from discretization with numerical integration. Moreover, they have been certified by testing the convergence of solutions as the converging-diverging geometries approach the limiting straight geometry.Comment: 23 pages, 5 figures, 1 table. This is an extended and improved version. arXiv admin note: substantial text overlap with arXiv:1006.151

Developing an AI IoT application with open software on a RISC-V SoC

RISC-V is an emergent architecture that is gaining strength in low-power IoT applications. The stabilization of the architectural extensions and the start of commercialization of RISC-V based SOCs, like the Kendryte K210, raises the question of whether this open standard will facilitate the development of applications in specific markets or not.In this paper we evaluate the development environments, the toolchain, the debugging processes related to the Sipeed MAIX Go development board, as well as the standalone SDK and the Micropython port for the Kendryte K210. The training pipeline for the built-in convolutional neural network accelerator, with support for Tiny YOLO v2, has also been studied. In order to evaluate all the above aspects in depth, two low-cost, low-power, IoT edge applications based on AI have been developed. The first one is capable of recognizing movement in a house and autonomously identify whether it was caused by a human or by a house pet, like for example a dog or a cat. In the context of the current COVID-19 pandemic, the second application is capable of labeling whether a pedestrian is wearing a face mask or not, doing real-time object recognition at a mean rate of 13 FPS. Throughout the process, we can conclude that, despite the potential of the hardware and its excellent performance/cost ratio, the documentation for developers is scarce, the development environments are in low maturity levels, and the debugging processes are sometimes nonexistent

Parametric uncertainty analysis of pulse wave propagation in a model of a human arterial network

Accepted versio

The challenges of the genome-based identification of antifungal resistance in the clinical routine

The increasing number of chronic and life-threatening infections caused by antimicrobial resistant fungal isolates is of critical concern. Low DNA sequencing cost may facilitate the identification of the genomic profile leading to resistance, the resistome, to rationally optimize the design of antifungal therapies. However, compared to bacteria, initiatives for resistome detection in eukaryotic pathogens are underdeveloped. Firstly, reported mutations in antifungal targets leading to reduced susceptibility must be extensively collected from the literature to generate comprehensive databases. This information should be complemented with specific laboratory screenings to detect the highest number possible of relevant genetic changes in primary targets and associations between resistance and other genomic markers. Strikingly, some drug resistant strains experience high-level genetic changes such as ploidy variation as much as duplications and reorganizations of specific chromosomes. Such variations involve allelic dominance, gene dosage increments and target expression regime effects that should be explicitly parameterized in antifungal resistome prediction algorithms. Clinical data indicate that predictors need to consider the precise pathogen species and drug levels of detail, instead of just genus and drug class. The concomitant needs for mutation accuracy and assembly quality assurance suggest hybrid sequencing approaches involving third-generation methods will be utilized. Moreover, fatal fast infections, like fungemia and meningitis, will further require both sequencing and analysis facilities are available in-house. Altogether, the complex nature of antifungal resistance demands extensive sequencing, data acquisition and processing, bioinformatic analysis pipelines, and standard protocols to be accomplished prior to genome-based protocols are applied in the clinical setting

On the Mechanics Underlying the Reservoir-Excess Separation in Systemic Arteries and their Implications for Pulse Wave Analysis

Several works have separated the pressure waveform p in systemic arteries into reservoir pr and excess pexc components, p = pr + pexc, to improve pulse wave analysis, using windkessel models to calculate the reservoir pressure. However, the mechanics underlying this separation and the physical meaning of pr and pexc have not yet been established. They are studied here using the time-domain, inviscid and linear one-dimensional (1-D) equations of blood flow in elastic vessels. Solution of these equations in a distributed model of the 55 larger human arteries shows that pr calculated using a two-element windkessel model is space-independent and well approximated by the compliance-weighted space-average pressure of the arterial network. When arterial junctions are well-matched for the propagation of forward-travelling waves, pr calculated using a three-element windkessel model is space-dependent in systole and early diastole and is made of all the reflected waves originated at the terminal (peripheral) reflection sites, whereas pexc is the sum of the rest of the waves, which are obtained by propagating the left ventricular flow ejection without any peripheral reflection. In addition, new definitions of the reservoir and excess pressures from simultaneous pressure and flow measurements at an arbitrary location are proposed here. They provide valuable information for pulse wave analysis and overcome the limitations of the current two- and three-element windkessel models to calculate pr

Accelerating Sequence Alignments Based on FM-Index Using the Intel KNL Processor

FM-index is a compact data structure suitable for fast matches of short reads to large reference genomes. The matching algorithm using this index exhibits irregular memory access patterns that cause frequent cache misses, resulting in a memory bound problem. This paper analyzes different FM-index versions presented in the literature, focusing on those computing aspects related to the data access. As a result of the analysis, we propose a new organization of FM-index that minimizes the demand for memory bandwidth, allowing a great improvement of performance on processors with high-bandwidth memory, such as the second-generation Intel Xeon Phi (Knights Landing, or KNL), integrating ultra high-bandwidth stacked memory technology. As the roofline model shows, our implementation reaches 95% of the peak random access bandwidth limit when executed on the KNL and almost all the available bandwidth when executed on other Intel Xeon architectures with conventional DDR memory. In addition, the obtained throughput in KNL is much higher than the results reported for GPUs in the literature. IEE

An impedance pneumography signal quality index: Design, assessment and application to respiratory rate monitoring.

Impedance pneumography (ImP) is widely used for respiratory rate (RR) monitoring. However, ImP-derived RRs can be imprecise. The aim of this study was to develop a signal quality index (SQI) for the ImP signal, and couple it with a RR algorithm, to improve RR monitoring. An SQI was designed which identifies candidate breaths and assesses signal quality using: the variation in detected breath durations, how well peaks and troughs are defined, and the similarity of breath morphologies. The SQI categorises 32 s signal segments as either high or low quality. Its performance was evaluated using two critical care datasets. RRs were estimated from high-quality segments using a RR algorithm, and compared with reference RRs derived from manual annotations. The SQI had a sensitivity of 77.7 %, and specificity of 82.3 %. RRs estimated from segments classified as high quality were accurate and precise, with mean absolute errors of 0.21 and 0.40 breaths per minute (bpm) on the two datasets. Clinical monitor RRs were significantly less precise. The SQI classified 34.9 % of real-world data as high quality. In conclusion, the proposed SQI accurately identifies high-quality segments, and RRs estimated from those segments are precise enough for clinical decision making. This SQI may improve RR monitoring in critical care. Further work should assess it with wearable sensor data.This work was supported by a UK Engineering and Physical Sciences Research Council (EPSRC) Impact Acceleration Award awarded to PHC; the EPSRC [EP/H019944/1]; the Wellcome EPSRC Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z]; the Oxford and King’s College London Centres of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC under grants [WT88877/Z/09/Z] and [WT088641/Z/09/Z]; the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s & St Thomas’ NHS Foundation Trust and King’s College London; the NIHR Oxford Biomedical Research Centre Programme; a Royal Academy of Engineering Research Fellowship (RAEng) awarded to DAC; and EPSRC grants EP/P009824/1 and EP/N020774/1 to DAC

Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants.

OBJECTIVE: Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. APPROACH: Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. MAIN RESULTS: Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of  <250 Hz and  <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender. SIGNIFICANCE: Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available