A subdermally implantable flexible photovoltatic (IPV) device is proposed for supplying sustainable electric power to in vivo medical implants. Electric properties of the implanted IPV device are characterized in live animal models. Feasibility of this strategy is demonstrated by operating a flexible pacemaker with the subdermal IPV device which generates DC electric power of ≈647 μW under the skin.

Cytochrome P450 1B1 (CYP1B1) is a major E2 hydroxylase involved in the metabolism of potential carcinogens. CYP1B1 expression has been reported to be higher in tumors compared to normal tissues, especially in hormone-related cancers including breast, ovary, and prostate tumors. To explore the role of CYP1B1 in cancer progression, we investigated the action of CYP1B1 in cells with increased CYP1B1 via the inducer 7,12-dimethylbenz[α]anthracene (DMBA) or an overexpression vector, in addition to decreased CYP1B1 via the inhibitor tetramethoxystilbene (TMS) or siRNA knockdown. We observed that CYP1B1 promoted cell proliferation, migration, and invasion in MCF-7 and MCF-10A cells. To understand its molecular mechanism, we measured key oncogenic proteins including β-catenin, c-Myc, ZEB2, and matrix metalloproteinases following CYP1B1 modulation. CYP1B1 induced epithelial-mesenchymal transition (EMT) and activated Wnt/β-catenin signaling via upregulation of CTNNB1, ZEB2, SNAI1, and TWIST1. Sp1, a transcription factor involved in cell growth and metastasis, was positively regulated by CYP1B1, and suppression of Sp1 expression by siRNA or DNA binding activity using mithramycin A blocked oncogenic transformation by CYP1B1. Therefore, we suggest that Sp1 acts as a key mediator for CYP1B1 action. Treatment with 4-hydroxyestradiol (4-OHE2), a major metabolite generated by CYP1B1, showed similar effects as CYP1B1 overexpression, indicating that CYP1B1 activity mediated various oncogenic events in cells. In conclusion, our data suggests that CYP1B1 promotes cell proliferation and metastasis by inducing EMT and Wnt/β-catenin signaling via Sp1 induction.

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Recurrent neural networks have shown excellent performance in many applications; however they require increased complexity in hardware or software based implementations. The hardware complexity can be much lowered by minimizing the word-length of weights and signals. This work analyzes the fixed-point performance of recurrent neural networks using a retrain based quantization method. The quantization sensitivity of each layer in RNNs is studied, and the overall fixedpoint optimization results minimizing the capacity of weights while not sacrificing the performance are presented. A language model and a phoneme recognition examples are used.Index Terms-recurrent neural network, quantization, word length optimization, fixed-point optimization, deep neural network

Gesture recognition is a very essential technology for many wearable devices. While previous algorithms are mostly based on statistical methods including the hidden Markov model, we develop two dynamic hand gesture recognition techniques using low complexity recurrent neural network (RNN) algorithms. One is based on video signal and employs a combined structure of a convolutional neural network (CNN) and an RNN. The other uses accelerometer data and only requires an RNN. Fixed-point optimization that quantizes most of the weights into two bits is conducted to optimize the amount of memory size for weight storage and reduce the power consumption in hardware and software based implementations.

In this paper, a neural network based real-time speech recognition (SR) system is developed using an FPGA for very low-power operation. The implemented system employs two recurrent neural networks (RNNs); one is a speech-tocharacter RNN for acoustic modeling (AM) and the other is for character-level language modeling (LM). The system also employs a statistical word-level LM to improve the recognition accuracy. The results of the AM, the character-level LM, and the word-level LM are combined using a fairly simple N -best search algorithm instead of the hidden Markov model (HMM) based network. The RNNs are implemented using massively parallel processing elements (PEs) for low latency and high throughput. The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA. The proposed algorithm is implemented on a Xilinx XC7Z045, and the system can operate much faster than real-time.

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