Cerebral Autoregulation Real-Time Monitoring.

Cerebral autoregulation is a mechanism which maintains constant cerebral blood flow (CBF) despite changes in mean arterial pressure (MAP). Assessing whether this mechanism is intact or impaired and determining its boundaries is important in many clinical settings, where primary or secondary injuries to the brain may occur. Herein we describe the development of a new ultrasound tagged near infra red light monitor which tracks CBF trends, in parallel, it continuously measures blood pressure and correlates them to produce a real time autoregulation index. Its performance is validated in both in-vitro experiment and a pre-clinical case study. Results suggest that using such a tool, autoregulation boundaries as well as its impairment or functioning can be identified and assessed. It may therefore assist in individualized MAP management to ensure adequate organ perfusion and reduce the risk of postoperative complications, and might play an important role in patient care

The Bitlet model: a parameterized analytical model to compare PIM and CPU systems

Currently, data-intensive applications are gaining popularity. Together with this trend, processing-in-memory (PIM)-based systems are being given more attention and have become more relevant. This article describes an analytical modeling tool called Bitlet that can be used in a parameterized fashion to estimate the performance and power/energy of a PIM-based system and, thereby, assess the affinity of workloads for PIM as opposed to traditional computing. The tool uncovers interesting trade-offs between, mainly, the PIM computation complexity (cycles required to perform a computation through PIM), the amount of memory used for PIM, the system memory bandwidth, and the data transfer size. Despite its simplicity, the model reveals new insights when applied to real-life examples. The model is demonstrated for several synthetic examples and then applied to explore the influence of different parameters on two systems - IMAGING and FloatPIM. Based on the demonstrations, insights about PIM and its combination with a CPU are provided.This work was supported by the European Research Council through the European Union’s Horizon 2020 Research and Innovation Programme under Grant No. 757259 and by the Israel Science Foundation under Grant No. 1514/17

optical and acoustic properties of the phantom and the tissue[32, 33].

<p>optical and acoustic properties of the phantom and the tissue[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161907#pone.0161907.ref032" target="_blank">32</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161907#pone.0161907.ref033" target="_blank">33</a>].</p

Boxplot for autoregulation index values calculated for the two to cFLOW-AR sensors.

<p>Pink astrics represent averaged ARI for each condition. A distinct separation between the conditions is apparent.</p

Averaged CFI values obtained in <i>in-vitro</i> experiment.

<p>Averaged CFI values obtained in <i>in-vitro</i> experiment.</p

Cerebral Autoregulation Real-Time Monitoring - Fig 5

<p>Left—MAP and CFI data over time throughout the study. MAP was increased to 140mmHg followed by a return to baseline and a decrease to 40mmHg. Dashed green lines represent initial injections of Phnylephrine and Nitroprusside respectively. Blue points represent all MAP values. Red points are associated with periods in which the algorithm identified a significant MAP change and a correlation index (ARI) can be calculated. Right—Scatter plot of CFI versus MAP revealing two distinct slopes obtained for values under of over 100mmHg. This point was defined as the upper limit of autoregulation (ULA).</p