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Forecast-Based Sample Preparation Algorithm for Unbalanced Splitting Correction on DMFBs

机译：DMFB不平衡分裂校正的基于预测的样本制备算法

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摘要

Sample preparation is regarded as one of essential processing steps in most biochemical assays. In the past decade, numerous techniques have been presented to deal with sample preparation under the (1:1) mixing model on digital microfluidic biochips (DMFBs) for various optimization goals. However, most of previous works assumed that mixing-then-splitting would get two identical output droplets, which is not always true due to unbalanced splitting. As a consequence, those works may fail to provide correct solutions at the presence of unbalanced splitting. Several methods have been proposed to deal with this issue. Nevertheless, some of them rely on hypotheses that may not be practical, while the others demand extra reactants or special hardware. In this paper, we propose a new probability-based sample preparation algorithm for unbalanced splitting correction. Our new algorithm not only guarantees a correct solution, but requires neither extra reactants nor on-chip special hardware. Experimental results show that the effect of unbalanced splitting can be eliminated only at the cost of 20% more operation steps. That is, the proposed algorithm is both reliable and efficient.
机译：在大多数生化分析中，样品制备被视为必不可少的处理步骤之一。在过去的十年中，已经提出了许多技术来处理数字微流体生物芯片（DMFB）上（1：1）混合模型下的样品制备，以实现各种优化目标。但是，大多数以前的工作都假设混合然后分裂会得到两个相同的输出液滴，由于分裂不平衡，这种情况并不总是正确的。结果，在存在不平衡分裂的情况下，这些作品可能无法提供正确的解决方案。已经提出了几种方法来解决这个问题。然而，其中一些依赖于可能不切实际的假设，而另一些则需要额外的反应物或特殊的硬件。在本文中，我们提出了一种新的基于概率的不平衡分裂校正样本制备算法。我们的新算法不仅保证了正确的解决方案，而且不需要额外的反应物或片上特殊硬件。实验结果表明，只有以增加20％的操作步骤为代价，才能消除不平衡分裂的影响。即，所提出的算法既可靠又有效。

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