Mammalian Ste20-Like Kinase and SAV1 Promote 3T3-L1 Adipocyte Differentiation by Activation of PPARγ

The mammalian ste20 kinase (MST) signaling pathway plays an important role in the regulation of apoptosis and cell cycle control. We sought to understand the role of MST2 kinase and Salvador homolog 1 (SAV1), a scaffolding protein that functions in the MST pathway, in adipocyte differentiation. MST2 and MST1 stimulated the binding of SAV1 to peroxisome proliferator-activated receptor γ (PPARγ), a transcription factor that plays a key role in adipogenesis. The interaction of endogenous SAV1 and PPARγ was detected in differentiating 3T3-L1 adipocytes. This binding required the kinase activity of MST2 and was mediated by the WW domains of SAV1 and the PPYY motif of PPARγ. Overexpression of MST2 and SAV1 increased PPARγ levels by stabilizing the protein, and the knockdown of SAV1 resulted in a decrease of endogenous PPARγ protein in 3T3-L1 adipocytes. During the differentiation of 3T3-L1 cells into adipocytes, MST2 and SAV1 expression began to increase at 2 days when PPARγ expression also begins to increase. MST2 and SAV1 significantly increased PPARγ transactivation, and SAV1 was shown to be required for the activation of PPARγ by rosiglitazone. Finally, differentiation of 3T3-L1 cells was augmented by MST2 and SAV1 expression and inhibited by knockdown of MST1/2 or SAV1. These results suggest that PPARγ activation by the MST signaling pathway may be a novel regulatory mechanism of adipogenesis

A pathogen-derived metabolite induces microglial activation via odorant receptors

Microglia (MG), the principal neuroimmune sentinels in the brain, continuously sense changes in their environment and respond to invading pathogens, toxins, and cellular debris, thereby affecting neuroinflammation. Microbial pathogens produce small metabolites that influence neuroinflammation, but the molecular mechanisms that determine whether pathogen-derived small metabolites affect microglial activation of neuroinflammation remain to be elucidated. We hypothesized that odorant receptors (ORs), the largest subfamily of G protein-coupled receptors, are involved in microglial activation by pathogen-derived small metabolites. We found that MG express high levels of two mouse ORs, Olfr110 and Olfr111, which recognize a pathogenic metabolite, 2-pentylfuran, secreted by Streptococcus pneumoniae. These interactions activate MG to engage in chemotaxis, cytokine production, phagocytosis, and reactive oxygen species generation. These effects were mediated through the G(alpha s)-cyclic adenosine monophosphate-protein kinase A-extracellular signal-regulated kinase and G(beta gamma)-phospholipase C-Ca2+ pathways. Taken together, our results reveal a novel interplay between the pathogen-derived metabolite and ORs, which has major implications for our understanding of microglial activation by pathogen recognition. Database Model data are available in the PMDB database under the accession number PM0082389.N

압축 센싱 복원을 위한 최소 제곱 솔버 구조

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Low-Complexity On-Device ECG Classifier using Binarized Neural Network

Targeting the real-Time arrhythmia diagnosis on resource-limited devices, in this paper, we present a cost-effective heartbeat classifier that uses a binarized neural network (BNN). Based on the previous CNN-based approaches, several optimization schemes are applied for error-resilient binarization. With the full-precision gradient descent, the proposed model reduces the variance during the training. The learnable activation function additionally compensates binarization errors by adjusting information shifts. Targeting the MIT-BIH arrhythmia database, allowing less than 1% accuracy drop, the proposed BNN model reduces the energy consumption and the memory usage by 94.63% and 84.62%, respectively, compared to the full-precision CNN-based model.2

Lightweight End-to-End Stress Recognition using Binarized CNN-LSTM Models

In this paper, we propose a novel end-to-end stress recognition model by combining binarized convolutional neural network (CNN) and long short-term memory (LSTM) models. Based on the previous CNN-LSTM model using electrocardiogram (ECG) and respiration (RESP) signals, we newly apply the bandit-based hyperparameter optimization to find more accurate solutions. Analyzing the computational costs of the accuracy-aware model, we also introduce advanced memory-reduction techniques with downscaling and binarization for realizing the cost-efficient stress recognition solution. As a result, compared to the state-of-the-art methods, the proposed model reduces the memory size, the inference latency, and the energy consumption by 93 %, 39 %, and 42 %, respectively, while even increasing the recognition accuracy up to 87%.2

Low-complexity and low-latency SVC decoding architecture using modified MAP-SP algorithm

The compressive sensing (CS) based sparse vector coding (SVC) method is one of the promising ways for the next-generation ultra-reliable and low-latency communications. In this paper, we present advanced algorithm-hardware co-optimization schemes for realizing a cost-effective SVC decoding architecture. The previous maximum a posteriori subspace pursuit (MAP-SP) algorithm is newly modified to relax the computational overheads by applying novel residual forwarding and LLR approximation schemes. A fully-pipelined parallel hardware is also developed to support the modified decoding algorithm, reducing the overall processing latency, especially at the support identification step. In addition, an advanced least-square-problem solver is presented by utilizing the parallel Cholesky decomposer design, further reducing the decoding latency with parallel updates of support values. The implementation results from a 22nm FinFET technology showed that the fully-optimized design is 9.6 times faster while improving the area efficiency by 12 times compared to the baseline realization.11Nsciescopu

Analysis of visual sensibility evaluation of naturally colored organic cotton: Identification of reliability and proper scouring method

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