The
objective of this work is to develop a methodology and associated
platform for nucleic acid detection at the point-of-care (POC) that
is sensitive, user-friendly, affordable, rapid, and robust. The heart
of this system is an acoustic wave sensor, based on a Surface Acoustic
Wave (SAW) or Quartz Crystal Microbalance (QCM) device, which is employed
for the label-free detection of isothermally amplified target DNA.
Nucleic acids amplification and detection is demonstrated inside three
crude human samples, i.e., whole blood, saliva, and nasal swab, spiked
in with 10–100 Salmonella cells. To qualify
for POC applications, a portable platform was developed based on 3D
printing, integrating inside a single box: (i) simple fluidics based
on plastic tubing and a mini peristaltic pump, (ii) a heating plate
combined with disposable reaction tubes for isothermal amplification;
(iii) a mini antenna analyzer operated through a tablet; and (iv)
an acoustic wave device housing unit. The simplicity of the method
combined with smartphone operation and detection, rapid sample-to-answer
analysis time (30 min), and high performance (detection limit 4 ×
103 CFU/ml) in three of the most important human samples
in diagnostics suggest that the methodology could become a tool of
choice for nucleic acid detection at the POC. In addition, the low
cost of the platform and assay holds promise for its adoption in resource
limited areas. The acoustic detection method is shown to give similar
results with a standard colorimetric assay carried out in saliva and
nasal swab but can also be used to detect nucleic acids inside whole
blood, where a colorimetric assay failed to perform.
This article introduces a significance-centric programming model and runtime support that sets the supply voltage in a multicore CPU to sub-nominal values to reduce the energy footprint and provide mechanisms to control output quality. The developers specify the significance of application tasks respecting their contribution to the output quality and provide check and repair functions for handling faults. On a multicore system, we evaluate five benchmarks using an energy model that quantifies the energy reduction. When executing the least-significant tasks unreliably, our approach leads to 20% CPU energy reduction with respect to a reliable execution and has minimal quality degradation.
We introduce a task-based programming model and runtime system that exploit the observation that not all parts of a program are equally significant for the accuracy of the end-result, in order to trade off the quality of program outputs for increased energyefficiency. This is done in a structured and flexible way, allowing for easy exploitation of different points in the quality/energy space, without adversely affecting application performance. The runtime system can apply a number of different policies to decide whether it will execute less-significant tasks accurately or approximately. The experimental evaluation indicates that our system can achieve an energy reduction of up to 83% compared with a fully accurate execution and up to 35% compared with an approximate version employing loop perforation. At the same time, our approach always results in graceful quality degradation.
Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints.Evaluation on a dual-socket 16-core Intel platform using 9 kernels and applications shows that the proposed framework performs very close to an oracle always selecting the optimal configuration, both in terms of energy efficiency and quality of results. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.
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