Multiple bombesin-like peptides with opposite functions from skin of Odorrana grahami

AbstractBombesin-like peptides (BLPs) are a family of neuroendocrinic peptides that mediate a variety of biological activities. Three mature BLPs from the skin secretions of the frog Odorrana grahami were purified. Several bombesin-like peptide cDNA sequences encoding precursors of BLPs were identified from the skin cDNA library of O. grahami. This is the maximal diversity of BLPs ever found in animals. Five mature BLPs (B1–B5) based on the amino acid sequences derived from the cDNA cloning were synthesized. In the in vitro myotropic contraction experiment, all synthesized BLPs displayed a stimulating effect toward rat stomach strips, except B4 and B5 which showed the opposite effect, suggesting that certain BLPs may act as antagonists of bombesin receptors while most other BLPs act as agonists. This finding will facilitate the finding of novel bombesin receptors and novel ligands of bombesin receptors. The diversity of amphibian BLPs and their precursors were also analyzed and results suggest that amphibian BLPs and corresponding precursors of various sizes and processing patterns can be used as markers of taxonomic and molecular phylogenetics. The remarkable similarity of preproregions gives rise to very different BLPs and 3′-terminal regions in distantly related frog species, suggesting that the corresponding genes form a multigene family originating from a common ancestor. The diversification of BLP loci could thus be part of an evolutionary strategy developed by amphibian species as a result of shifts to novel ecological niches when environmental factors change rapidly

A deep learning framework based on Koopman operator for data-driven modeling of vehicle dynamics

Autonomous vehicles and driving technologies have received notable attention in the past decades. In autonomous driving systems, \textcolor{black}{the} information of vehicle dynamics is required in most cases for designing of motion planning and control algorithms. However, it is nontrivial for identifying a global model of vehicle dynamics due to the existence of strong non-linearity and uncertainty. Many efforts have resorted to machine learning techniques for building data-driven models, but it may suffer from interpretability and result in a complex nonlinear representation. In this paper, we propose a deep learning framework relying on an interpretable Koopman operator to build a data-driven predictor of the vehicle dynamics. The main idea is to use the Koopman operator for representing the nonlinear dynamics in a linear lifted feature space. The approach results in a global model that integrates the dynamics in both longitudinal and lateral directions. As the core contribution, we propose a deep learning-based extended dynamic mode decomposition (Deep EDMD) algorithm to learn a finite approximation of the Koopman operator. Different from other machine learning-based approaches, deep neural networks play the role of learning feature representations for EDMD in the framework of the Koopman operator. Simulation results in a high-fidelity CarSim environment are reported, which show the capability of the Deep EDMD approach in multi-step prediction of vehicle dynamics at a wide operating range. Also, the proposed approach outperforms the EDMD method, the multi-layer perception (MLP) method, and the Extreme Learning Machines-based EDMD (ELM-EDMD) method in terms of modeling performance. Finally, we design a linear MPC with Deep EDMD (DE-MPC) for realizing reference tracking and test the controller in the CarSim environment.Comment: 12 pages, 10 figures, 1 table, and 2 algorithm

High-performance large-area blade-coated perovskite solar cells with low ohmic loss for low lighting indoor applications

Emerging hybrid organic–inorganic perovskites with superior optoelectronic property demonstrate promising prospect for photovoltaic (PV) applications, in particular for low-lighting indoor applications e.g. within internet of things (IoT) networks or low-energy wireless communication devices. In order to prepare devices with high power output under low-illumination conditions, scalable fabrication techniques are preferred for large-area perovskite solar cells. In additions, one of the key parameters to achieve high-efficiency large-area perovskite solar cells is to minimize the ohmic loss to further boost the solar cell efficiency. Herein, a one-step blade-coating method assisted by hexafluorobenzene (HFB) was developed to deposit dense, large-area smooth and high- quality perovskite films with low ohmic loss. The as-fabricated devices demonstrated power conversion effi- ciency (PCE) of 20.7% (area of 0.2 cm2) and 16.5% (1 cm2), respectively, under standard (AM 1.5G) illumination conditions. Besides, the large-area (1 cm2) devices demonstrated a remarkable PCE of ~ 33.8% and ~ 30.0% under 1000 lx and 100 lx illumination provided by white light-emitting diode (LED) lamp, respectively. We exhibited a series-connected stack of large-area (totally active area ~ 4 cm2) perovskite photovoltaic device powering up a LED under common indoor environment as an indoor self-power indicator lamp. The analysis using a single diode model suggests that the high performance of the large-area devices under low-lighting in- door conditions is highly associated with the largely reduced ohmic losses, which particularly indicate that the perovskite films by a facile and scalable blade-coating method. The presented scalable approach paves the way to designing high-performance perovskite solar cells for a variety of emerging indoor PV application

Zero-shot Clinical Entity Recognition using ChatGPT

In this study, we investigated the potential of ChatGPT, a large language model developed by OpenAI, for the clinical named entity recognition task defined in the 2010 i2b2 challenge, in a zero-shot setting with two different prompt strategies. We compared its performance with GPT-3 in a similar zero-shot setting, as well as a fine-tuned BioClinicalBERT model using a set of synthetic clinical notes from MTSamples. Our findings revealed that ChatGPT outperformed GPT-3 in the zero-shot setting, with F1 scores of 0.418 (vs.0.250) and 0.620 (vs. 0.480) for exact- and relaxed-matching, respectively. Moreover, prompts affected ChatGPT's performance greatly, with relaxed-matching F1 scores of 0.628 vs.0.541 for two different prompt strategies. Although ChatGPT's performance was still lower than that of the supervised BioClinicalBERT model (i.e., relaxed-matching F1 scores of 0.628 vs. 0.870), our study demonstrates the great potential of ChatGPT for clinical NER tasks in a zero-shot setting, which is much more appealing as it does not require any annotation.Comment: 7 pages, 5 tables, 1 figur

TL-nvSRAM-CIM: Ultra-High-Density Three-Level ReRAM-Assisted Computing-in-nvSRAM with DC-Power Free Restore and Ternary MAC Operations

Accommodating all the weights on-chip for large-scale NNs remains a great challenge for SRAM based computing-in-memory (SRAM-CIM) with limited on-chip capacity. Previous non-volatile SRAM-CIM (nvSRAM-CIM) addresses this issue by integrating high-density single-level ReRAMs on the top of high-efficiency SRAM-CIM for weight storage to eliminate the off-chip memory access. However, previous SL-nvSRAM-CIM suffers from poor scalability for an increased number of SL-ReRAMs and limited computing efficiency. To overcome these challenges, this work proposes an ultra-high-density three-level ReRAMs-assisted computing-in-nonvolatile-SRAM (TL-nvSRAM-CIM) scheme for large NN models. The clustered n-selector-n-ReRAM (cluster-nSnRs) is employed for reliable weight-restore with eliminated DC power. Furthermore, a ternary SRAM-CIM mechanism with differential computing scheme is proposed for energy-efficient ternary MAC operations while preserving high NN accuracy. The proposed TL-nvSRAM-CIM achieves 7.8x higher storage density, compared with the state-of-art works. Moreover, TL-nvSRAM-CIM shows up to 2.9x and 1.9x enhanced energy-efficiency, respectively, compared to the baseline designs of SRAM-CIM and ReRAM-CIM, respectively