1,721,150 research outputs found
A 250MHz-2GHz Wide Range Delay-Locked Loop
No full textCICC 2004This work was supported by KOSEF through the MICROS at KAIST and IT-SOC Promotion Group through Ministry of Information and Communication, Kore
An area-efficient and fully synthesizable Bluetooth baseband module for wireless communication
No full textIn this paper, we describe the implementation and the test results of a Bluetooth baseband module we have developed. For small chip size, we eliminate FIFOs for data buffering between hardware functional units and data buffers for bit streaming among channel coding blocks. Furthermore, we carefully consider hardware and software partitioning. We implement complex control tasks of the Bluetooth baseband layer protocols in software running on an embedded microcontroller. Hardware-efficient functions, such as low-level bitstream link control; host controller interfaces (HCIs), such as universal asynchronous receiver transmitter (UART) and universal serial bus (USB) interfaces; and audio CODEC are performed by dedicated hardware blocks. In addition, the bitstream data path block of the link controller constructing the baseband module has been designed by considering low power. The design of the baseband module is done using fully synthesizable Verilog HDL to enhance the portability between process technologies. A field programmable gate array (FPGA) implementation of the module was tested for functional verification and real time operation of file and bitstream transfer between PCs. The module was also fabricated in a 0.25 mum CMOS technology, the core size of which is only 2.79 x 2.80 mm(2)
Fabrication of nanometer-scale surface modified glass fiber/epoxy composite for bonding experiment
No full textModeling subcortical ischemic white matter injury in rodents: Unmet need for a breakthrough in translational research
Full text linkSubcortical ischemic white matter injury (SIWMI), pathological correlate of white matter hyperintensities or leukoaraiosis on magnetic resonance imaging, is a common cause of cognitive decline in elderly. Despite its high prevalence, it remains unknown how various components of the white matter degenerate in response to chronic ischemia.This incomplete knowledge is in part due to a lack of adequate animal model. The current review introduces various SIWMI animal models and aims to scrutinize their advantages and disadvantages primarily in regard to the pathological manifestations of white matter components. The SIWMI animal models are categorized into 1) chemically induced SIWMI models, 2) vascular occlusive SIWMI models, and 3) SIWMI models with comorbid vascular risk factors. Chemically induced models display consistent lesions in predetermined areas of the white matter, but the abrupt evolution of lesions does not appropriately reflect the progressive pathological processes in human white matter hyperintensities. Vascular occlusive SIWMI models often do not exhibit white matter lesions that are sufficiently unequivocal to be quantified. When combined with comorbid vascular risk factors (specifically hypertension), however, they can produce progressive and definitive white matter lesions including diffuse rarefaction, demyelination, loss of oligodendrocytes, and glial activation, which are by far the closest to those found in human white matter hyperintensities lesions. However, considerable surgical mortality and unpredictable natural deaths during a follow-up period would necessitate further refinements in these models. In the meantime, in vitro SIWMI models that recapitulate myelinated white matter track may be utilized to study molecular mechanisms of the ischemic white matter injury. Appropriate in vivo and in vitro SIWMI models will contribute in a complementary manner to making a breakthrough in developing effective treatment to prevent progression of white matter hyperintensities
Transformer-Based Gene Scoring Model for Extracting Representative Characteristic of Central Dogma Process to Prioritize Pathogenic Genes Applying Breast Cancer Multi-omics Data
No full textVarious deep learning approaches using big multiomics data of cancer patients are being applied to identify biomarkers of diverse cancer types these days. Because multiomics data generally have a character with high dimensions compared with relatively few patient samples, this imbalance is a recognized bottleneck to apply integrated characteristics of multiomics in cancer research. Among the dimensionality reduction techniques, deep learning-based approaches, such as autoencoder, are known to have strength in handling high dimensional data with few samples. However, the black box model makes it difficult to explain which genes are essential. In this study, we develop a transformer-based representative Central tendency Gene score considering Central Dogma process information (CGCD) model to predict optimized potential anti-breast cancer therapeutic target genes. It is based on a unified representation applying the compressed features learned through Transformer using multiomics data of 105 breast cancer patients from The Cancer Genome Atlas (TCGA). Unlike other autoencoder-based models, CGCD can derive gene scores from the self-attention mechanism in the transformer model. The significant encoding genes were selected by computing the p-value per each gene based on the scores for all the patients. To verify CGCD score ability for predicting target genes, we estimated hazard ratio and p-value per gene by conducting survival analysis using Cox proportional hazard model and calculated area under the curve (AUC) with CGCD score and the p-value per patient, and performed biological functional analysis including Gene Set Enrichment Analysis (GSEA). As the CGCD score became higher, the results showed a pronounced increasing trend in the retention rate of breast cancer marker genes and pathways. From this point of view, the CGCD score that reflects harmony of multi-omics data in a gene is considered suitable as a criterion for predicting cancer diagnostic markers
BTOB-T: Bilateral orchesTrated deep learning framework based on proteogenOmics for drug-repositioning of Breast cancer Treatment
No full textComputational predictive models based on a deep learning framework, which have recently been developed, substantially reduced the average time and cost of drug development. However, the molecular heterogeneity of breast cancer presents challenges in extracting representative gene-specific signatures from multi-omics data. In this study, we introduce a novel framework, the Bilateral orchesTrated deep learning framework based on proteogenOmics for drug-repositioning of Breast cancer Treatment (BTOB-T). First, integrated gene representations are extracted using a transformer-based model. After generating a breast cancer network which composed of gene representations and their relations, graph kernel methods are applied to measure dissimilarity scores ("ensemble drug scores") between the breast cancer network and the perturbed-by-compounds networks. The BTOB-T predictions are verified using clinical trial data, prescribed drug lists, breast cancer phenomics data, and cell viability assays. BTOB-T integrates multi-omics biological dynamics data and the relations between genes, facilitating more accurate prediction of the efficacy of new drugs
A single type composite hybrid bipolar plate with Gas Diffusion layer for High efficiency PEMFC
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