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Patient-Specific Analysis of Co-expression Networks for Predicting Clinical Outcomes in Breast Cancer
Abstract To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to har- ness the power of differential co-expression network analysis. Despite the promise of differential co-expression network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked.
Water printing of ferroelectric polarization
BFO thin films with switchable ferroelectric polarization were prepared on SrTiO (STO) substrates with conductive (La,Sr)MnO (LSMO) bottom layers. The initial out-of-plane projections of the ferroelectric polarization (upward or downward) can be artificially controlled by designing the termination of the conductive buffer layers37,38, which were characterized using piezoresponse force microscopy (PFM). As shown in Fig.
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