The Elmore delay is an extremely popular delay metric, particularly for RC tree analysis. The widespread usage of this metric is mainly attributable to it being the most accurate delay measure that is a simple analytical function of the circuit parameters. The only drawbacks to this delay metric are the uncertainty as to whether it is an optimistic or a pessimistic estimate, and the restriction to step response delay estimation.In this paper, we prove that the Elmore delay is an absolute upper bound on the 50% delay of an RC tree response. Moreover, we prove that this bound holds for input signals other than steps, and that the actual delay asymptotically approaches the Elmore delay as the input signal rise time increases. A lower bound on the delay is also developed using the Elmore delay and the second moment of the impulse response. The utility of this bound is for understanding the accuracy and the limitations of the Elmore delay metric as we use it for design automation.

We have developed a synthetic polymer interface for the long-term self-renewal of human embryonic stem cells (hESCs) in defined media. We successfully cultured hESCs on hydrogel interfaces of aminopropylmethacrylamide (APMAAm) for over 20 passages in chemically-defined mTeSR™ 1 media and demonstrated pluripotency of multiple hESC lines with immunostaining and quantitative RT-PCR studies. Results for hESC proliferation and pluripotency markers were both qualitatively and quantitatively similar to cells cultured on Matrigel™ -coated substrates. Mechanistically, it was resolved that bovine serum albumin (BSA) in the mTeSR™ 1 media was critical for cell adhesion on APMAAm hydrogel interfaces. This study uniquely identified a robust long-term culture surface for the self-renewal of hESCs without the use of biologic coatings (e.g., peptides, proteins, or Matrigel™) in completely chemically-defined media that employed practical culturing techniques amenable to clinical-scale cell expansion.

Adding bone marrow-derived mesenchymal stromal cells (bmMSCs) to endothelialized collagen gel modules resulted in mature vessel formation, presumably caused in part by the observed display of pericyte-like behavior for the transplanted GFP(+) bmMSCs. A previous study determined that rat aortic endothelial cells (RAECs) delivered on the surface of small (∼0.8 mm long×0.5 mm diameter) collagen gel cylinders (microtissues, modular tissue engineering) formed vessels after transplantation into immunosuppressed Sprague-Dawley (SD) rats. Although the RAECs formed vessels in this allogeneic transplant model, there was a robust inflammatory response and the vessels that formed were leaky as shown by microcomputed tomography (microCT) perfusion studies. In vitro assays showed that SD rat bmMSCs embedded into the collagen gel modules increased the extent of EC proliferation and enhanced EC sprouting. In vivo, although vessel number was not affected, the new vessels formed by the bmMSCs and RAECs were more stable and leaked less in the microCT perfusion analysis than vessels formed by implanted RAECs alone. Addition of the bmMSCs also decreased the total number of CD68(+) macrophages that infiltrated the implant and changed the distribution of CD163(+) (M2) macrophages so that they were found within the newly developed vascularized tissue. Most interestingly, the bmMSCs became smooth muscle actin positive and migrated to surround the EC layer of the vessel, which is the location typical of pericytes. The combination of these two effects was presumed to be the cause of improved vascularity when bmMSCs were embedded in the EC-coated modules. Further exploration of these observations is warranted to exploit modular tissue engineering as a means of forming large vascularized functional tissues using microtissue components.

Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.

The Elmore delay is an extremely popular delay metric, particularly for RC tree analysis. The widespread usage of this metric is mainly attributable to it being the most accurate delay measure that is a simple analytical function of the circuit parameters. The only drawbacks to this delay metric are the uncertainty as to whether it is an optimistic or a pessimistic estimate, and the restriction to step response delay estimation.In this paper, we prove that the Elmore delay is an absolute upper bound on the 50% delay of an RC tree response. Moreover, we prove that this bound holds for input signals other than steps, and that the actual delay asymptotically approaches the Elmore delay as the input signal rise time increases. A lower bound on the delay is also developed using the Elmore delay and the second moment of the impulse response. The utility of this bound is for understanding the accuracy and the limitations of the Elmore delay metric as we use it for design automation.