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Abstract Background RNA sequencing is a powerful approach to quantify the genome-wide distribution of mRNA molecules in a population to gain deeper understanding of cellular functions and phenotypes. However, unlike eukaryotic cells, mRNA sequencing of bacterial samples is more challenging due to the absence of a poly-A tail that typically enables efficient capture and enrichment of mRNA from the abundant rRNA molecules in a cell. Moreover, bacterial cells frequently contain 100-fold lower quantities of RNA compared to mammalian cells, which further complicates mRNA sequencing from non-cultivable and non-model bacterial species. To overcome these limitations, we report EMBR-seq (Enrichment of mRNA by Blocked rRNA), a method that efficiently depletes 5S, 16S and 23S rRNA using blocking primers to prevent their amplification.
Results EMBR-seq results in 90% of the sequenced RNA molecules from an
E. coli culture deriving from mRNA. We demonstrate that this increased efficiency provides a deeper view of the transcriptome without introducing technical amplification-induced biases. Moreover, compared to recent methods that employ a large array of oligonucleotides to deplete rRNA, EMBR-seq uses a single or a few oligonucleotides per rRNA, thereby making this new technology significantly more cost-effective, especially when applied to varied bacterial species. Finally, compared to existing commercial kits for bacterialmore »Conclusions EMBR-seq provides an efficient and cost-effective approach to quantify global gene expression profiles from low input bacterial samples.
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Multi-modal bio-sensing has recently been used as effective research tools in affective computing, autism, clinical disorders, and virtual reality among other areas. However, none of the existing bio-sensing systems support multi-modality in a wearable manner outside well-controlled laboratory environments with research-grade measurements. This work attempts to bridge this gap by developing a wearable multi-modal biosensing system capable of collecting, synchronizing, recording and transmitting data from multiple bio-sensors: PPG, EEG, eye-gaze headset, body motion capture, GSR, etc. while also providing task modulation features including visual-stimulus tagging. This study describes the development and integration of the various components of our system. We evaluate the developed sensors by comparing their measurements to those obtained by a standard research-grade bio-sensors. We first evaluate different sensor modalities of our headset, namely earlobe-based PPG module with motion-noise canceling for ECG during heart-beat calculation. We also compare the steady-state visually evoked potentials (SSVEP) measured by our shielded dry EEG sensors with the potentials obtained by commercially available dry EEG sensors. We also investigate the effect of head movements on the accuracy and precision of our wearable eyegaze system. Furthermore, we carry out two practical tasks to demonstrate the applications of using multiple sensor modalities for exploring previouslymore »
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Today's CMOS technologies allow larger circuit designs to fit on a single chip. However, this advantage comes at a high price of increased process-voltage-temperature (PVT) variations. FPGAs and their designs are no exceptions to such variations. In fact, the same bit file loaded into two different FPGAs of the same model can produce a significant difference in power and thermal characteristics due to variations that exist within the chip. Since it is increasingly difficult to control physical variations through manufacturing tasks, there is a need for practical ways to sense chip variations to provide a way for circuit designers to compensate or avoid its negative effects. One of the most critical aspects of such variation is power. Therefore, we developed and demonstrated a high accuracy on-chip on-line Energy-per-Component (EPC) measurement technology on Xilinx FPGAs since 2011. However, we found that the hardware overhead associated with such method limited the use of the technology. Therefore, our follow-up work in Energy-per-Operation (EPO) on Spartan FPGA with OpenRISC SoC produced an equally accurate power monitoring technology with drastically lower hardware overhead. While this method made our technology more practical for SoC designs on FPGAs, it did not produce component level power dissipation datamore »
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